Publication

Abstract : Medical image is the visual representation of anatomy or physiology of internal structures of the body and it is useful for clinical analysis and medical intervention. Modern medical imaging devices provide an excellent view of anatomy and physiology of internal structures of the body non-invasively. However, the usage of computers to measure, examine and determine the state of internal structures of the body with accuracy and efficiency is limited. Automated medical image segmentation techniques have wide range of utility in diagnosis, treatment planning and computer integrated surgery. These automated medical image segmentation techniques could also be used as an assisting tool to radiologists by saving their time in selecting, measuring and classifying various findings. However, automated medical image segmentation is challenging because the quality of the image is low due to the presence of noise, artefacts, partial volume effects etc., low contrast between different structures in an image and intensity variations within a region itself. This research paper focuses on fastening a region based deformable model called Chan-Vese model through various first order optimization techniques. Chan - Vese model can perform segmentation effectively even in low quality images but the limitation of Chan-Vese model is that convergence towards optimal solution is slow. The objective of this work is to fasten the convergence of Chan-Vese model towards optimal solution by using various first order optimization schemes. Chan-Vese model with proposed optimization techniques is tested with X-ray, CT and MRI images of different organs. Comparative study between traditional optimization technique used in Chan-Vese model and proposed optimization techniques has been carried out. From the comparative study, it is found that Chan-Vese model with proposed optimization schemes is efficient in terms of speedy delineation with less number of iterations and processing time. Therefore, this fastened Chan-Vese model is better suited algorithm for fast image segmentation needs such as tracking of region of interest in subsequent frames in a video.