A Hybrid Approach for Classification of DICOM Image

Image classification is a most important step for image analysis. As the same in medical area especially for diagnosing the disease of the patient, classification plays a great role for the doctors to treat the patient according to the severeness of the diseases. In case of DICOM images it is very tough for optimal identification and early detection of diseases. Classification is a computational procedure that separates the images into groups according to their features that extracted. DICOM is latest medical imaging technology. DICOM is used for brain scans and it is very useful and effective technique to detect the dissimilarity in brain images. In this paper a hybrid approach is proposed for DICOM image classification. The approach consists of feature extraction and classification. The classification consists of Multi Linear Discriminent Analysis (MLDA) and Support Vector Machine (SVM). Classification is done on the base of parameter extracted by Gray Level Co-occurrence Matrix (GLCM) and histogram texture feature extraction method. The feature is selected using fuzzy rough set and Genetic Algorithm (GA). The proposed approach has high approximation capability and much faster convergence. KeywordsClassification; Linear Discriminent Analysis (LDA); Support Vector Machine (SVM); GA, Fuzzy Rough set; GLCM; Histogram Texture feature.