Segmentation For Different Image Modality
In this paper we presented comparative methods for image segmentation. The Segmentation of different modality images is an important step in forming realistic tissue models. Current segmentation approaches are reviewed with an emphasis placed on revealing the advantages and disadvantages of these methods for medical imaging applications. To assist in classifying the relevant literature, there many methods for image segmentation image, we used a the method witch based region segmentation. Segmentation of medical images is an important step in forming realistic tissue models. Segmentation of the image is an image processing operation, particularly in the medical field. Diagnostic imaging is an invaluable tool in medicine today. Magnetic resonance imaging (MRI), computed tomography (CT), digital mammography, and other imaging modalities provide an effective means for noninvasively mapping the anatomy of a subject. The segmentation of medical images is of paramount importance in the diagnosis and detection of various pathologies. We present in this paper a comparative study of segmentation methods by region such Fuzzy C-Means, K-Means, Meanshift and EM, where the results obtained are evaluated by three criteria: IntraInter_LN, Intra_LN, CritAtt, we used medical images base and x-ray image image Ultra Sound. The diversity of segmentation methods offers us several ways to segment the image. Always look for the EM method to get good results.
Keywords - Image Segmentation, Modality of Image, Criteria for evaluation.