Image segmentation is an important processing step in many image, video and computer vision applications especially in medical field. Extensive research has been done in creating many different approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm produces more accurate segmentations than the other. Up to date, the most common method for evaluating the effectiveness of a segmentation method is subjective evaluation, in which a human visually compares the image segmentation results for separate segmentation algorithms, which is a tedious process. The use of computer-aided diagnosis (CAD) systems to improve the sensitivity and specificity of tumor detection has become a focus of medical imaging and diagnostic radiology research. Accurate segmentation of medical images is an important step in contouring during radiotherapy planning. This paper provides new hybrid medical image segmentation method based on Watershed and Wavelet Transform. In this paper, we propose an algorithm for segmentation problems in medical imaging modalities especially for brain Magnetic Resonance Images MRI). The watershed transformation is a useful morphological segmentation tool used for a variety of grey-scale images. The segmentation procedure consists of pyramid representation, image segmentation, region merging and region projection. Each layer is split into a number of regions by rooting, labeling technique and the boundary is extracted by threshold and the image is smoothened by wavelet transform. Experimental results prove that the proposed algorithm is comparatively better than the existing systems .

Computer-Aided Diagnosis Magnetic Resonance Image Contouring Watershed Transformation Wavelet Transform Grey-scale Image Pyramidal Image Segmentation