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In land-use classification of hyperspectral remote sensing (RS) images, traditional classification methods often experience large amount of datasets and low efficiency. To solve these problems, a fast machine-learning method, the extreme learning machine (ELM) algorithm, was introduced. However, basic use of the ELM usually encounters problems of unstable classification results and low classification accuracy. Hence, in this paper, optimization methods for ELM-based RS image classification were mainly discussed and applied to solve the bottleneck problems. From the three perspectives of ensemble learning, making full use of image texture features, and deep learning, three classification optimization methods have been designed and implemented. The results show that: 1) To some extent, all the three methods can achieve a balance between classification accuracy and efficiency, i.e., they can maintain the advantage of ELM algorithm in classification efficiency and speed while have better classification accuracy; 2) The image texture feature optimization method (LBP-KELM) solves the problem of unsatisfactory classification results experienced by the ensemble learning optimization method (Ensemble-ELM) and further improves classification accuracy. However, the classification results are sensitive to the type of dataset; and 3) Fortunately, the optimization method combined with deep learning (CNN-ELM) can meet the application needs of multiple datasets. Furthermore, it can also further improve classification accuracy.

Pneumonia is a condition that can be life-threatening and affects a high number of children around the world. Lung ultrasound can be used for the diagnosis of pneumonia, but requires high experience. This paper presents an approach for pneumonia detection based on texture analysis of ultrasound images. Several measures were taken in healthy tissues and pneumonia lesions, and the most significant features were identified by statistical analysis. The results of the analysis of variance and exploratory analysis suggest that detection of pneumonia is possible based on image texture features. © 2019 IEEE. IEEE Colombia Section;IEEE Signal Processing Society Colombia Chapter;Universidad Industrial de Santander

Soil is an important environment for crop growth. Quick and accurately access to soil nutrient content information is a prerequisite for scientific fertilization. In this work, hyperspectral imaging (HSI) technology was applied for the classification of soil types and the measurement of soil total nitrogen (TN) content. A total of 183 soil samples collected from Shangyu City (People’s Republic of China), were scanned by a near-infrared hyperspectral imaging system with a wavelength range of 874–1734 nm. The soil samples belonged to three major soil types typical of this area, including paddy soil, red soil and seashore saline soil. The successive projections algorithm (SPA) method was utilized to select effective wavelengths from the full spectrum. Pattern texture features (energy, contrast, homogeneity and entropy) were extracted from the gray-scale images at the effective wavelengths. The support vector machines (SVM) and partial least squares regression (PLSR) methods were used to establish classification and prediction models, respectively. The results showed that by using the combined data sets of effective wavelengths and texture features for modelling an optimal correct classification rate of 91.8%. could be achieved. The soil samples were first classified, then the local models were established for soil TN according to soil types, which achieved better prediction results than the general models. The overall results indicated that hyperspectral imaging technology could be used for soil type classification and soil TN determination, and data fusion combining spectral and image texture information showed advantages for the classification of soil types.

Speckle noise reduction is an important area of research in the field of ultrasound image processing. Several algorithms for speckle noise characterization and analysis have been recently proposed in the area. Synthetic ultrasound images can play a key role in noise evaluation methods as they can be used to generate a variety of speckle noise models under different interpolation and sampling schemes, and can also provide valuable ground truth data for estimating the accuracy of the chosen methods. However, not much work has been done in the area of modelling synthetic ultrasound images, and in simulating speckle noise generation to get images that are as close as possible to real ultrasound images. An important aspect of simulated synthetic ultrasound images is the requirement for extensive quality assessment for ensuring that they have the texture characteristics and gray-tone features of real images. This paper presents texture feature analysis of synthetic ultrasound images using local binary patterns (LBP) and demonstrates the usefulness of a set of LBP features for image quality assessment. Experimental results presented in the paper clearly show how these features could provide an accurate quality metric that correlates very well with subjective evaluations performed by clinical experts.

