Gray Level Run Length Matrix Research Articles

Texture based feature extraction methods for content based medical image retrieval systems.

The developments of content based image retrieval (CBIR) systems used for image archiving are continued and one of the important research topics. Although some studies have been presented general image achieving, proposed CBIR systems for archiving of medical images are not very efficient. In presented study, it is examined the retrieval efficiency rate of spatial methods used for feature extraction for medical image retrieval systems. The investigated algorithms in this study depend on gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), and Gabor wavelet accepted as spatial methods. In the experiments, the database is built including hundreds of medical images such as brain, lung, sinus, and bone. The results obtained in this study shows that queries based on statistics obtained from GLCM are satisfied. However, it is observed that Gabor Wavelet has been the most effective and accurate method.

Statistical Methods for Quantitatively Detecting Fungal Disease from Fruits' Images

In this paper we have proposed statistical methods for detecting fungal disease and classifying based on disease severity levels. Most fruits diseases are caused by bacteria, fungi, virus, etc of which fungi are responsible for a large number of diseases in fruits. In this study images of fruits, affected by different fungal symptoms are collected and categorized based on disease severity. Statistical features like block wise, gray level co-occurrence matrix (GLCM), gray level runlength matrix (GLRLM) are extracted from these images. The nearest neighbor classifier using Euclidean distance was used to classify images as partially affected, moderately affected, severely affected and normal. The average classification accuracies are 91.37% and 86.715% using GLCM and GLRLM features. The average classification accuracy has increased to 94.085% using block wise features.

Characterization of spatiotemporal changes for the classification of dynamic contrast-enhanced magnetic-resonance breast lesions

The early detection of breast cancer is one of the most important predictors in determining the prognosis for women with malignant tumours. Dynamic contrast-enhanced magnetic-resonance imaging (DCE-MRI) is an important imaging modality for detecting and interpreting the different breast lesions from a time sequence of images and has proved to be a very sensitive modality for breast-cancer diagnosis. However, DCE-MRI exhibits only a moderate specificity, thus leading to a high rate of false positives, resulting in unnecessary biopsies that are stressful and physically painful for the patient and lead to an increase in the cost of treatment. There is a strong medical need for a DCE-MRI computer-aided diagnosis tool that would offer a reliable support to the physician's decision providing a high level of sensitivity and specificity. In our study we investigated the possibility of increasing differentiation between the malignant and the benign lesions with respect to the spatial variation of the temporal enhancements of three parametric maps, i.e., the initial enhancement (IE) map, the post-initial enhancement (PIE) map and the signal enhancement ratio (SER) map, by introducing additional methods along with the grey-level co-occurrence matrix, i.e., a second-order statistical method already applied for quantifying the spatiotemporal variations. We introduced the grey-level run-length matrix and the grey-level difference matrix, representing two additional, second-order statistical methods, and the circular Gabor as a frequency-domain-based method. Each of the additional methods is for the first time applied to the DCE-MRI data to differentiate between the malignant and the benign breast lesions. We applied the least-square minimum-distance classifier (LSMD), logistic regression and least-squares support vector machine (LS-SVM) classifiers on a total of 115 (78 malignant and 37 benign) breast DCE-MRI cases. The performances were evaluated using ten experiments of a ten-fold cross-validation. Our experimental analysis revealed the PIE map, together with the feature subset in which the discriminating ability of the co-occurrence features was increased by adding the newly introduced features, to be the most significant for differentiation between the malignant and the benign lesions. That diagnostic test - the aforementioned combination of parametric map and the feature subset achieved the sensitivity of 0.9193 which is statistically significantly higher compared to other diagnostic tests after ten-experiments of a ten-fold cross-validation and gave a statistically significantly higher specificity of 0.7819 for the fixed 95% sensitivity after the receiver operating characteristic (ROC) curve analysis. Combining the information from all the three parametric maps significantly increased the area under the ROC curve (AUC) of the aforementioned diagnostic test for the LSMD and logistic regression; however, not for the LS-SVM. The LSMD classifier yielded the highest area under the ROC curve when using the combined information, increasing the AUC from 0.9651 to 0.9755. Introducing new features to those of the grey-level co-occurrence matrix significantly increased the differentiation between the malignant and the benign breast lesions, thus resulting in a high sensitivity and improved specificity.

