An efficient clustering based texture feature extraction for medical image

In some medical applications where a tissue of interest covers a large fraction of the image or a prior knowledge on the region of interest is available, extracting features by fixed blocs in the image is sufficient. However in the general case, one would like to identify features for each tissue in the image. This would require prior image segmentation. Medical image segmentation is one of the most challenging problems in medical image analysis and a very active research topic. Therefore, there is no algorithm available in the general case for isolating medical image regions. This paper presents an accurate method for extracting texture features from medical image for classification. It is based on bloc wise clustering of medical images. The proposed technique extracts accurate and general set of texural features. Experimental result showed the high accuracy of the extracted textural features. Experiments held on mammographic image analysis society (MIAS) dataset.