A segmentation method of out-of-focus image regions for processed metal surfaces, based on focus textural features is proposed. Such regions contain small amount of useful information. The object of study is a metal surface, which has a cone shape. Some regions of images are blurred because the depth of field of industrial cameras is limited. Automatic removal of out-of-focus regions in such images is one of the possible solutions to this problem. Focus texture features were used to calculate characteristics that describe the sharpness of particular image area. Such features are used in autofocus systems of microscopes and cameras, and their application for segmentation of out-of-focus regions of images is unusual. Thirty-four textural features were tested on a set of metal surface images with out-of-focus regions. The most useful features, usable for segmentation of an image more accurately, are an average grey level and spatial frequency. Proposed segmentation method of out-of-focus image regions for metal surfaces can be successfully applied for evaluation of processing quality of materials with the use of industrial cameras. The method has simple implementation and high calculating speed.

The paper presents the development of a computeraided diagnostic (CAD) system for the early detection of endometrial cancer. The proposed CAD system supports reproducibility through texture feature standardization, standardized multifeature selection, and provides physicians with comparative distributions of the extracted texture features. The CAD system was validated using 516 regions of interest (ROIs) extracted from 52 subjects. The ROIs were equally distributed among normal and abnormal cases. To support reproducibility, the RGB images were first gamma corrected and then converted into HSV and YCrCb. From each channel of the gamma-corrected YCrCb, HSV, and RGB color systems, we extracted the following texture features: 1) statistical features (SFs), 2) spatial gray-level dependence matrices (SGLDM), and 3) gray-level difference statistics (GLDS). The texture features were then used as inputs with support vector machines (SVMs) and the probabilistic neural network (PNN) classifiers. After accounting for multiple comparisons, texture features extracted from abnormal ROIs were found to be significantly different than texture features extracted from normal ROIs. Compared to texture features extracted from normal ROIs, abnormal ROIs were characterized by lower image intensity, while variance, entropy, and contrast gave higher values. In terms of ROI classification, the best results were achieved by using SF and GLDS features with an SVM classifier. For this combination, the proposed CAD system achieved an 81% correct classification rate. 2168-2194 © 2014 IEEE. 19 3 1129 1136 Cited By :2

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.

International audience; In this paper, a full reference document image quality assessment (FR DIQA) method using texture features is proposed. Local binary patterns (LBP) as texture features are extracted at the local and global levels for each image. For each extracted LBP feature set, a similarity measure called the LBP similarity index (LBPSI) is computed. A weighting strategy is further proposed to improve the LBPSI obtained based on local LBP features. The LBPSIs computed for both local and global features are then combined to get the final LBPSI, which also provides the best performance for DIQA. To evaluate the proposed method, two different datasets were used. The first dataset is composed of document images, whereas the second one includes natural scene images. The mean human opinion scores (MHOS) were considered as ground truth for performance evaluation. The results obtained from the proposed LBPSI method indicate a significant improvement in automatically/accurately predicting image quality, especially on the document image-based dataset.

International audience; Textures are typical elements of natural scene images widely used in pattern recognition and image classification. Noise, often being present in acquired images, deteriorates texture features (characteristics), and it is desirable both to suppress it and to preserve a texture. This task is quite difficult even for the most advanced filters, and the resulting denoising efficiency can be quite low. Due to this, it is desirable to predict a denoising efficiency before filtering to decide whether it is worth filtering a given image or not. In this paper, we analyze several quantitative criteria (metrics) that can characterize filtering efficiency. Prediction strategy is described and its accuracy is studied. Several modern filtering techniques are analyzed and compared. Based on this, practical recommendations are given. © 2016, Springer-Verlag London.

