Search

Hardness is one of the important attributes in determining the quality of dried fruits. Hardness assessment is normally carried out by manual inspection. This method is time consuming, laborious, expensive and subjective. The objective of this study was to develop a computer vision system with a monochrome camera to classify dates based on hardness. Date samples were obtained from three different growing regions in Oman and graded into soft, semi-hard, and hard classes based on hardness. A total of 1800 date samples were imaged individually using a monochrome camera (600 dates / class). Histogram and texture features were extracted from the acquired monochrome images and used in the classification models. The overall classification accuracies in three class model (soft, semi-hard, and hard) were 66% and 71% for linear discriminant analysis (LDA) and artificial neural network (ANN), respectively. It was improved to 84% and 77% in LDA and ANN, respectively while using two class model (soft and hard (semi-hard and hard together)). The histogram features were more contributing in the date classification based on hardness than image texture features. Computer vision technique has great potential to develop online quality monitoring systems for dates and other dried fruits.

Heritage sites are vulnerable to damage due to social, anthropological and environmental factors. Major Earthquakes are followed by damage to cultural heritage buildings. The assessment of such building damage is a critical problem. Earth Observation data, owing to its property of being non-contact, cost effective, synoptic view and high repeatability, has a significant role to play in estimation of damage in the earthquake affected areas. Currently, several and varied types of remote sensing data have become available, and therefore, appropriate methods for rapid assessment and analysis of the data need to be developed. Rapid damage assessment is critical to minimize loss in terms of life and property. In case of cultural monuments, rapid assessment can minimize damage and help in the conservation of monument. This research focuses on evolving a robust method for rapid identification and extraction of damaged heritage building structures, especially those affected by disasters such as earthquakes. In this study, we propose to examine the utility of advance texture algorithms such as Gabor, fractal and semi-variogram for rapid damage detection in heritage building structures. The methodology attempts to automatically highlight damaged portions of the structure through a knowledge driven rule set. The technique was able to extract the damaged area from the heritage building structure with the use of high-resolution space borne data. It is observed that feature extraction algorithms based on fractal and variogram provide better results than the Gabor based textures and are very useful in the case of high-resolution satellite imagery. Both the methods are able to extract damaged features in both shadowed and non-shadowed regions of the image. Hence the problems posed by shadowed dead grounds on EO data can be effectively resolved. However, it is also observed that the advance texture feature extraction algorithms are useful only in case of high-spatial resolution dataset and has limited use for rapid damage assessment from medium and low resolution datasets. [ABSTRACT FROM AUTHOR]

Crowd density estimation is a challenging research problem in computer vision and has many applications in commercial and defense sectors. Various crowd density estimation methods have been proposed by researchers in the past, but there is an utmost need for accurate, robust and efficient crowd density estimation techniques for its practical implementation. In this paper, we propose a fine-tuned and computationally economical, ensemble regression-based machine learning model for crowd density estimation. The WorldExpo'10 dataset has been used for experimental analysis and model performance evaluation. We extract variety of features in texture-based features such as gray-level co-occurrence matrix, local binary pattern and histogram of oriented gradients, structure-based features such as perimeter pixel and the orientation of pixels, and segment-based handcrafted features from each patch of the image and use an optimum combination of these features as input to the regression model. To achieve optimized memory utilization and faster speed, principal component analysis is employed to reduce the dimensions of the lengthy feature vector. Extensive experiments on different fronts ranging from the model hyperparameter optimization, features optimization and features selection were conducted, and at each step, we selected the most favorable results as input to the optimized model. The performance of the model is evaluated based on two popular metrics, i.e., mean absolute error and mean squared error. The comparative analysis shows that the proposed system outperforms the former methods tested on the WorldExpo'10 dataset. [ABSTRACT FROM AUTHOR]

Mammography is a very useful tool to diagnose breast cancer in early stages when it is easier to treat. There are two types of evidence that radiologists look for in a mammogram, calcifications and the existence of masses. In this study, an intelligent computer‐aided diagnosis system is proposed for the detection of masses in mammographic images regardless of their nature. The proposed method uses a combination of extended maxima transformations, having different threshold values, in order to find suitable internal and external markers for a marker‐based watershed segmentation. After segmentation, a two‐stage classifier is used to distinguish the masses better from the healthy breast tissue. A feature vector based mainly on contrast and texture features is calculated and two alternative approaches, a Bayesian classifier and a support vector machine (SVM) with Gaussian kernel function, are implemented for further reduction of the false positive areas. The system was evaluated using the data from two online databases. Specifically, 73 mammographic images from the new curated breast imaging subset of digital database for screening mammography (CBIS‐DDSM) database and all the mammographic images that contain masses from the mini‐mammographic image analysis society (MIAS) database were used. The overall sensitivity, in both datasets, was near 80% when the Bayesian classifier was used and above 85% when the SVM was applied. [ABSTRACT FROM AUTHOR]

Magnetic Resonance Imaging (MRI) was introduced as an alternative to destructive methods for determining the quality parameters of meat products, since MRI is a non-destructive, non-ionizing, and innocuous technique. The use of MRI is linked to the use of computer vision algorithms and data mining techniques. Thus, the success of MRI could not be understood without referring to the implementation of powerful algorithms to handle all data generated. In this study, a new texture algorithm was developed in order to obtain new features based on the radial distribution of the textures from MRI. This new algorithm is called Radial Texture Algorithm (RTA) and it was compared with four classical texture features approaches. The results obtained allow describing the semantic context of the texture features for this new algorithm and the relationship among the features from this algorithm and the classical approaches. Besides, the results obtained by means of these computer vision algorithms were correlated with the results obtained by means of physico-chemical and sensory parameters of the Iberian loins. All of computer vision algorithms achieved correlation coefficients higher than 0.500, noting that RTA reached the highest correlation coefficients in almost all cases. These high correlation coefficients confirm the proposed algorithm as an alternative to the other computational texture features approaches in order to compute the physico-chemical and sensory parameters of meat products in an accurate, fast, non-destructive and efficient way. [ABSTRACT FROM AUTHOR]

Automatic segmentation of land use and land cover from high resolution remote sensing imagery has been an essential research area in image processing for the past two decades. Timely and reliable information of land use and land cover is very much essential in administration for proper planning and decision making in various areas like agriculture, urban development, environment protection, etc. In this paper, a new algorithm ULCDSA (Unsupervised Linear Contact Distributions Segmentation Algorithm) is proposed for unsupervised segmentation of high resolution panchromatic data. Texture features extracted with the help of linear contact distributions and mathematical morphology are used in this paper. The proposed method has been implemented and tested on various panchromatic images of N.R.S.C's Cartosat-II data sets, Google earth images and on other aerial images. The results have then been compared with gray scale co-occurrence matrix algorithms and promising results have been obtained. [ABSTRACT FROM AUTHOR]