Changes on image texture features of breakfast flakes cereals during water absorption

Normally breakfast cereal flakes are consumed by pouring them into a bowl and covering them with fresh or cold milk. During this process the liquid uptake causes changes in the surface and internal matrix of breakfast cereals that influence texture and integrity. Some breakfast cereal as flakes have a translucent structure that could provide information about the solid matrix and air cells and how they change during liquid absorption. The objective of the study was to assess the image texture changes of corn flakes and frosted flakes during water absorption at 5, 15 and 25 °C, employing 11 image feature textures extracted from grey-level co-occurrence matrix and grey-level run length matrix (at three directions) and to relate the fractal dimension (FD) of images with rupture force (RF) reduction during soaking of both flakes at 5 °C. The most relevant result from principal component analysis calculated with a matrix of 54 (soaking times) × 22 (texture features), shows that it was possible to distinguish an isolated group consisting of different soaking times at the same water temperature in each breakfast cereal flakes evaluated, corroborating that superficial liquid imbibition is important during the liquid absorption process when flakes are soaked. Furthermore, standardized FD could be related to RF in the period when samples tend to search for an equilibrium state.

Application of Fuzzy Enhancement in the Diagnosis of Liver Cancer from Ultrasound Images

Fuzzy enhancement is applied in computer aided diagnosis of liver cancer from B mode ultrasound images as a pre-processing procedure in this paper. It was evaluated with three classifiers including K means, back propagation neural network and support vector machine using 25 features from single gray-level statistic, gray-level co-occurrence matrix (GLCM), and gray-level run-length matrix (GLRLM). The results show that the fuzzy enhancement algorithm can improve classification accuracy of normal liver, liver cancer and Hemangioma from B mode ultrasound images for three classifiers. It is proved that fuzzy enhancement as an efficient preprocessing procedure could be used in the computer aided diagnosis system of liver cancer.

Texture-based features for classification of mammograms using decision tree

Mammogram—breast X-ray—is considered the most effective, low cost, and reliable method in early detection of breast cancer. Although general rules for the differentiation between benign and malignant breast lesions exist, only 15–30 % of masses referred for surgical biopsy are actually malignant. In this work, an approach is proposed to develop a computer-aided classification system for cancer detection from digital mammograms. The proposed system consists of three major steps. The first step is region of interest (ROI) extraction of 256 × 256 pixels size. The second step is the feature extraction; we used a set of 19 GLCM and GLRLM features, and the 19 (nineteen) features extracted from gray-level run-length matrix and gray-level co-occurrence matrix could distinguish malignant masses from benign masses with an accuracy of 96.7 %. Further analysis was carried out by involving only 12 of the 19 features extracted, which consists of 5 features extracted from GLCM matrix and 7 features extracted from GLRL matrix. The 12 selected features are as follows: Energy, Inertia, Entropy, Maxprob, Inverse, SRE, LRE, GLN, RLN, LGRE, HGRE, and SRLGE; ARM with 12 features as prediction can distinguish malignant mass image and benign mass with a level of accuracy of 93.6 %. Further analysis showed that area under the receiver operating curve was 0.995, which means that the accuracy level of classification is good or very good. Based on that data, it was concluded that texture analysis based on GLCM and GLRLM could distinguish malignant image and benign image with considerably good result. The third step is the classification process; we used the technique of decision tree using image content to classify between normal and cancerous masses. The proposed system was shown to have the large potential for cancer detection from digital mammograms.

Identification of nine Iranian wheat seed varieties by textural analysis with image processing

Applying machine vision techniques to classify wheat seeds based on their varieties is an objective method which can increase the accuracy of this process in real applications. In this study, several textural feature groups of seeds images were examined to evaluate their efficacy in identification of nine common Iranian wheat seed varieties. On the whole, 1080 gray scale images of bulk wheat seeds (120 images of each variety) were acquired at a stable illumination condition (florescent ring light). Totally, 131 textural features were extracted from gray level, GLCM (gray level cooccurrence matrix), GLRM (gray level run-length matrix), LBP (local binary patterns), LSP (local similarity patterns) and LSN (local similarity numbers) matrices. The so-called stepwise discrimination method was employed to select and rank the most significant textural features of each matrix individually as well as features of all matrices simultaneously. LDA (linear discriminate analysis) classifier was employed for classification using top selected features. The average classification accuracy of 98.15% was obtained when top 50 of all selected features were used in the classifier. The results confirmed that LSP, LSN and LBP features had a significant influence on the improvement of classification accuracy compared to previous studies.

