Performance Assessment of Active Contour Models: for image segmentation

Image segmentation is one of the significant techniques in image processing to distinguish desired parts from its background for further analysis. It provides visual means for inspection of anatomical structure of human body, identification of disease, tracking of its development and input for surgical planning and simulation. Active contour models are regarded as promising and vigorously research model-based approach to computer assisted medical image analysis. However, it is not trivial to assess whether one segmentation algorithm performs more superior than the other. Therefore, a systematic assessment tool is designed and implemented to examine all the important aspects of active contour models. Meanwhile, a novel supervised evaluator including analytical method and empirical methods are proposed to acts as objective evaluator. The obtained results highlighted both the strengths and limitations of the studied active contour models.

is a present researcher in the field of biomedical engineering. He is working as a research member in Centre for Biomedical Engineering in Universiti Teknologi Malaysia(UTM). His research interests are Digital X-Ray imaging, medical image processing, artificial intelligence, fuzzy logic, and medical computing and performance optimization.