In the present work, a machine vision system is introduced, which captures images and extracts surface texture features of machined surfaces. The texture feature parameters are extracted using the gray‐level co‐occurrence matrix and correlated with different surface roughness parameters recorded by a contact‐type surface profilometer. The image acquisition carried out at different roughness levels in order to extract texture features. The variation between each texture features and surface roughness parameter is investigated. Multiple regression models are developed to predict the subjective estimation of surface roughness parameter (Ra) and qualitative detection of the degree of surface roughness. It is observed that the linear detection model shows better performance characteristics compared with a nonlinear detection model. The comparison between measured and predicted results shows that the linear detection model had a maximum relative error of 2.01%, drastically better than nonlinear detection model of −9.60% error parts, hence indicating better surface detection capability over the nonlinear detection model. The results demonstrate that the prediction of surface roughness using linear regression model is a reliable approach of noncontact measurement. [ABSTRACT FROM AUTHOR]

Shape and texture features provide a novel framework for expression recognition by using salient landmarks based triangle initialisation and identification of texture regions. Detection of effective landmarks is achieved by well‐known appearance‐based model to form a grid. Accordingly, several triangles are identified within the grid with respect to the nose landmark reference point. These salient landmarks are also used to find the texture regions. Normalised shape and texture signatures are derived from triangles and texture regions. Stability indices are determined from shape and texture signatures which are also exploited as significant features for recognition of facial expressions. Statistical measures such as range, moment, skewness, kurtosis, and entropy are used to supplement the feature set. Individual and combined features are fed into multilayer perceptron and deep belief network (DBN) network to classify different expression categories encompassing anger, sadness, fear, disgust, surprise, and happiness. The authors investigated the performance of their proposed system on Cohn–Kanade, JAFFE, MMI, MUG, and Wild face benchmark databases. Thorough experimentation establishes the performance superiority of the proposed methodology over other existing competitors. Combining features is also validated through extensive results and analyses. [ABSTRACT FROM AUTHOR]

Rationale and objectives: The ultrasound B-mode-based morphological and texture analysis and Nakagami parametric imaging have been proposed to characterize breast tumors. Since these three feature categories of ultrasonic tissue characterization supply information on different physical characteristics of breast tumors, by combining the above methods is expected to provide more clues for classifying breast tumors.Materials and methods: To verify the validity of the concept, raw data were obtained from 160 clinical cases. Six different types of morphological-feature parameters, four texture features, and the Nakagami parameter of benignancy and malignancy were extracted for evaluation. The Pearson's correlation matrix was used to calculate the correlation between different feature parameters. The fuzzy c-means clustering and stepwise regression techniques were utilized to determine the optimal feature set, respectively. The logistic regression, receiver operating characteristic curve, and support vector machine were used to estimate the diagnostic ability.Results: The best performance was obtained by combining morphological-feature parameter (e.g., standard deviation of the shortest distance), texture feature (e.g., variance), and the Nakagami parameter, with an accuracy of 89.4%, a specificity of 86.3%, a sensitivity of 92.5%, and an area under receiver operating characteristic curve of 0.96. There was no significant difference between using fuzzy c-means clustering, logistic regression, and support vector machine based on the optimal feature set for breast tumors classification.Conclusion: Therefore, we verified that different physical ultrasonic features are functionally complementary and thus improve the performance in diagnosing breast tumors. Moreover, the optimal feature set had the maximum discriminating performance should be irrelative to the power of classifiers. [ABSTRACT FROM AUTHOR]

Purpose: Patient‐specific quality assurance (QA) for intensity‐modulated radiation therapy (IMRT) is a ubiquitous clinical procedure, but conventional methods have often been criticized as being insensitive to errors or less effective than other common physics checks. Recently, there has been interest in the application of radiomics, quantitative extraction of image features, to radiotherapy QA. In this work, we investigate a deep learning approach to classify the presence or absence of introduced radiotherapy treatment delivery errors from patient‐specific QA. Methods: Planar dose maps from 186 IMRT beams from 23 IMRT plans were evaluated. Each plan was transferred to a cylindrical phantom CT geometry. Three sets of planar doses were exported from each plan corresponding to (a) the error‐free case, (b) a random multileaf collimator (MLC) error case, and (c) a systematic MLC error case. Each plan was delivered to the electronic portal imaging device (EPID), and planned and measured doses were used to calculate gamma images in an EPID dosimetry software package (for a total of 558 gamma images). Two radiomic approaches were used. In the first, a convolutional neural network with triplet learning was used to extract image features from the gamma images. In the second, a handcrafted approach using texture features was used. The resulting metrics from both approaches were input into four machine learning classifiers (support vector machines, multilayer perceptrons, decision trees, and k‐nearest‐neighbors) in order to determine whether images contained the introduced errors. Two experiments were considered: the two‐class experiment classified images as error‐free or containing any MLC error, and the three‐class experiment classified images as error‐free, containing a random MLC error, or containing a systematic MLC error. Additionally, threshold‐based passing criteria were calculated for comparison. Results: In total, 303 gamma images were used for model training and 255 images were used for model testing. The highest classification accuracy was achieved with the deep learning approach, with a maximum accuracy of 77.3% in the two‐class experiment and 64.3% in the three‐class experiment. The performance of the handcrafted approach with texture features was lower, with a maximum accuracy of 66.3% in the two‐class experiment and 53.7% in the three‐class experiment. Variability between the results of the four machine learning classifiers was lower for the deep learning approach vs the texture feature approach. Both radiomic approaches were superior to threshold‐based passing criteria. Conclusions: Deep learning with convolutional neural networks can be used to classify the presence or absence of introduced radiotherapy treatment delivery errors from patient‐specific gamma images. The performance of the deep learning network was superior to a handcrafted approach with texture features, and both radiomic approaches were better than threshold‐based passing criteria. The results suggest that radiomic QA is a promising direction for clinical radiotherapy. [ABSTRACT FROM AUTHOR]

Accurate categorization of cirrhosis liver image in ultrasound modality is of great importance in medical diagnosis and treatment. Health informatics is important as it provides quick predictions on diseases based on nearness of symptoms. Modelling solutions for the same on cloud using deep learning is the motivation of this paper. Here, we propose a deep learning model associated with correlation based feature selection method for cirrhosis image classification. We compare the results with three other conventional classifiers algorithms to improve the better classification accuracy. First in pre-processing stage, noises are eliminated from pathological scan images by using modified laplacian pyramid non-linear diffusion filter. From the pre-processed scan images, each cirrhosis region is obtained under the guidance of radiology or physicians. Then, after extracting the complete features of each patch by gray level, local binary pattern and scale invariant feature, a feature selection technique is applied to choice the predominant texture features for each classifier. Finally a convolution neural network is implemented to improve the performance of classifiers in terms of sensitivity, specificity and accuracy. Convolution neural network algorithm with two hidden layers gives more accuracy in classifying cirrhosis image with 98% sensitivity. Experiments are carried out with 990 cirrhosis image patches which demonstrates that our proposed deep learning classifier perform 100% well than original classifiers in terms of accuracy. [ABSTRACT FROM AUTHOR]

