Nonlinear active contour model for medical image segmentation / Norshaliza Kamaruddin

• Main Author: Norshaliza, Kamaruddin
• Format: Thesis
• Published: 2016
• Subjects: Q Science (General)
• Online Access: http://studentsrepo.um.edu.my/6576/4/norshaliza.pdf; http://studentsrepo.um.edu.my/6576/
• Institution: Universiti Malaya

With the introduction of fractional calculus, this study proposes two automatic segmentation methods which are based on nonlinear Active Contour Model (ACM) for medical image segmentation. Before that, a semi-automated approach is developed which is based on Mathematical Morphology function to overcome the gap problems. Medical images are classified as having low in quality due to its level of noise and level of intensity inhomogeneity. These characteristics of medical images create problems of over segmentation and local minima during the segmentation process that leads to inaccurate segmentation. Therefore the study proposes two automated methods to overcome those problems in providing successful medical image segmentation. The first proposed method is designed using the collaboration of fractional function and sinc method. Our first method, Fractional Sinc Wave method (FSW) ACM, managed to reduce the over segmentation problem thus provide successful segmentation. The fractional function provides rapid, dynamic and bending effect capability to the contour to evolve towards the object. On the contrary, the sinc wave method with the interpolation capability, support the fractional calculus in constructing new data points within the current data points. The method shows good potential in providing an improved segmenting where the over segmentation problem is reduced However, the method did not managed to provide accurate boundary segmentation on some of the medical images. This problem is then overcome by our second method namely Fractional Gaussian Heaviside (FGH) ACM. We introduce two importance techniques which are Adaptive Fractional Gaussian Kernel (AFGK) and Fractional Differential Heaviside (FDH). The introduction of Adaptive Fractional Gaussian Kernel (AFGK), offers an excellent enhancement process where the iv inhomogeneous objects in regions are now more accurately classified. The proposed Fractional Differential Heaviside (FDH) provides the nonlinear protecting capability and produce extraction of accurate local image information. The collaboration of AFGK and FDH via ACM produces a method that provides accurate boundary segmentation on four different medical image modalities. In order to access accuracy of segmentation on medical images, two types of evaluations were conducted. The first evaluation is based on quantitative evaluation where the metric of accuracy is stressed on. It was found that, the metric of accuracy for all images used in the experiments were more than 90%. The second evaluation is based on visual interpretation where the FSW ACM and FGH ACM were compared to other methods of ACM. It is noted that the accuracy produced by both methods are better than others.