Evaluation of Texture for Classification of Abdominal Aortic Aneurysm After Endovascular Repair

The use of the endovascular prostheses in abdominal aortic aneurysm has proven to be an effective technique to reduce the pressure and rupture risk of aneurysm. Nevertheless, in a long-term perspective, complications such as leaks inside the aneurysm sac (endoleaks) could appear causing a pressure elevation and increasing the danger of rupture consequently. At present, computed tomographic angiography (CTA) is the most common examination for medical surveillance. However, endoleak complications cannot always be detected by visual inspection on CTA scans. The investigation on new techniques to detect endoleaks and analyse their effects on treatment evolution is of great importance for endovascular aneurysm repair (EVAR) technique. The purpose of this work was to evaluate the capability of texture features obtained from the aneurysmatic thrombus CT images to discriminate different types of evolutions caused by endoleaks. The regions of interest (ROIs) from patients with different post-EVAR evolution were extracted by experienced radiologists. Three techniques were applied to each ROI to obtain texture parameters, namely the grey level co-occurrence matrix (GLCM), the grey level run length matrix (GLRLM) and the grey level difference method (GLDM). The results showed that GLCM, GLRLM and GLDM features presented a good discrimination ability to differentiate between favourable or unfavourable evolutions. GLCM was the most efficient in terms of classification accuracy (93.41% ± 0.024) followed by GLRLM (90.17% ± 0.077) and finally by GLDM (81.98% ± 0.045). According to the results, we can consider texture analysis as complementary information to classified abdominal aneurysm evolution after EVAR.

Study on Texture Features of Cingulum in Different Gender Patients with Alzheimer’s Disease and Mild Cognitive Impairment Based on MR Images

The aim of this article is to study the texture features of cingulum in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) based on magnetic resonance images, and explore the texture differences derived from different gender among each group. Texture analysis was performed on 7 AD patients, 14 MCI patients and 11 normal controls (NC). Texture features extracted from gray level co-occurrence matrix and run-length matrix were analyzed between each two groups. The results showed that texture features of the anterior cingulum had significant differences in the multiple comparisons and features of the posterior cingulum had significant differences between AD and MCI group as well as AD and NC group. There were significant differences between AD and MCI group as well as AD and NC group in male’s cingulum. While in female’s cingulaum, the differences were founded between AD and NC group. The results indicated that the pathological changes in cingulum could be reflected by texture features and the pathological changes may be different in the two genders.

Pattern Extraction from a Sequence Surrounding a Transcriptional Starting Site Using Texture Analysis: The Utility of Texture Analysis Based on the Gray Level Run Length Matrix

The computational methods to detect the coding regions of genes and specific sequences of the transcriptional control region using stochastic models such as the hidden Markov Model have been developed. But there are still aspects of the control of the gene expression based on a sequence of the transcriptional control region that have not been described. We report here our attempt to apply texture analysis to a sequence surrounding a transcriptional starting site to extract the features. We developed the features extraction program by employing texture analysis based on the gray level run length matrix and applied it to sequences surrounding transcriptional starting sites of genes coding the heat shock proteins. Thus we could successfully represent features extracted from a sequence surrounding transcriptional starting sites as specific patterns of them.

An Estimation of the Quality of Hairline Finished Surfaces by Applying Textural Analysis

Hairline finishing, which leaves a detailed continuous line pattern on the surface of a metal, is being used widely in industrial products for its ornamental and functional effects. However, the evaluation of their quality is done, in many cases, sensuously and subjectively though visual inspection by skilled workers because evaluation is based on geometrical continuity and regularity of the line pattern and other kinds of information. This research aims at developing a way to evaluate hairline finished surfaces objectively and quantitatively by the image processing. First, the pictures of hairline finished surfaces were taken using a CCD camera, and the textural analysis was carried out by estimating the gray level co-occurrence matrix and the gray level run length matrix. This textural analysis is effective because there was a clear difference in textural features between the pictures judged as good and the pictures judged as bad. In consideration of these results, the evaluation system using the neural network was developed.

CORRECTION EFFECTS OF MACHINE DEPENDENCY ON THE TEXTURAL PARAMETERS OF ULTRASOUND IMAGES

In this paper we presented the effect of correction of machine dependency such as LookUpTable (LUT) correction , Time-Gain-Control(TGC) correction, Diffraction and Focusing correction on the textural parameters of ultrasound images. The group of texture parameters are: histogram parameters , coocurrance matrix parameters , first order gradient parameters , grey level run length matrix parameters ,texture feature Descriptors (TFD parameters),acoustical parameters. Mapping this parameters into color coded display improve the visual inspection to the sonographers. After correction an increase of some textural parameters and decrease of others improve the visual inspection from the ultrasonograohers point of view. These results can improve the segmentation quality of the ultrasound images that will facilitate the build of 3D ultrasound images and can have an accurate measurement from the images and from the 3D data.