Comparative analysis of edge detection techniques for medical images of different body parts

Abstract

Medical images are arduous to process since they possess distinct modalities. Therefore, the medical practitioners cannot competently detect and diagnosis the diseases in conventional ways. There should be a system which helps physicians to understand medical images very easily. Image segmentation using edge detection is commonly used for image analysis and better visualization of medical images. Various methods have been used for image segmentation such as Threshold detection, Region detection, Edge detection and Clustering technique. Edge detection is one of the prominently used methods for segmentation. This technique focuses on identifying and analyzing the entire image based upon the detected edges. In this paper, MRI images of human body parts such as abdomen, ankle, elbow, hand, knee, leg, liver and brain are considered for edge detection. Further, filtering has been performed on the segmented images to remove the unwanted noise. This makes the image more clearly for further reference. The effectiveness of the proposed technique has been evaluated quantitatively by using the performance measures like Entropy and Standard Deviation. The proposed technique may be highly beneficial for medical practitioners to carry out the diagnosis for effective treatment.