This paper presents a new approach for unsupervised segmentation of histopathological tissue images. This approach has two main contributions. First, it introduces a new set of high-level texture features to represent the prior knowledge of spatial organization of the tissue components. These texture features are defined on the tissue components, which are approximately represented by tissue objects, and quantify the frequency of two component types being cooccurred in a particular spatial relationship. As they are defined on components, rather than on image pixels, these object cooccurrence features are expected to be less vulnerable to noise and variations that are typically observed at the pixel level of tissue images. Second, it proposes to obtain multiple segmentations by multilevel partitioning of a graph constructed on the tissue objects and combine them by an ensemble function. This multilevel graph partitioning algorithm introduces randomization in graph construction and refinements in its multilevel scheme to increase diversity of individual segmentations, and thus, improve the final result. The experiments on 200 colon tissue images reveal that the proposed approachthe object cooccurrence features together with the multilevel segmentation algorithmis effective to obtain high-quality results. The experiments also show that it improves the segmentation results compared to the previous approaches. © 1964-2012 IEEE.

In this work, we have developed an adjunct Computer Aided Diagnostic (CAD) technique that uses 3D acquired ultrasound images of the ovary and data mining algorithms to accurately characterize and classify benign and malignant ovarian tumors. In this technique, we extracted image-texture based and Higher Order Spectra (HOS) based features from the images. The significant features were then selected and used to train and test the Decision Tree (DT) classifier. The proposed technique was validated using 1000 benign and 1000 malignant images, obtained from 10 patients with benign and 10 with malignant disease, respectively. On evaluating the classifier with 10-fold stratified cross validation, we observed that the DT classifier presented a high accuracy of 95.1%, sensitivity of 92.5% and specificity of 97.7%. Thus, the four significant features could adequately quantify the subtle changes and nonlinearities in the pixel intensities. The preliminary results presented in this paper indicate that the proposed technique can be reliably used as an adjunct tool for ovarian tumor classification since the system is accurate, completely automated, cost-effective, and can be easily written as a software application for use in any computer.

International audience; We propose, in this paper, a method of image indexing and research by exploiting the digital component of the images. Our method is founded on the representation of the image digital component by a vector of characteristics its own for the indexed image. This vector will be called: numerical signature of the image. With this intention, we exploited the texture of the image by using the Gabor’s wavelets. In this work, each image of the training base is indexed and represented by its characteristics (texture). This representation, which is carried out in offline, is characterized by the saving, in a data base, of all the signatures of the indexed images. What enables us, in online, to carry out a numerical search for similarity compared to a request image. This same request image will be indexed in online with the same algorithm used in offline. In order to evaluate the performances we tested our application on a training images basis containing 320 mammography. The results obtained show well that the representation of the digital component of the images proves to be significant as regards search for information in imagery.

The intima-media thickness (IMT) of the common carotid artery (CCA) is widely used as an early indicator of cardiovascular disease (CVD). It was proposed but not thoroughly investigated that the media layer (ML), its composition and texture, may be indicative for identifying the risk of stroke and differentiating between patients of high and low risk. In this study we investigate the usefulness of texture analysis of the ML of the CCA. The study was performed on 100 longitudinal ultrasound images acquired from asymptomatic subjects at risk of atherosclerosis. The images were separated into three different age groups, namely below 50, 50 to 60, and above 60 years old. A total of 61 different texture features were extracted from the intima-media complex (IMC), ML and the intima layer (IL). The IMC and ML were segmented manually by a neurovascular expert and automatically by a snakes segmentation system. It was shown that texture features extracted from the IL, ML and IMC are significantly different (mean, gray scale median (GSM), standard deviation, contrast, difference variance, periodicity) and that some of them can be associated with the increase (difference variance, entropy) or decrease (GSM) of patient's age. It was also shown that the GSM of the ML falls linearly with increasing ML thickness (MLT) and with increasing age. Further research on more subjects is required for estimating other features that may provide information for patients at risk of stroke. Conference code: 77628

