This study proposed an approach to retrieve images based on texture features using GLCM and image subblocks. Each image is divided into three rows and three columns with equal sizes. Texture features are extracted based on GLCM (Gray Level Co-occurrence Matrix) using four statistical features that is contrast, homogeneity, energy and correlation. The features are calculated in four directions (...

-The image mining technique deals with the extraction of implicit knowledge and image with data relationship or other patterns not explicitly stored in the images. It is an extension of data mining to image domain. The main objective of this paper is to apply image mining in the domain such as breast mammograms to classify and detect the cancerous tissue. Mammogram image can be classified into ...

Computer aided diagnosis systems provide vital opinion to radiologists in the detection of early signs of breast cancer from mammogram images. Architectural distortions, masses and microcalcifications are the major abnormalities. In this paper, a computer aided diagnosis system has been proposed for distinguishing abnormal mammograms with architectural distortion from normal mammogram. Four typ...

This paper presents a novel content based image retrieval (CBIR) system based on Framelet Transform combined with gray level co-occurrence matrix (GLCM).The proposed method is shift invariant which captured edge information more accurately than conventional transform domain methods as well as able to handle images of arbitrary size. Current system uses texture as a visual content for feature ex...

This paper investigates on extending and comparing the Gray level co-occurrence matrices (GLCM) and 3D Gabor filters in volumetric texture analysis of brain tumor tissue classification. The extracted features are sub-selected by genetic algorithm for dimensionality reduction and fed into Extreme Learning Machine Classifier. The organizational prototype of image voxels distinctive to the underly...

In this paper, we propose a new image segmentation World Academy of Science, Engineering and Technology International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol:5, No:5, 2011 427 International Scholarly and Scientific Research & Innovation 5(5) 2011 scholar.waset.org/1999.4/6017 In te rn at io na l S ci en ce I nd ex , C om pu te r an d In fo rm at io n...