We propose a multiscale texture features based on Laplacian-of Gaussian (LoG) filter to predict progression free (PFS) and overall survival (OS) in patients newly diagnosed with glioblastoma (GBM). Experiments use the extracted features derived from 40 patients of GBM with T1-weighted imaging (T1-WI) and Fluid-attenuated inversion recovery (FLAIR) images that were segmented manually into areas of active tumor, necrosis, and edema. Multiscale texture features were extracted locally from each of these areas of interest using a LoG filter and the relation between features to OS and PFS was investigated using univariate (i.e., Spearman's rank correlation coefficient, log-rank test and Kaplan-Meier estimator) and multivariate analyses (i.e., Random Forest classifier). Three and seven features were statistically correlated with PFS and OS, respectively, with absolute correlation values between 0.32 and 0.36 and p < 0.05. Three features derived from active tumor regions only were associated with OS (p < 0.05) with hazard ratios (HR) of 2.9, 3, and 3.24, respectively. Combined features showed an AUC value of 85.37 and 85.54% for predicting the PFS and OS of GBM patients, respectively, using the random forest (RF) classifier. We presented a multiscale texture features to characterize the GBM regions and predict he PFS and OS. The efficiency achievable suggests that this technique can be developed into a GBM MR analysis system suitable for clinical use after a thorough validation involving more patients. Graphical abstract Scheme of the proposed model for characterizing the heterogeneity of GBM regions and predicting the overall survival and progression free survival of GBM patients. (1) Acquisition of pretreatment MRI images; (2) Affine registration of T1-WI image with its corresponding FLAIR images, and GBM subtype (phenotypes) labelling; (3) Extraction of nine texture features from the three texture scales fine, medium, and coarse derived from each of GBM regions; (4) Comparing heterogeneity between GBM regions by ANOVA test; Survival analysis using Univariate (Spearman rank correlation between features and survival (i.e., PFS and OS) based on each of the GBM regions, Kaplan-Meier estimator and log-rank test to predict the PFS and OS of patient groups that grouped based on median of feature), and multivariate (random forest model) for predicting the PFS and OS of patients groups that grouped based on median of PFS and OS. [ABSTRACT FROM AUTHOR]

The authors propose a novel‐makeup detection approach that assists face recognition systems to achieve a higher‐accuracy rate while dealing with makeup images. Makeup features are defined in this work using biologically inspired features (BIFs). To establish makeup depictive features more specifically, colour and texture features are essential to be extracted from images. Hence, they create makeup depictive features in the last complex layer of BIFs (C2) as average skin tone (AST) and a histogram of oriented gradient (HOG), where AST is representative of colour and HOG exhibits texture. The proposed makeup BIFs are extracted from grayscale images and instead of breaking the face image into several patches, the whole face image is employed. This resulted in the performance acceleration as well as higher accuracy rate compared with state‐of‐the‐art makeup detection schemes. Subsequently, they employ a machine learning scheme to train the makeup detection system by feeding it makeup and non‐makeup labelled images. They exploit the correlation‐based method for the face recognition system and compare the results with the direct two‐dimensional principal component analysis face recognition scheme for makeup datasets. Experimental results show the highest accuracy rate of 97.07% was achieved by the proposed algorithm for face recognition system considering makeup. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the prognostic value of texture features based on late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) images in hypertrophic cardiomyopathy (HCM) patients with systolic dysfunction.Methods: 67 HCM patients with systolic dysfunction (41 male and 26 female, mean age ± standard deviation, 46.20 years ± 13.38) were enrolled. All patients underwent 1.5 T CMR cine and LGE imaging. Texture features were extracted from LGE images. Cox proportional hazard analysis and Kaplan-Meier analysis were used to determine the association of texture features and traditional parameters with event free survival.Results: Family history (hazard ratio [HR]=2.558, 95 % confidence interval [CI]=1.060-6.180), NYHA III-IV (HR=5.627, CI=1.652-19.173), left ventricular ejection fraction (HR=0.945, CI=0.902-0.991), left ventricular end-diastolic volume index (HR=1.006, CI=1.000-1.012), LGE extent (HR=1.911, CI=1.348-2.709) and three texture parameters [X0_H_skewness (HR=0.783, CI=0.691-0.889), X0_GLCM_cluster_tendency (HR=0.735, CI=0.616-0.877) and X0_GLRLM_energy (HR=1.344, CI=1.173-1.540)] were significantly associated with event free survival in univariate analysis (p<0.05). The HR of LGE extent (HR=1.548 [CI=1.046-2.293], 1.650 [CI=1.122-2.428] and 1.586 [CI=1.044-2.409] per 10 % increase, p<0.05) remained significant when adjusted by one of the three texture features.Conclusion: Increased LGE heterogeneity (higher X0_GLRLM_energy, lower X0_H_skewness and lower X0_GLCM_cluster_tendency) was associated with adverse events in HCM patients with systolic dysfunction.Key Points: • Textural analysis from CMR can be applied in HCM. • Texture features derived from LGE images can capture fibrosis heterogeneity. • CMR texture analysis provides prognostic information in HCM patients. [ABSTRACT FROM AUTHOR]

This paper investigated the possibility of discriminating tomato yellow leaf curl disease by a hyperspectral imaging technique. A hyperspecral imaging system collected hyperspectral images of both healthy and infected tomato leaves. The reflectance spectra, first derivative reflectance spectra and absolute reflectance difference spectra in the wavelength range of 500-1000 nm of both background and the leaf area were analyzed to select sensitive wavelengths and band ratios. 853 nm was selected to create a mask image for background segmentation, while 720 nm from the reflectance spectra, four peaks (560, 575, 712, and 729 nm) from the first derivative spectra and, four wavelengths with higher values (586, 720 nm) and lower values (690, 840 nm) in the absolute difference spectra were selected as a set of sensitive wavelengths. Four band ratio images (560/575, 712/729, 586/690, and 720/840 nm) were compared with four widely used vegetation indices (VIs). 24 texture features were extracted using grey level co-occurrence matrix (GLCM), respectively. The performance of each feature was evaluated by receiver operator characteristic (ROC) curve analysis. The best threshold values of each feature were calculated by Yonden’s index. Mean value of correlation (COR_MEAN) extracted from the band ratio image (720/840 nm) had the best performance, whose AUC value was 1.0. The discrimination result for a validation set based on its best threshold value was 100%. This research also demonstrated that multispectral images at 560, 575 and 720 nm have a potential for detecting tomato yellow leaf curl virus infection in field applications. [ABSTRACT FROM AUTHOR]