Noise reduction is essential for increasing the visual quality or as a preprocessing step for further automated analysis in video sequences and video coding. The objective of this work was to investigate four different video despeckle filtering techniques and evaluate them using visual assessment by two medical experts, texture features analysis, and video quality evaluation metrics. The four proposed video despeckle filtering techniques were evaluated on 10 ultrasound videos of the common carotid artery (CCA). The filters were applied on the whole video frame and in a selected by the user region of interest (ROI) which included the atherosclerotic carotid plaque. The despeckle filters were based on linear filtering (DsFlsmv), hybrid median filtering (DsFhmedian), nonlinear filtering (DsFkuwahara) and speckle reducing anisotropic diffusion (DsFsrad) filtering. Our results showed that, the best video despeckle filtering methods were the linear filter DsFlsmv, followed by the hybrid median filter DsFhmedian. Both filters improved the visual perception evaluation by experts and gave better texture and video quality metrics. Further work on a larger number of videos and by employing additional despeckle filtering techniques is required for the evaluation of video despeckle filtering methods on ultrasound videos of the CCA. © 2012 IEEE. 721 726 Sponsors: IEEE IEEE Computer Society University of Cyprus Biological and AI Foundation (BAIF) Frederick University Conference code: 95206 Cited By :8

Mammographic breast density refers to the prevalence of fibroglandular tissue as it appears on a mammogram. Breast density is not only an important risk for developing breast cancer but can also mask abnormalities. Breast density information can be used for planning individualized screening and treatment. In this work, statistical distributions of different texture descriptors and their combination are investigated with Support Vector Machines (SVMs) for objective breast density classification: Scale Invariant Feature Transforms (SIFT), Local Binary Patterns (LBP) and texton histograms. SIFT is an approach for detecting and extracting local feature descriptors that are reasonably invariant to changes in illumination, image noise, rotation, scaling and small changes in viewpoint. The SIFT descriptor is a coarse descriptor of the edges found in the keypoints. LBPs provide a robust and computationally simple way for describing pure local binary patterns in a texture. They provide information regarding the prevalence of different edge patterns and uniformity. Textons are defined under the operational definition of clustered filter responses and provide a statistical and structural unifying approach for texture characterization. The breast density classification accuracy of the SVM classifiers modeled on the histograms of the three different sets of texture features separately and their combination is evaluated on the Medical Image Analysis Society (MIAS) mammographic database and the results are presented. The combination of the statistical distributions of all the different texture features allows for the highest classification accuracy, reaching over 93%. © 2012 IEEE. 732 737 Sponsors: IEEE IEEE Computer Society University of Cyprus Biological and AI Foundation (BAIF) Frederick University Conference code: 95206 Cited By :9

International audience; The objective of this paper is to investigate the effectiveness of a Multilayer Perceptron (MLP) to discriminate subjects with and without osteoporosis using a set of five parameters characterizing the quality of the bone structure. These parameters include Age, Bone mineral content (BMC), Bone mineral density (BMD), fractal Hurst exponent (Hmean) and coocurrence texture feature (CoEn). The purpose of the study is to detect the potential usefulness of the combination of different features to increase the classification rate of 2 populations composed of osteporotic patients and control subjects. k-fold Cross Validation (CV) was used in order to assess the accuracy and reliability of the neural network validation. Compared to other methods MLP-based analysis provides an accurate and reliable platform for osteoporosis prediction. Moreover, the results show that the combination of the five features provides better performance in terms of discrimination of the subjects.

Carotid plaques have been associated with ipsilateral neurological symptoms. High-resolution ultrasound can provide information not only on the degree of carotid artery stenosis but also on the characteristics of the arterial wall including the size and consistency of atherosclerotic plaques. The aim of this study is to determine whether the addition of ultrasonic plaque texture features to clinical features in patients with asymptomatic internal carotid artery stenosis (ACS) improves the ability to identify plaques that will produce stroke. 1121 patients with ACS have been scanned with ultrasound and followed for a mean of 4 years. It is shown that the combination of texture features based on second-order statistics spatial gray level dependence matrices (SGLDM) and clinical factors improves stroke prediction (by correctly predicting 89 out of the 108 cases that were symptomatic). Here, the best classification results of $77 \pm 1.8\%$ were obtained from the use of the SGLDM texture features with support vector machine classifiers. The combination of morphological features with clinical features gave slightly worse classification results of $76 \pm 2.6\%$. These findings need to be further validated in additional prospective studies. © 2012 IEEE. 16 5 966 973 Cited By :24

