Segmenting MR Images by Level-Set Algorithms for Perspective Colorectal Cancer Diagnosis

Segmentation is an essential and crucial step in interpreting medical images for possible treatment. Medical image segmentation is very chaotic procedure as medical image may have different structures of same organ in different image modalities and may also have different features in different image slices of same modality. In this work, we present a comparison of segmentation algorithms based on level set methods, viz. Caselles, Chan & Vese, Li, Lankton, Bernard, and Shi algorithms. We assessed these algorithms with our T2-weighted colorectal MR images using Dice coefficient that measures the similarity between the reference sketched by specialist and the segmentation result produced by each algorithm. In addition, computational time taken by each algorithm to perform the segmentation is also computed. Our results on average Dice coefficient and average time computation demonstrate that Bernard has the lowest average Dice coefficient and the highest computational complexity followed by Li which has second lowest Dice coefficient and highest computational complexity. Lankton has achieved satisfactory results on average Dice coefficient and computational complexity followed by Chan & Vese and Shi. Whereas, Caselles algorithm outperforms than all with respect to average Dice coefficient and computational time.