Purpose: Prostate segmentation in computed tomography (CT) images is useful for treatment planning and procedure guidance such as external beam radiotherapy and brachytherapy. However, because of the low, soft tissue contrast of CT images, manual segmentation of the prostate is a time‐consuming task with high interobserver variation. In this study, we proposed a semiautomated, three‐dimensional (3D) segmentation for prostate CT images using shape and texture analysis and we evaluated the method against manual reference segmentations. Methods: The prostate gland usually has a globular shape with a smoothly curved surface, and its shape could be accurately modeled or reconstructed having a limited number of well‐distributed surface points. In a training dataset, using the prostate gland centroid point as the origin of a coordination system, we defined an intersubject correspondence between the prostate surface points based on the spherical coordinates. We applied this correspondence to generate a point distribution model for prostate shape using principal component analysis and to study the local texture difference between prostate and nonprostate tissue close to the different prostate surface subregions. We used the learned shape and texture characteristics of the prostate in CT images and then combined them with user inputs to segment a new image. We trained our segmentation algorithm using 23 CT images and tested the algorithm on two sets of 10 nonbrachytherapy and 37 postlow dose rate brachytherapy CT images. We used a set of error metrics to evaluate the segmentation results using two experts' manual reference segmentations. Results: For both nonbrachytherapy and post‐brachytherapy image sets, the average measured Dice similarity coefficient (DSC) was 88% and the average mean absolute distance (MAD) was 1.9 mm. The average measured differences between the two experts on both datasets were 92% (DSC) and 1.1 mm (MAD). Conclusions: The proposed, semiautomatic segmentation algorithm showed a fast, robust, and accurate performance for 3D prostate segmentation of CT images, specifically when no previous, intrapatient information, that is, previously segmented images, was available. The accuracy of the algorithm is comparable to the best performance results reported in the literature and approaches the interexpert variability observed in manual segmentation. [ABSTRACT FROM AUTHOR]

Identification of clouds in optical images is often a necessary step toward their use. However, aimed at the cloud detection methods used on GF-1 is relatively less. In order to meet the requirement of accurate cloud detection in GF-1 WFV imagery, a new method based on the combination of band operation and spatial texture feature(BOTF) is proposed in this paper. First of all, the BOTF algorithm minimize interference of highlight surface and cloud regions by the band operation, and then distinguish between cloud area and non-cloud area with spatial texture feature. Finally, the cloud mask can be acquired by threshold segmentation method. The method was validated using scenes. The results indicate that the BOTF performs well under normal conditions, and the average overall accuracy of BOTF cloud detection is better than 90%. The proposed method can meet the needs of routine work. [ABSTRACT FROM AUTHOR]

The detection and segmentation of tumor region in brain image is a critical task due to the similarity between abnormal and normal region. In this article, a computer-aided automatic detection and segmentation of brain tumor is proposed. The proposed system consists of enhancement, transformation, feature extraction, and classification. The shift-invariant shearlet transform (SIST) is used to enhance the brain image. Further, nonsubsampled contourlet transform (NSCT) is used as multiresolution transform which transforms the spatial domain enhanced image into multiresolution image. The texture features from grey level co-occurrence matrix (GLCM), Gabor, and discrete wavelet transform (DWT) are extracted with the approximate subband of the NSCT transformed image. These extracted features are trained and classified into either normal or glioblastoma brain image using feed forward back propagation neural networks. Further, K-means clustering algorithm is used to segment the tumor region in classified glioblastoma brain image. The proposed method achieves 89.7% of sensitivity, 99.9% of specificity, and 99.8% of accuracy. [ABSTRACT FROM AUTHOR]

The extraction of textural information from images to explore parameters related to food quality is very common. In this paper, the extraction of quality features from MRI is performed by a new fractal algorithm and second order statistics, as an alternative to the classical texture approaches. The proposed method needs fewer features than classical textures, computing them with a lower computational complexity. Quality characteristics from MRI of Iberian loins are extracted to validate the practical application of the proposed algorithm. The new method is compared to the standard fractal algorithm and also to the classical texture approaches. Characteristics obtained by means of the new fractal algorithm, by the standard fractal algorithm, and by the three classical texture methods are correlated to the results obtained by using physico-chemical methods. The correlations achieve coefficients higher than 0.75. Therefore, the new algorithm could be used to calculate quality parameters of meat products in a non-destructive and efficient way, being also suitable for the meat industries to characterize meat products. [ABSTRACT FROM PUBLISHER]

Purpose: The study was designed to evaluate 1) the relationship between PET image textural features (TFs) and SUVs, metabolic tumour volume (MTV), total lesion glycolysis (TLG) and tumour characteristics in a large prospective and homogenous cohort of oestrogen receptor-positive (ER+) breast cancer (BC) patients, and 2) the capability of those parameters to predict response to neoadjuvant chemotherapy (NAC). Methods: 171 consecutive patients with large or locally advanced ER+ BC without distant metastases underwent an F-FDG PET examination before NAC. The primary tumour was delineated with an adaptive threshold segmentation method. Parameters of volume, intensity and texture (entropy, homogeneity, contrast and energy) were measured and compared with tumour characteristics determined on pre-treatment breast biopsy (Wilcoxon rank-sum test). The correlation between PET-derived parameters was determined using Spearman's coefficient. The relationship between PET features and pathological findings was determined using the Wilcoxon rank-sum test. Results: Spearman's coefficients between SUV and TFs were 0.43, 0.24, -0.43 and -0.15 respectively for entropy, homogeneity, energy and contrast; they were higher between MTV and TFs: 0.99, 0.86, -0.99 and -0.87. All TFs showed a significant association with the histological type (IDC vs. ILC; 0.02 < P < 0.03) but didn't with immunohistochemical characteristics. SUV and TLG predicted the pathological response ( P = 0.0021 and P = 0.02 respectively); TFs didn't ( P: 0.27, 0.19, 0.94, 0.19 respectively for entropy, homogeneity, energy and contrast). Conclusions: The correlation of TFs was poor with SUV parameters and high with MTV. TFs showed a significant association with the histological type. Finally, while SUV and TLG were able to predict response to NAC, TFs failed. [ABSTRACT FROM AUTHOR]

Ultra‐high‐frequency (UHF) method is an effective approach to power transformer partial discharge (PD) detection. The feature parameters extracted from UHF PD signal can be applied to insulation defect type recognition. In this study, a novel feature parameters extraction method based on texture features in time–frequency (TF) representation is proposed. PD detections of four typical insulation defects were performed on a 110kV oil‐immersed power transformer. Time‐domain waveform and corresponding TF representations or images of UHF PD signals were obtained. About 36 texture features were extracted from the grey‐level co‐occurrence matrix of TF images. The texture features were reduced into six new feature parameters by principal component analysis. These feature parameters were used as input of the support vector machine classifier for defect type recognition. The recognition accuracies of four kinds of typical defects reached 97.67, 97.00 97.67 and 98.33% proving that the proposed extracted feature parameters are suitable for insulation defect type recognition in power transformer. [ABSTRACT FROM AUTHOR]

