Gray level co-occurrence matrix in polar orientation

IJEER, 2016

Detection of brain tumour is very important current scenario of the health care society. Image processing techniques are used to extract the abnormal tumour portion and other features in the brain. Brain tumor is an abnormal mass of lesson in which cells grow up and multiply uncontrollably, apparently unregulated by the mechanisms that control cells. Several techniques like Segmentation, morphological have been developed for detection of tumor in the brain. Texture is a critical feature of several image types and textural features have a lot of application in image processing, content-based image retrieval and so on. There are several ways of extracting these features and the most common way is by using a gray-level co-occurrence matrix (GLCM). In our proposed work Texture characterisation has been made to obtain the Haralick features and SVM classifier is used in the Texture classification algorithm which used in detecting the brain tumor. This technique has been tested for 45 images, true positives are 33, True negative is 1, false positive is 1, and True negatives are 10. Sensitivity 97.0%, Specificity 90.9%, Precision or Positive Predictive Value (PPV) 97.0%,Negative Predictive Value (NPV)90.9%, Accuracy 95.0%.

Clinical Radiology, 2004

The analysis of texture parameters is a useful way of increasing the information obtainable from medical images. It is an ongoing field of research, with applications ranging from the segmentation of specific anatomical structures and the detection of lesions, to differentiation between pathological and healthy tissue in different organs. Texture analysis uses radiological images obtained in routine diagnostic practice, but involves an ensemble of mathematical computations performed with the data contained within the images. In this article we clarify the principles of texture analysis and give examples of its applications, reviewing studies of the technique.

In this paper, we will focus on the Spatial Gray Level Dependence Matrices SGLDM to extract the Haralick's texture features of the ultrasound breast lesions. This method relies on the manual selection of the region of interest, which results in the dependence of parameters values on the extracted region. For that reason, an improved Spatial Gray Level Dependence Matrices based on the segmented masses using active contour was applied. This method outperforms the existing SGLDM method because it allows establishing a well determined threshold for the classification of lesions.

— Comparative analysis of nine textural feature measures derived from gray-level co-occurrence matrix obtained from the region(s) of interest (ROI) among the normal and abnormal anatomical structures that appear in the patient's ultrasound liver images is presented in this paper. Selection of the most robust discriminating features for classification experiment is performed through analysis of each feature classes' separability power. The results analysis shows that cluster prominence, cluster shade, maximum probability, and entropy have high classes' separability power and were selected for the classification of liver ultrasound images into normal liver (NL), primary liver cell carcinoma (PLCC) and hepatocellular carcinoma (HCC) at 0.4, 0.4, 0.2 and 0.6 sensitivity respectively.

International Journal of Engineering Research and Technology (IJERT), 2012

https://www.ijert.org/persian-signature-verification-using-convolutional-neural-networks https://www.ijert.org/research/persian-signature-verification-using-convolutional-neural-networks-IJERTV1IS2001.pdf The objective of this paper is to recognize different textures in an image, particularly a satellite image where properties of the image are not distinctly identified. Texture classification involves determining texture category of an observed image. The present study on Image Processing & Texture Classification was undertaken with a view to develop a comparative study about the texture classification methods. The algorithms implemented herein classify the different parts of the image into distinct classes, each representing one property, which is different from the other parts of the image. The aim is to produce a classification map of input image where each uniform textured region is identified with its respective texture class. The classification is done on the basis of texture of the image, which remains same throughout a region, which has a consistent property. The classified areas can be assigned different colours, each representing one texture of the image. In order to accomplish this, prior knowledge of the classes to be recognized is needed, texture features extracted and then classical pattern classification techniques are used to do the classification. Examples where texture classification was applied as the appropriate texture processing method include the classification of regions in satellite images into categories of land use. Here we have implemented two methods namely-Cross Diagonal Texture Matrix (CDTM) and Grey-Level Co-occurrence Matrix (GLCM), which are based on properties of texture spectrum (TS) domain for the satellite images. In CDTM, the texture unit is split into two separable texture units, namely, Cross texture unit and Diagonal texture unit of four elements each. These four elements of each texture unit occur along the cross direction and diagonal direction. For each pixel, CDTM has been evaluated using various types of combinations of cross and diagonal texture units. GLCM, on the other hand, is a tabulation of occurrence of different combinations of pixel brightness values (grey levels) in an image. Basically, the GLCM expresses the spatial relationship between a gray-level in a pixel with the gray-level in the neighboring pixels. The study focuses on extraction of entropy, energy, inertia and correlation features using several window sizes, which are calculated, based on the GLCM. A maximum likelihood supervised classifier is used for classification. While applying the algorithms on the images, we characterize our processed image by its texture spectrum. In this paper we deal with extraction of micro texture unit of 7X7 window to represent the local texture unit information of a given pixel and its neighborhood. The result shows that increasing the window size showed no significant contribution in improving the classification accuracy. In addition, results also indicate that the window size of 7x7 pixels is the optimal window size for classification. The texture features of a GLCM and CDTM have been used for comparison in discriminating natural texture images in experiments based on minimum distance. Experimental results reveal that the features of the GLCM are superior to the ones given by CDTM method for texture classification.

