A Simple Approach to Analyze and Evaluate Image Segmentation Methods Based on Image Quality Metrics

The role of image segmentation is inevitable in image analysis. There are many algorithms and methods for image segmentation. However it is tricky to choose best possible clustering procedure. The proper selection of clustering method leads to the successful outcome in image analysis. In segmentation process, data is fragmented into some reasonable regions (clusters). Elements (image pixels) in a particular cluster should be very analogous w.r.t some attributes like lightness, texture or color. In the proposed method, test image is clustered based on chrominance (color) using four segmentation methods, viz., FCM, PCM, PFCM and MFCM. The outcome of segmentation methods are transformed into binary image using otsu threshold method. The binary image of FCM is taken as a reference image because it is standard and fundamental one. The binary images of other methods are compared with reference image. The comparison is done using some image quality metrics such as dice, jaccard. The optimal segmentation method is decided based on the analysis of these image quality metrics.