Development of Adaptive Active Contour Models for Medical Image Segmentation

Abstract

Active Contour Models (ACMs) are increasingly becoming a vital component of advanced newlinetools in medical image segmentation, particularly for accurately delineating complex regions newlineof interest (ROI) bounded by weak edges. Despite the continuous development, ACMs often newlineface challenges such as dependency on manual initialization, premature convergence due to newlineintensity inhomogeneity, and leakage through weak edges. This thesis introduces a series of newlinenovel methodologies to address these limitations, enhancing the robustness and accuracy of newlineACMs in medical image segmentation tasks. newlineThe first method focuses on eliminating the dependency on manual contour initialization newlinein ACMs by introducing an automated approach tailored for MR brain images. This newlinemethod bypasses the need for an exclusive brain extraction tool. It also incorporates a newlinediscrete level-set evolution scheme with a variable time-step to mitigate leakage issues. The newlinesecond method dynamically adjusts the energy weights of the ACM based on a probability newlinemeasure that estimates boundary-relevant edges. This allows the ACM to overcome false newlineconvergence for an improved segmentation accuracy. Its adaptive time-step management newlinefurther enhances its robustness against relative feature variability across various images. newlineThe third method dynamically updates the weights of the internal energy terms based on newlinelocal image statistics, while maintaining unity priority weights for the external image-fitting newlineterms. The integration of an adaptive time-step scheme in it promotes a leakage-free newlineconvergence. Finally, a spatially adaptive weighted ACM is developed, which is devoid newlineof any exclusive image-fitting term. It utilizes the Hellinger distances between the reference newlineand local intensity distributions to dynamically adjust the energy weights. The ACM newlineeffectively addresses intensity inhomogeneity, while its performance is independent of newlineweight initialization. The notable contribution of this work is the incorporation of a newlinespecialized weighting factor that plays a pivotal role in