Engineering in Medicine …, 1995

2012

Automatic recognition system for medical images is challenging task in the field of medical image processing. Medical images acquired from different modalities such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), etc which are used for the diagnosis purpose. In the medical field, brain tumor classification is very important phase for the further treatment. Human interpretation of large number of MRI slices (Normal or Abnormal) may leads to misclassification hence there is need of such a automated recognition system, which can classify the type of the brain tumor. In this research work, we used four different classes of brain tumors and extracted the GLCM based textural features of each class, and applied to twolayered Feed forward Neural Network, which gives 97.5% classification rate.

In this paper, a novel texture classification system based on Gray Level Co-occurrence Matrix (GLCM) is presented. The texture classification is achieved by extracting the spatial relationship of pixel in the GLCM. In the proposed method, GLCM is calculated from the original texture image and the differences calculated along the first non singleton dimension of the input texture image. Then the statistical features contrast, correlation, energy and homogeneity are calculated from both the GLCM. The extracted features are used as an input to the K Nearest Neighbor (K-NN) for classification. The performance of the proposed system is evaluated by using Brodatz database and compared with the methods PSWT, TSWT, the Gabor transform, and Linear Regression Model. Experimental results show that the proposed method produces more accurate classification rate of over 100%. Index Terms: Texture Classification, K nearest neighbor, gray level co-occurrence matrix, Brodatz album.

International Journal of Biomedical and Clinical Engineering, 2020

Breast cancer is one among the most common cancers in women. The early detection of breast cancer reduces the risk of death. Mammograms are an efficient breast imaging technique for breast cancer screening. Computer aided diagnosis (CAD) systems reduce manual errors and helps radiologists to analyze the mammogram images. The mammogram images are typically in two views, cranial-caudal (CC) and medio lateral oblique (MLO) views. MLO contains pectoral muscles (chest muscles) at the upper right or left corner of the image. In this study, it was removed by using a semi-automated method. All the normal and abnormal images were filtered and enhanced to improve the quality. GLCM (Gray Level Co-occurrence Matrix) texture features were extracted and analyzed by changing the number of features in a feature set. Linear Support Vector Machine (LSVM) was used as classifier. The classification accuracy was improved as the number of features in GLCM feature set increases. Simulation results show an...

First Canadian Conference on Computer and Robot Vision, 2004. Proceedings., 2004

The discrimination of textures is a significant aspect in segmenting SAR sea ice imagery. Texture features calculated from grey level co-occurring probabilities (GLCP) are well accepted and applied in the analysis of many images. When calculating GLCPs, each co-occurring pixel pair within the image window is given a uniform weighting. Although a novel technique, co-occurring texture features have a tendency to misclassify and erode texture boundaries due to the large window sizes needed to capture meaningful statistics.