Medical Image Segmentation Based on Edge Detection Techniques

In  this  article  a  new  combination  of  image  segmentation  techniques  including  K-means clustering,  watershed  transform,  region  merging  and  growing  algorithm  was  proposed  to segment computed tomography(CT) and magnetic resonance(MR) medical images. The first stage in the proposed system is "preprocessing" for required  image enhancement, cropped, and convert the images into .mat or png ...etc image file formats then the image will be segmented using combination    methods (clustering , region growing, and watershed, thresholding).  Some  initial  over-segmentation  appears  due  to  the  high  sensitivity  of  the watershed algorithm to  the gradient image intensity variations. Here,  K- means  and region growing with correct thresholding value are used to overcome that over segmentations. in our system the number of pixels of segmented area is calculated which is very important for medical image analysis for diseases or medicine effects on affected area of human body. also displaying the edge map. The results show that using clustering method output to region growing as input image, gives accurate and very good results compare with watershed technique which depends on gradient of input image, the mean and the threshold values which are chosen manually. Also the results show that the manual selection of the threshold value for the watershed is not as good as automatically selecting, where data misses may be happen.