Image segmentation techniques in medical sciences

Classical and clustering techniques for image segmentation are important tools in medical sciences. Classical techniques include histogram, region growing, watershed, and contour. The more recent clustering techniques include standard fuzzy c-means clustering, kernelized c-means, spatial constrained fuzzy c-means, and k-means clustering. These methods are applied on different images, synthetic image, T1-weighted MR phantom, and real MR slices, which the performance of them are compared. The comparison is based on estimating the segmentation accuracy and time for each method when applied on three test images: synthetic image, T1-weighted MR phantom, and real MR slices.