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Striped stem‐borer (SSB) larvae are one of the most serious insect pests that damages rice. In this study, the combined hyperspectral imaging and chemometric method are proposed detect the early SSB larvae infestation in rice stems. Firstly, hyperspectral reflectance data (390–1040 nm) of rice stems were collected for normal, 3 days of infection, 6 days of infection, and 9 days of infection, respectively. On the spectra, the average spectra of the region of interest of the rice stalk samples were extracted, and 19 characteristic wavelengths were screened by the successive projection algorithm (SPA). Morphological processing was performed on the pseudo‐color (generally known as RGB) images of rice stalk samples on the images. Then five image texture features based on the gray‐scale co‐occurrence matrix (GLCM) and nine image color features based on color moments were obtained. The evaluation models of SSB larval infestation based on full spectra, characteristic wavelengths, GLCM, color moments, and data fusion were developed using partial least squares discriminant analysis (PLS‐DA), respectively. The results show that performance of the PLS‐DA model based on the fusion of characteristic wavelengths and GLCM is best. The accuracy of both the comprehensive modeling set and the prediction set is above 97%. The accuracy of the degree of infestation classification is good for all categories, and only the samples on the third day of infection are slightly less accurate. In conclusion, the hyperspectral imaging technology can be used to effectively detect the infection degree situation of early SSB larval infestation. Practical Applications: Chemical control using insecticides to manage SSB infestation on rice is still the farmers' first choice. However, in the middle and late stages of rice growth, SSB larvae may moth into the inside of the rice stalk, and the rice provides a shelter for SSB so that the sprayed pesticides cannot reach SSB larvae, which produce a large number of overwintering SSB. This leads to the misuse of chemical pesticides, which causes serious pollution to the environment, pesticide residues also cause serious harm to the human body, and it also makes the pest resistance to pesticides rapidly increase. Therefore, it is necessary to find a rapid and effective detection method to detect the early infestation of SSB larvae on rice to reduce the use of pesticides and improve rice yield. [ABSTRACT FROM AUTHOR]

Liver ultrasound is a cost‐effective, non‐invasive, and sufficient technique to diagnose most of the liver disorders. The recent advancements in research in image processing have led to the development of image‐based liver disorder classification systems. In spite of being popular in the diagnostic imaging of liver, ultrasound images, owing to their poor quality, render the conventional and state of the art segmentation and feature extraction techniques incapable, to accurately classify a large mixed group of liver disorders; due to the similarities and differences in appearances among the different and same disorders, respectively. Classification of liver disorders using ultrasound images poses various challenges at each phase, from segmentation to classification. There is a need for better segmentation, powerful features, and optimal classification parameter combinations to obtain decent classification accuracy, when a large sub‐set of liver disorders is considered. In this work, the region of interest is extracted using iso‐contour technique. Feature extraction is performed using multi‐level fractal features and multi‐domain wavelet‐texture features for better discrimination capability. Then, an optimization problem is formulated, for minimizing the five fold cross validation error to classify 10 types of disorders, both focal and diffused, by selecting the best features, suitable classifier, and its parameters using the particle swarm optimization technique for obtaining better classification. An overall accuracy of 91% is obtained using the proposed features in addition to 50% reduction in multi‐level fractal feature set which justifies the efficacy of the proposed technique. [ABSTRACT FROM AUTHOR]

Computer‐aided diagnosis (CAD) is a common tool for the detection of diseases, particularly different types of cancers, based on medical images. Digital image processing thus plays a significant role in the processing and analysis of medical images for diseases identification and detection purposes. In this study, an efficient CAD system for the acute lymphoblastic leukaemia (ALL) detection is proposed. The proposed approach entails two phases. In the first phase, the white blood cells (WBCs) are segmented from the microscopic blood image. The second phase involves extracting important features, such as shape and texture features from the segmented cells. Eventually, on the extracted features, Naïve Bayes and k‐nearest neighbour classifier techniques are implemented to identify the segmented cells into normal and abnormal cells. The performance of the proposed approach has been assessed through comprehensive experiments carried out on the well‐known ALL‐IDB data set of microscopic blood images. The experimental results demonstrate the superior performance of the proposed approach over the state‐of‐the‐art in terms of accuracy rate in which achieved 98.7%. [ABSTRACT FROM AUTHOR]

Since histopathological images exist in various forms, performing segmentation on these images is tedious. While in cancer‐free colon tissue, epithelial cells generally have an elliptical shape; their structure alters in a malignant tissue. This study proposes a technique consisting of colon biopsy image segmentation and a hybrid set of features for classification, and is evaluated on multiple databases with various levels of magnifications. This study presents a novel image segmentation method with multi‐level thresholding based on Rényi's two‐dimensional entropy with a cultural algorithm (2DReCA). Based on the entropy, elliptical epithelial cells, being the region of interest, are identified from the segmented background. After successful segmentation, shape descriptors are extracted with morphological operations. Two sets of texture features (grey‐level co‐occurrence matrix and block‐wise elliptical local binary pattern) are calculated based on pre‐processed grey‐scale colon images. The proposed hybrid feature vector set, then concatenates the extracted features for training and testing with a random forest classifier. The proposed segmentation and classification model is evaluated by considering four data sets consisting of various colon images at different magnifications. In addition, it is evaluated by multiple performance measures and compared with existing techniques. [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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

In recent years, active contour models (ACM) have been considered as powerful tools for image segmentation and object tracking in computer vision and image processing applications. This article presents a new tracking method based on parametric active contour models. In the proposed method, a new pressure energy called “texture pressure energy” is added to the energy function of the parametric active contour model to detect and track a texture target object in a texture background. In this scheme, the texture features of the contour are calculated by a moment-based method. Then, by comparing these features with texture features of the target object, the contour curve is expanded or contracted to be adapted to the object boundaries. Experimental results show that the proposed method is more efficient and accurate in the tracking of objects compare to the traditional ones, when both object and background are textures in nature. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 187–198, 2009 [ABSTRACT FROM AUTHOR]