The intima-media thickness (IMT) of the common carotid artery (CCA) is widely used as an early indicator of cardiovascular disease (CVD). Clinically, there is strong interest in identifying how the composition and texture of the media layer (ML) can be associated with the risk of stroke. In this study, we use 2-D amplitude-modulation frequency-modulation (AMFM) analysis of the intima-media complex (IMC), the ML, and intima layer (IL) of the CCA to detect texture changes as a function of age and sex. The study was performed on 100 ultrasound images acquired from asymptomatic subjects at risk of atherosclerosis. To investigate texture variations associated with age, we separated them into three age groups: 1) patients younger than 50 2) patients aged between 50 and 60 years old and 3) patients over 60 years old. We also separated the patients by sex. The IMC, ML, and IL were segmented manually by a neurovascular expert and also by a snake-based segmentation system. To reject strong edge artifacts, we prefilter with an AMFM filterbank that is centered along the horizontal frequency axis (parallel to the long axis of the IMC, ML, and IL), while removing the low-pass filter estimates and frequency bands with large, vertical frequency components. To investigate significant texture changes, we extract the instantaneous amplitude (IA) and the magnitude of the instantaneous frequency (IF) over each layer component, for low-, medium-, and high-frequency AMFM components. We detected significant texture differences between the higher risk age group of 60 years versus the lower risk age group of Cited By :18

This study introduces the use of multiscale amplitude modulation-frequency modulation (AM-FM) texture analysis of multiple sclerosis (MS) using magnetic resonance (MR) images from brain. Clinically, there is interest in identifying potential associations between lesion texture and disease progression, and in relating texture features with relevant clinical indexes, such as the expanded disability status scale (EDSS). This longitudinal study explores the application of 2-D AM-FM analysis of brain white matter MS lesions to quantify and monitor disease load. To this end, MS lesions and normal-appearing white matter (NAWM) from MS patients, as well as normal white matter (NWM) from healthy volunteers, were segmented on transverse T2-weighted images obtained from serial brain MR imaging (MRI) scans (0 and 6-12 months). The instantaneous amplitude (IA), the magnitude of the instantaneous frequency (IF), and the IF angle were extracted from each segmented region at different scales. The findings suggest that AM-FM characteristics succeed in differentiating 1) between NWM and lesions 2) between NAWM and lesions and 3) between NWM and NAWM. A support vector machine (SVM) classifier succeeded in differentiating between patients that, two years after the initial MRI scan, acquired an EDSS ≤ 2 from those with EDSS > 2 (correct classification rate = 86%). The best classification results were obtained from including the combination of the low-scale IA and IF magnitude with the medium-scale IA. The AM-FM features provide complementary information to classical texture analysis features like the gray-scale median, contrast, and coarseness. The findings of this study provide evidence that AM-FM features may have a potential role as surrogate markers of lesion load in MS. © 2006 IEEE. 15 1 119 129 Cited By :31