Background: Texture features are being increasingly evaluated in F-fluorodeoxyglucose positron emission tomography (F-FDG PET) as adjunctive imaging biomarkers in a number of different cancers. Whilst studies have reported repeatability between scans, there have been no studies that have specifically investigated the effect that the time of acquisition post-injection of F-FDG has on texture features. The aim of this study was to investigate if texture features change between scans performed at different time points post-injection. Results: Fifty-four patients (30 male, 24 female, mean age 35.1 years) with neurofibromatosis-1 and suspected malignant transformation of a neurofibroma underwent F-FDG PET/computed tomography (CT) scans at 101.5 ± 15.0 and 251.7 ± 18.4 min post-injection of 350 MBq F-FDG to a standard clinical protocol. Following tumour segmentation on both early and late scans, first- ( n = 37), second- ( n = 25) and high-order ( n = 31) statistical features, as well as fractal texture features ( n = 6), were calculated and a comparison was made between the early and late scans for each feature. Of the 54 tumours, 30 were benign and 24 malignant on histological analysis or on clinical follow-up for at least 5 years. Overall, 25/37 first-order, 9/25 second-order, 13/31 high-order and 3/6 fractal features changed significantly ( p < 0.05) between early and late scans. The corresponding proportions for the 30 benign tumours alone were 22/37, 7/25, 8/31 and 2/6 and for the 24 malignant tumours, 11/37, 6/25, 8/31 and 0/6, respectively. Conclusions: Several texture features change with time post-injection of F-FDG. Thus, when comparing texture features in intra- and inter-patient studies, it is essential that scans are obtained at a consistent time post-injection of F-FDG. [ABSTRACT FROM AUTHOR]

Objectives/hypothesis: Plasma Epstein-Barr virus (EBV) DNA concentrations predict prognosis in patients with nasopharyngeal carcinoma (NPC). Recent evidence also indicates that intratumor heterogeneity on F-18 fluorodeoxyglucose positron emission tomography (18 F-FDG PET) scans is predictive of treatment outcomes in different solid malignancies. Here, we sought to investigate the prognostic value of heterogeneity parameters in patients with primary NPC.Study Design: Retrospective cohort study.Methods: We examined 101 patients with primary NPC who underwent pretreatment 18 F-FDG PET/computed tomography. Circulating levels of EBV DNA were measured in all participants. The following PET heterogeneity parameters were collected: histogram-based heterogeneity parameters, second-order texture features (uniformity, contrast, entropy, homogeneity, dissimilarity, inverse difference moment), and higher-order (coarseness, contrast, busyness, complexity, strength) texture features.Results: The median follow-up time was 5.14 years. Total lesion glycolysis (TLG), tumor heterogeneity measured by histogram-based parameter skewness, and the majority of second-order or higher-order texture features were significantly associated with overall survival (OS) and/or recurrence-free survival (RFS). In multivariate analysis, age (P =.005), EBV DNA load (P = .0002), and uniformity (P = .001) independently predicted OS. Only skewness retained the independent prognostic significance for RFS. Tumor stage, standardized uptake value, or TLG did not show an independent association with survival endpoints. The combination of uniformity, EBV DNA load, and age resulted in a more reliable prognostic stratification (P < .001).Conclusions: Tumor heterogeneity is superior to traditional PET parameters for predicting outcomes in primary NPC. The combination of uniformity with EBV DNA load can improve prognostic stratification in this clinical entity.Level Of Evidence: 4 Laryngoscope, 127:E22-E28, 2017. [ABSTRACT FROM AUTHOR]

In this paper, different texture-analysis methods are used to describe different cloud types in MODIS satellite images. A universal technique is suggested for the formation of efficient sets of textural features using the algorithm of truncated scanning of the features for different classifiers based on neural networks and cluster-analysis methods. Efficient sets of textural features are given for the considered classifiers; the cloud-image classification results are discussed. The characteristics of the classification methods used in this work are described: the probabilistic neural network, K-nearest neighbors, self-organizing Kohonen network, fuzzy C-means, and density clustering algorithm methods. It is shown that the algorithm based on a probabilistic neural network is the most efficient. It provides for the best classification reliability for 25 cloud types and allows the recognition of 11 cloud types with a probability greater than 0.7. As an example, the cloud classification results are given for the Tomsk region. The classifications were carried out using full-size satellite cloud images and different methods. The results agree with each other and agree well with the observational data from ground-based weather stations. [ABSTRACT FROM AUTHOR]

Image saliency analysis plays an important role in various applications such as object detection, image compression, and image retrieval. Traditional methods for saliency detection ignore texture cues. In this paper, we propose a novel method that combines color and texture cues to robustly detect image saliency. Superpixel segmentation and the mean-shift algorithm are adopted to segment an original image into small regions. Then, based on the responses of a Gabor filter, color and texture features are extracted to produce color and texture sub-saliency maps. Finally, the color and texture sub-saliency maps are combined in a nonlinear manner to obtain the final saliency map for detecting salient objects in the image. Experimental results show that the proposed method outperforms other state-of-the-art algorithms for images with complex textures. [ABSTRACT FROM AUTHOR]

Purpose: To develop a new algorithm to measure the similarity between the query lung mass and reference lung mass data set for content-based medical image retrieval (CBMIR). Methods: A lung mass data set including 746 mass regions of interest (ROIs) was assembled. Among them, 375 ROIs depicted malignant lesions and 371 depicted benign lesions. Each mass ROI is represented by a vector of 26 texture features. A kernel function was employed to map the original data in input space to a feature space. In this space, a semisupervised distance metric was learned, which used differential scatter discriminant criterion to represent the semantic relevance, and the regularization term to represent the visual similarity. The learned distance metric can measure the similarity of the query mass and reference mass data set. The clustering accuracy is used to configure the parameters. The retrieval accuracy and classification accuracy are used as the performance assessment index. Results: After configuring the parameters, a mean clustering accuracy of 0.87 can be achieved. For retrieval accuracy, our algorithm achieves better performance than other state-of-the-art retrieval algorithms when applying a leave-one-out validation method to the testing data set. For classification accuracy, the area under the ROC curve of our algorithm can be achieved as 0.941±0.006. The running times of 346 query images with the proposed algorithm are 5.399 and 6.0 s, respectively. Conclusions: The study results demonstrated the proposed algorithm outperforms the compared algorithms, when taking the semantic relevant and visual similarity into account in kernel space. The algorithm can be used in a CBMIR system for a query mass to retrieve similarity masses, which can help doctors make better decisions. [ABSTRACT FROM AUTHOR]

