;Image segmentation plays an important role in the field of image processing and computer vision. The goal of image segmentation is to partition an image into semantic parts including foreground and background. There have been many methods for image segmentation such as histogram thresholding, region-growing, clustering, explicit active contours, and level set methods. Among them, level set methods (or implicit active contours) are shown to be a promising approach to perform the segmentation tasks. Level set methods belong to variational methods in which the segmentation solution is derived by minimizing an energy functional associated with the curve itself and features of image to segment. Although being a promising approach for image segmentation and contour-based object tracking, level set methods generally suffer from some drawbacks such as initial curve sensitivity and high computational cost. In addition, common models meet difficulties when dealing with images in the presence of clutter and occlusion.
This dissertation aims at developing level set models to handle challenges in segmentation of objects under occlusion and cluttered background as well as computational cost.Particularly, in the first model, to deal withimages in the presence of clutter and occlusion, we encoded the shape prior knowledge of desired object into energy functional which includes a data term and a shape term. The data term, inspired from the region-based approach, evolves the contour relied on the image intensity information. The shape prior term, defined as the distance between the evolving shape and a reference one, constrains the evolution of the contour with respect to the reference shape. To handle shape variability, we build the reference shape through Principal Component Analysis approach, and encode the shape term into a fuzzy energy formulation. Especially, in this model, to align the reference shape and the evolving one as well as the shapes in the training data, we employ moment invariant approach and shape normalization procedure that directly calculates the shape transformation, instead of using common gradient descent approach. In addition, to minimize the energy functional, we utilize a direct method to calculate the alteration of the energy.The proposed model therefore can deal with images with cluttered background and object occlusion in an effective way. Moreover, it also improves the computational speed, and avoids difficulties associated with time step selection issue in gradient descent based approaches.
In the second model, also for segmenting imageswith cluttered background and occluded objects, we employ kernel density estimation to estimate shapes’ features in the set of training shapes to construct the shape prior. Along with utilizing the moment invariant approach and shape normalization procedure to align the shapes, for a fast convergence, we represent the level set functions as linear combinations of continuous basic function expressed on B-spline basics. Moreover, we also extend the model into multiphase formulation case and apply the extended version to segment and track the left ventricle from cardiac MR images. The model therefore reveals some advantages, such as avoiding shortcomings associated with solving a set of partial differential equations as in the conventional shape prior-based models. Moreover, the proposed model reduces computation time and fast converges to segmentation solutions.
In sum, this dissertation presents two supervised level set models to perform image segmentation tasks. In the first model, the shape prior knowledge of the desired objects is encoded into energy functional via performing principal component analysis on shapes in the given training data. The alignment between the shapes is achieved by utilizing moment invariant approach and shape normalization procedure. The segmentation solution is derived by directly calculating the changes in value of energy functional. In the second model, the shape prior term is constructed by employing kernel density estimation on the given training shapes. The level set functions are represented by a linear combination of continuous functions expressed on B-spline basic function. The proposed models are applied to segment a vast number of images and experimental results show the desired performances of our proposed models.