The purpose of this paper is to explore the use of texture features analysis for evaluating normal appearing white matter (NA WM) areas in brain Magnetic Resonance Imaging (MRI) scans of patients with a first demyelinating event (Clinically Isolated Syndrome-CIS) that have been subsequently converted (in follow up MRI scans) in demyelinating plaques. Texture features analysis was carried out in NA WM areas selected from transverse sections of T2-weighted MRI scans acquired from 10 untreated subjects that had developed their first neurological-demyelinating event (CIS). All NA WM areas and demyelinating lesions were manually segmented by an experienced multiple sclerosis (MS) neurologist. The patients were twice scanned with brain MRI with an interval of6-12 months (time 0, initial MRI and time 6-12, repeated MRI scan). Areas in repeated MRI scan, where new lesions had been developed, were mapped back to their corresponding initial MRI scan NA WM areas. Following that, texture features were extracted from those NA WM areas that evolved to demyelinating lesions and compared to texture features extracted from NA WM areas of the initial MRI scan that didn't evolve to demyelinating lesions. It was shown that for the texture features investigated, no single feature could be used to differentiate between NA WM at 0 months (NAW-C-O), and ROIS that will evolve into demyalating lesions also at 0 months (ROIS-O). Several features could be used to differentiate between ROIS-O vs Lesions at months, NA WM at 6 months and Lesions at 6 months, as well as between Lesions at 0 months vs Lesions at 6 months. Future work will investigate the proposed method on more subjects. © 2010 IEEE. Sponsors: University of Ioannina National Technical University of Athens University of Patras Ionian University Unit Med. Technol. Intelligent Inf. Syst., Univ. Ioannina Conference code: 83786

In this study the value of magnetic resonance image (MRI) shape and texture analysis was assessed in multiple sclerosis (MS) subjects, both in differentiating between normal or normal appearing and abnormal tissue and in assessing disease onset. Shape and texture analysis was carried out in normal brain white matter and lesions detected in transverse sections of T2-weighted magnetic resonance (MR) images acquired from 22 symptomatic untreated subjects. All detected brain lesions were manually segmented by an experienced MS neurologist and confirmed by a radiologist. The results showed that there was no significant difference for most of the shape features and for all of the texture features between MS lesions at 0 and 6-12 months. For some texture features there was significant difference between normal or normal appearing tissue and MS lesions at 0 and 6-12 months. Further research with more subjects is required for computing shape and texture features that may provide information for better and earlier differentiation between normal tissue and MS lesions. ©2009 IEEE. Sponsors: IBM Italia S.p.A. Datamed SA, Healthcare Integrator LinkSCEEM: Link. Sci. Comput. Eur. East. Mediterr. AGIOS THERISSOS M.R.1. Medical Diagnostic Center University of Cyprus Conference code: 79527 Cited By :10

The intima-media thickness (IMT) of the common carotid artery (CCA) is widely used as an early indicator of the development of cardiovascular disease (CVD). It was proposed but not thoroughly investigated that the media layer (ML) thickness (MLT), its composition, and its texture may be indicative of cardiovascular risk and for differentiating between patients with high and low risk. In this study, we investigate an automated method for segmenting the ML and the intima layer (IL) and measurement of the MLT and the intima layer thickness (ILT) in ultrasound images of the CCA. The snakes segmentation method was used and was evaluated on 100 longitudinal ultrasound images acquired from asymptomatic subjects, against manual segmentation performed by a neurovascular expert. The mean plusmn standard deviation (sd) for the first and second sets of manual and the automated IMT, MLT, and ILT measurements were 0.71 plusmn 0.17 mm, 0.72 plusmn 0.17 mm, 0.67 plusmn 0.12 mm; 0.25 plusmn 0.12 mm, 0.27 plusmn 0.14 mm, 0.25 plusmn 0.11 mm; and 0.43 plusmn 0.10 mm, 0.44 plusmn 0.13 mm, and 0.42 plusmn 0.10 mm, respectively. There was overall no significant difference between the manual and the automated IMC, ML, and IL segmentation measurements. Therefore, the automated segmentation method proposed in this study may be used successfully in the measurement of the MLT and ILT complementing the manual measurements. MLT was also shown to increase with age (for both the manual and the automated measurements). Future research will incorporate the extraction of texture features from the segmented ML and IL bands, which may indicate the risk of future cardiovascular events. However, more work is needed for validating the proposed technique in a larger sample of subjects.