Image oversegmentation creates small, compact, and irregularly shaped regions subject to further clustering. Consideration of texture characteristics can improve the resulting quality of the clustering process. Existing methods based on an orthogonal transform into frequency domain can extract texture features of arbitrarily shaped regions only from inscribed rectangles. We propose a method for extracting texture features of entire arbitrarily shaped image regions using orthogonal transforms. Furthermore, we introduce a mathematically correct method for unifying spectral dimensions that is necessary for accurate comparison and classification of spectra with different dimensions. The proposed method is particularly suitable for classifying areas with periodic and quasiperiodic textures. Our approach exploits the texture periodification property of certain orthogonal transforms that is based on insertion of zeros into the spectrum. We identified some of those orthogonal transforms which possess this important property and also provide mathematical proofs of our claims. Last, we show that inclusion of luminance and chrominance components into the feature vector increases the precision of the proposed method which then becomes suitable for natural scene images as well. [ABSTRACT FROM AUTHOR]

Facial expression recognition in the wild is a very challenging task. We describe our work in static and continuous facial expression recognition in the wild. We evaluate the recognition results of gray deep features and color deep features, and explore the fusion of multimodal texture features. For the continuous facial expression recognition, we design two temporal-spatial dense scale-invariant feature transform (SIFT) features and combine multimodal features to recognize expression from image sequences. For the static facial expression recognition based on video frames, we extract dense SIFT and some deep convolutional neural network (CNN) features, including our proposed CNN architecture. We train linear support vector machine and partial least squares classifiers for those kinds of features on the static facial expression in the wild (SFEW) and acted facial expression in the wild (AFEW) dataset, and we propose a fusion network to combine all the extracted features at decision level. The final achievement we gained is 56.32% on the SFEW testing set and 50.67% on the AFEW validation set, which are much better than the baseline recognition rates of 35.96% and 36.08%. [ABSTRACT FROM AUTHOR]

The usage of biomedical imaging in the diagnosis of dementia is increasingly widespread. A number of works explore the possibilities of computational techniques and algorithms in what is called computed aided diagnosis. Our work presents an automatic parametrization of the brain structure by means of a path generation algorithm based on hidden Markov models (HMMs). The path is traced using information of intensity and spatial orientation in each node, adapting to the structure of the brain. Each path is itself a useful way to characterize the distribution of the tissue inside the magnetic resonance imaging (MRI) image by, for example, extracting the intensity levels at each node or generating statistical information of the tissue distribution. Additionally, a further processing consisting of a modification of the grey level co-occurrence matrix (GLCM) can be used to characterize the textural changes that occur throughout the path, yielding more meaningful values that could be associated to Alzheimer's disease (AD), as well as providing a significant feature reduction. This methodology achieves moderate performance, up to 80.3% of accuracy using a single path in differential diagnosis involving Alzheimer-affected subjects versus controls belonging to the Alzheimer's disease neuroimaging initiative (ADNI). [ABSTRACT FROM AUTHOR]

One of the properties of wood is a mechanical property, includes: hardness, strength, cleavage resistance, etc. Among these properties there that can be measured or estimated by visual observation on cross-sectional areas of wood, which is based on inter-fiber density, fiber size, and lines that build the annual rings. In this paper, we proposed a new wood quality classification method based on edge detections. Edge detection is applied to the wood test images with the aim to improving the characteristics of wood fibers so as to make it easier to distinguish their quality. Gray Level Cooccurrence Matrix (GLCM) used to obtain wood texture features, while the wood quality classification done by Naïve Bayes classifier. Found in our experimental results that the first-order edge detection is likely to provide a good accuracy rate and precision. The second order edge detection is highly dependent on the choice of parameters and tends to give worse classification results, as filtering the original wood image, thus blurring characteristics related to wood density. Selection of features obtained from co-occurrence matrix is also quite affected the classification results. [ABSTRACT FROM AUTHOR]

Impervious surface area (ISA) is one of the most important indicators of urban environments. At present, based on multi-resolution remote sensing images, numerous approaches have been proposed to extract impervious surface, using statistical estimation, subpixel classification and spectral mixture analysis method of sub-pixel analysis. Through these methods, impervious surfaces can be effectively applied to regional-scale planning and management. However, for the large scale region, high resolution remote sensing images can provide more details, and therefore they will be more conducive to analysis environmental monitoring and urban management. Since the purpose of this study is to map impervious surfaces more effectively, three classification algorithms (random forests, decision trees, and artificial neural networks) were tested for their ability to map impervious surface. Random forests outperformed the decision trees, and artificial neural networks in precision. Combining the spectral indices and texture, random forests is applied to impervious surface extraction with a producer's accuracy of 0.98, a user's accuracy of 0.97, and an overall accuracy of 0.98 and a kappa coefficient of 0.97. [ABSTRACT FROM AUTHOR]

There are many methods available for printer identification of questioned documents, however most of them need identical contents of the training and testing documents. There is no effective method yet when the contents of the training and testing documents are different. To overcome this obstacle a method based on synthetic texture analysis is proposed in this paper. The inner texture of printed characters is reconstructed in order to generate an artificial textural image similar to the real texture while independent of document content. Fast Fourier Transform (FFT) and Gray Level Co-occurrence Matrix (GLCM) methods are then used for feature extraction. Experimental results demonstrate that the proposed method can achieve a high recognition rate and provide a new platform for printer identification independent on the content of documents. [ABSTRACT FROM AUTHOR]

In this paper, we explore an original way to compute texture features for color images in a vector process. Using a dedicated approach for color ordering, we produce a complete framework for color mathematical morphology adapted to human visual system characteristics. Then, morphological multiscale texture features are defined. To understand the texture feature behavior, we present the feature response to basic images variations. Finally, we compare the texture feature performance in front of a classical classification task using Outex database. [ABSTRACT FROM AUTHOR]

Purpose: Glioblastoma multiforme (GBM) is the most common and aggressive primary brain cancer. Four molecular subtypes of GBM have been described but can only be determined by an invasive brain biopsy. The goal of this study is to evaluate the utility of texture features extracted from magnetic resonance imaging (MRI) scans as a potential noninvasive method to characterize molecular subtypes of GBM and to predict 12-month overall survival status for GBM patients. Methods: The authors manually segmented the tumor regions from postcontrast T1 weighted and T2 fluid-attenuated inversion recovery (FLAIR) MRI scans of 82 patients with de novo GBM. For each patient, the authors extracted five sets of computer-extracted texture features, namely, 48 segmentation-based fractal texture analysis (SFTA) features, 576 histogram of oriented gradients (HOGs) features, 44 run-length matrix (RLM) features, 256 local binary patterns features, and 52 Haralick features, from the tumor slice corresponding to the maximum tumor area in axial, sagittal, and coronal planes, respectively. The authors used an ensemble classifier called random forest on each feature family to predict GBM molecular subtypes and 12-month survival status (a dichotomized version of overall survival at the 12-month time point indicating if the patient was alive or not at 12 months). The performance of the prediction was quantified and compared using receiver operating characteristic (ROC) curves. Results: With the appropriate combination of texture feature set, image plane (axial, coronal, or sagittal), and MRI sequence, the area under ROC curve values for predicting different molecular subtypes and 12-month survival status are 0.72 for classical (with Haralick features on T1 postcontrast axial scan), 0.70 for mesenchymal (with HOG features on T2 FLAIR axial scan), 0.75 for neural (with RLM features on T2 FLAIR axial scan), 0.82 for proneural (with SFTA features on T1 postcontrast coronal scan), and 0.69 for 12-month survival status (with SFTA features on T1 postcontrast coronal scan). Conclusions: The authors evaluated the performance of five types of texture features in predicting GBM molecular subtypes and 12-month survival status. The authors' results show that texture features are predictive of molecular subtypes and survival status in GBM. These results indicate the feasibility of using tumor-derived imaging features to guide genomically informed interventions without the need for invasive biopsies. [ABSTRACT FROM AUTHOR]

The present study proposes a computer-aided classification (CAC) system for three kidney classes, viz. normal, medical renal disease (MRD) and cyst using B-mode ultrasound images. Thirty-five B-mode kidney ultrasound images consisting of 11 normal images, 8 MRD images and 16 cyst images have been used. Regions of interest (ROIs) have been marked by the radiologist from the parenchyma region of the kidney in case of normal and MRD cases and from regions inside lesions for cyst cases. To evaluate the contribution of texture features extracted from de-speckled images for the classification task, original images have been pre-processed by eight de-speckling methods. Six categories of texture features are extracted. One-against-one multi-class support vector machine (SVM) classifier has been used for the present work. Based on overall classification accuracy (OCA), features from ROIs of original images are concatenated with the features from ROIs of pre-processed images. On the basis of OCA, few feature sets are considered for feature selection. Differential evolution feature selection (DEFS) has been used to select optimal features for the classification task. DEFS process is repeated 30 times to obtain 30 subsets. Run-length matrix features from ROIs of images pre-processed by Lee's sigma concatenated with that of enhanced Lee method have resulted in an average accuracy (in %) and standard deviation of 86.3 ± 1.6. The results obtained in the study indicate that the performance of the proposed CAC system is promising, and it can be used by the radiologists in routine clinical practice for the classification of renal diseases. [ABSTRACT FROM AUTHOR]

This work presents an approach for combining texture and shape feature sets towards age‐invariant face recognition. Physiological studies have proven that the human visual system can recognise familiar faces at different ages from the face outline alone. Based on this scientific fact, the phase congruency features for shape analysis were adopted to produce a face edge map. This was beneficial in tracking the craniofacial growth pattern for each subject. Craniofacial growth is common during childhood years, but after the age of 18, the texture variations start to show as the effect of facial aging. Therefore, in order to handle such texture variations, a variance of the well‐known local binary pattern (LBP) texture descriptor, known as LBP variance was adopted. The results showed that fusing the shape and the texture features set yielded better performance than the individual performance of each feature set. Moreover, the individual verification accuracy for each feature set was improved when they were transformed to a kernel discriminative common vectors presentation. The system achieved an overall verification accuracy of above 93% when it was evaluated over the FG‐NET face aging database. [ABSTRACT FROM AUTHOR]

Purpose: To help improve efficacy of screening mammography by eventually establishing a new optimal personalized screening paradigm, the authors investigated the potential of using the quantitative multiscale texture and density feature analysis of digital mammograms to predict near-term breast cancer risk. Methods: The authors' dataset includes digital mammograms acquired from 340 women. Among them, 141 were positive and 199 were negative/benign cases. The negative digital mammograms acquired from the "prior" screening examinations were used in the study. Based on the intensity value distributions, five subregions at different scales were extracted from each mammogram. Five groups of features, including density and texture features, were developed and calculated on every one of the subregions. Sequential forward floating selection was used to search for the effective combinations. Using the selected features, a support vector machine (SVM) was optimized using a tenfold validation method to predict the risk of each woman having image-detectable cancer in the next sequential mammography screening. The area under the receiver operating characteristic curve (AUC) was used as the performance assessment index. Results: From a total number of 765 features computed from multiscale subregions, an optimal feature set of 12 features was selected. Applying this feature set, a SVM classifier yielded performance of AUC= 0.729±0.021. The positive predictive value was 0.657 (92 of 140) and the negative predictive value was 0.755 (151 of 200). Conclusions: The study results demonstrated a moderately high positive association between risk prediction scores generated by the quantitative multiscale mammographic image feature analysis and the actual risk of a woman having an image-detectable breast cancer in the next subsequent examinations. [ABSTRACT FROM AUTHOR]

Image segmentation plays a key role in many fields such as image processing and recognition. Although various segmentation methods have been proposed in recent decades, most of these methods are based on only a single feature space. How to combine various features to image segmentation is a challenge problem. To address this problem, the authors propose to combine different features based on evolutionary multiobjective optimisation. Two optimisation objectives, which are based on colour and texture features, respectively, are therefore designed for image segmentation. The experiments show that the author's method is able to combine multiple features for image segmentation successfully. [ABSTRACT FROM AUTHOR]

The present study proposes a computer-aided diagnosis (CAD) system for the diagnosis of grades of fatty liver disease, namely mild, moderate and severe fatty liver along with normal liver tissue. Fifty-three B-mode ultrasound images consisting of 12 normal, 14 mild, 14 moderate and 13 severe fatty liver images are used. Based on the visual interpretations by the radiologists, region of interests (ROIs) from within the liver and one ROI from the diaphragm region are considered from each image. The texture features of these ROIs are combined in three ways to form ratio features, inverse ratio features and additive features. The sub-sets of optimal features are obtained by a differential evolution feature selection (DEFS) algorithm and a support vector machine (SVM) has been used for the classification task. The Laws ratio features have shown better performance with an average accuracy and standard deviation of 84.9±3.2. Hence, the CAD system could be useful to the radiologists in diagnosing grades of fatty liver disease. [ABSTRACT FROM AUTHOR]