In this study the value of magnetic resonance image (MRI) shape and texture analysis was assessed in multiple sclerosis (MS) subjects, both in differentiating between normal and abnormal tissue and in assessing disease progression. Shape and texture analysis was carried out in normal and diseased lesions in transverse sections of T2-weighted magnetic resonance (MR) images acquired from 10 symptomatic untreated subjects with clinically isolated syndrome (CIS) scanned twice, with an interval of 6-12 months. All detected brain lesions were manually segmented by an experienced neurologist and confirmed by a neuro-radiologist, whilst different shape and texture features were extracted from the segmented lesions. The results showed that there was no significance difference between shape features of 0 and 6-12 months. For some texture features there was significance difference between normal tissue and MS lesions at 0 and 6-12 months and between MS lesions at 0 and 6-12 months (i.e contrast, difference variance, difference entropy, and other). Further research with more subjects is required for computing shape and texture features that may provide information for differentiating between normal tissue and MS lesions as well as for longitudinal monitoring of these lesions. In addition the proposed methodology can be used for the assessment of subjects at risk of developing future neurological events. The extracted shape and features can also offer additional information of undiagnosed lesions. ©2008 IEEE. 58 61 Sponsors: Chinese Acad. Sci. Shenzhen Academician Consultation Center Chinese University of Hong Kong Chinese Academy of Sciences Institute of Biomedical and Health Engineering Shenzhen Institute of Advanced Technology Key Laboratory for Biomedical Informatics and Health Engineering Conference code: 73498 Cited By :7

The robustness of image features is a very important consideration in quantitative image analysis. The objective of this paper is to investigate the robustness of a range of image texture features using hematoxylin stained breast tissue microarray slides which are assessed while simulating different imaging challenges including out of focus, changes in magnification and variations in illumination, noise, compression, distortion, and rotation. We employed five texture analysis methods and tested them while introducing all of the challenges listed above. The texture features that were evaluated include co-occurrence matrix, center-symmetric auto-correlation, texture feature coding method, local binary pattern, and texton. Due to the independence of each transformation and texture descriptor, a network structured combination was proposed and deployed on the Rutgers private cloud. The experiments utilized 20 randomly selected tissue microarray cores. All the combinations of the image transformations and deformations are calculated, and the whole feature extraction procedure was completed in 70 minutes using a cloud equipped with 20 nodes. Center-symmetric auto-correlation outperforms all the other four texture descriptors but also requires the longest computational time. It is roughly 10 times slower than local binary pattern and texton. From a speed perspective, both the local binary pattern and texton features provided excellent performance for classification and content-based image retrieval.

The objective of developing this software is to achieve auto-segmentation and tissue characterization. Therefore, the present algorithm has been designed and developed for analysis of medical images based on hybridization of syntactic and statistical approaches, using artificial neural network (ANN). This algorithm performs segmentation and classification as is done in human vision system, which recognizes objects; perceives depth; identifies different textures, curved surfaces, or a surface inclination by texture information and brightness. The analysis of medical image is directly based on four steps: 1) image filtering, 2) segmentation, 3) feature extraction, and 4) analysis of extracted features by pattern recognition system or classifier. In this paper, an attempt has been made to present an approach for soft tissue characterization utilizing texture-primitive features with ANN as segmentation and classifier tool. The present approach directly combines second, third, and fourth steps into one algorithm. This is a semisupervised approach in which supervision is involved only at the level of defining texture-primitive cell; afterwards, algorithm itself scans the whole image and performs the segmentation and classification in unsupervised mode. The algorithm was first tested on Markov textures, and the success rate achieved in classification was 100%; further, the algorithm was able to give results on the test images impregnated with distorted Markov texture cell. In addition to this, the output also indicated the level of distortion in distorted Markov texture cell as compared to standard Markov texture cell. Finally, algorithm was applied to selected medical images for segmentation and classification. Results were in agreement with those with manual segmentation and were clinically correlated.