Quantitative methods for noninvasive diagnosis of scars are a challenging issue in medicine. This work aims to implement a texture analysis method for quantitatively discriminating abnormal scars from normal scars based on second-harmonic generation (SHG) images. A local difference local binary pattern (LD-LBP) operator combined with a wavelet transform was explored to extract diagnosis features from scar SHG images that were related to the alteration in collagen morphology. Based on the quantitative parameters including the homogeneity, directional and coarse features in SHG images, the scar collagen SHG images were classified into normal or abnormal scars by a support vector machine classifier in a leave-one-out cross-validation procedure. Our experiments and data analyses demonstrated apparent differences between normal and abnormal scars in terms of their morphological structure of collagen. By comparing with gray level co-occurrence matrix, wavelet transform, and combined basic local binary pattern and wavelet transform with respect to the accuracy and receiver operating characteristic analysis, the method proposed herein was demonstrated to achieve higher accuracy and more reliable classification of SHG images. This result indicated that the extracted texture features with the proposed method were effective in the classification of scars. It could provide assistance for physicians in the diagnostic process. [ABSTRACT FROM AUTHOR]

The process which attaches label to a digital image by understanding the contents of image is termed as Automatic Image Annotation (AIA). Color and texture are the prominent features of a digital image. The content based image understanding is possible by using the feature strength of color and texture of an image. A classifier is designed using Decision Trees (DT) and Rough Sets (RS) to tag untagged images. Rough Set Exploration System (RSES) is used to develop decision tree and rough set based classifiers for classification of Corel images. In this paper, the result obtained using these classifiers are presented and discussed. Using rough sets cent percent accuracy is achieved for dinosaur images while for flower, horses and mountain categories the results are improved. [ABSTRACT FROM AUTHOR]

Writer verification methods from handwriting images have been widely used in biometrics, forensic casework and so on. However, their performance conducted by a computer is still far behind that of human beings. This study proposes an offline writer verification method which uses a new hybrid feature. The characteristics of this new feature are the combination of shape and pen pressure information. For shape information, the authors use the weighted direction code histogram, which is often used in Japanese handwriting recognition and writer verification methods. For pen pressure information, the authors use the texture features of infrared (IR) images obtained from a multi‐band image scanner. Although the simple use of pen pressure information encoded in the IR image cannot improve the geometric mean (g‐mean)‐based error rate, the use of clipping process and second‐order statistics can decrease the g‐mean‐based error rate from 4.8 to 3.2%. [ABSTRACT FROM AUTHOR]

Most existing classification studies use spectral information and those were adequate for cities or plains. This paper explores classification method suitable for the ALOS (Advanced Land Observing Satellite) in mountainous terrain. Mountainous terrain mapping using ALOS image faces numerous challenges. These include spectral confusion with other land cover features, topographic effects on spectral signatures (such as shadow). At first, topographic radiometric correction was carried out to remove the illumination effects of topography. In addition to spectral features, texture features were used to assist classification in this paper. And texture features extracted based on GLCM (Gray Level Co-occurrence Matrix) were not only used for segmentation, but also used for building rules. The performance of the method was evaluated and compared with Maximum Likelihood Classification (MLC). Results showed that the object-oriented method integrating spectral and texture features has achieved overall accuracy of 85.73% with a kappa coefficient of 0.824, which is 13.48% and 0.145 respectively higher than that got by MLC method. It indicated that texture features can significantly improve overall accuracy, kappa coefficient, and the classification precision of existing spectrum confusion features. Object-oriented method Integrating spectral and texture features is suitable for land use extraction of ALOS image in mountainous terrain. [ABSTRACT FROM AUTHOR]

In order to map the impervious surfaces for a coastal area, three non-parametric approaches: Classification and Regression Trees, Nearest Neighbor (NN), and Support Vector Machines (SVM)- were applied to a dataset of very high resolution archival orthoimages which had poor radiometry, with only red, green and blue spectral information. An object-based image analysis was carried out and four feature vectors were defined as input data for the classifier: 1) red, green and blue spectral information plus four relative spectral indices; 2) Dataset 1 plus texture indices based on the grey level co-occurrence matrix (GLCM); 3) Dataset 1 plus texture indices based on the local variance; and 4) the vector defined by 1), 2) and 3). Two classification strategies were developed in order to identify the pervious/impervious target classes (aggregation of all the subclasses and binary classification). The separability matrix was used to present the statistical comparative results clearly and concisely. The results obtained from this work showed that 1) “GLCM” texture indices did not lead to more accurate results; 2) the incorporation of the local variance texture index significantly increased the accuracy of the classification; 3) the classification results were not significantly affected by the classification strategy employed; 4) SVM and NN achieved statistically more accurate classification results than CARTs; 5) the SVM classifier was more efficient than the NN classifier, while NN was less dependent on the feature vector, and 6) suitable accuracy results were obtained for the most accurate approaches (SVM) which achieved a 89.4% overall accuracy. [ABSTRACT FROM PUBLISHER]

The Cardio Vascular Disease (CVD) is the most fatal disease in the world in terms of deaths. The cardio vascular disease, associated with stroke and heart attack, is mainly caused by the increase in calcium deposition in the carotid artery. The Intima-Media Thickness (IMT) of the Common Carotid Artery (CCA) is widely used as an early indicator of CVD. The risk of CVD varies with age groups and this can be categorized based on the texture pattern of image of the carotid artery. This work presents an automated method to classify the carotid artery abnormalities by determining an appropriate Region of Interest (ROI), extracting the texture features, and calculating the IMT. The Ultrasound specimen image is acquired, intensity normalized, pre-processed to remove the speckle noise and then segmented. The texture analysis for segmented images is done using AM - FM techniques. The instantaneous values of the amplitude and frequency of each image specimen is obtained and it is quantized. It is then compared with the standard texture pattern, to identify whether the artery is normal or abnormal. Simulation results shows significant texture differences between the higher-risk age group of >60 years and the lower-risk age group of <50 and the 50-60 age group. Detecting the level of CVD was done by measuring the IMT. The overall process aims at implementing a fully automated system which helps in avoiding human errors, while measuring these values. The measurement technique is described in detail, highlighting the advantages compared to other methods and reporting the experimental results. Finally, the intrinsic accuracy of the system is estimated by an analytical approach. It also decreases inter-reader bias, potentially making it applicable for use in cardiovascular risk assessment. [ABSTRACT FROM AUTHOR]

In this paper, an approach based on combined color and texture features to classify raisins is presented. Least squares support vector machine (LSSVM), linear discriminant analysis, and soft independent modeling of class analogy were used to construct classification models. A total of 480 images were captured from four grades of raisin samples by a Basler 601 fc IEEE1394 digital camera, 200 images were randomly selected to create calibration model (training set), and remaining images were used to verify the model (prediction set). Color features and texture features were obtained from two color spaces: red-green-blue and hue-saturation-intensity using histogram method and gray level co-occurrence matrix method, respectively. Our results indicate that the best performance with about 95% of average correct answer rate is achieved by LSSVM using combined color and texture features from HSI color space. This result is significantly higher than the performance of solely used color or texture features. The combined color and texture features coupled with a LSSVM classifier are a highly accurate way for raisin quality classification. [ABSTRACT FROM AUTHOR]