IMAGE SEGMENTATION USING ACTIVE CONTOUR MODEL

Abstract

Active contours are curves that deform within digital images to recover object shapes. They are classified as either parametric active contours or geometric active contours according to their representation and implementation. Active models have been widely used in image processing applications. The first model introduces a method for geometric active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah functional for segmentation and level sets. This model can detect objects whose boundaries are not necessarily defined by gradient. We minimize an energy which can be seen as a particular case of the minimal partition problem. In the level set formulation, the problem becomes evolving the active contour, which will stop on the desired boundary. However, the stopping term does not depend on the gradient of the image, as in the classical active contour models, but is instead related to a particular segmentation of the image. We will give a numerical algorithm using finite differences. The second model defined in this dissertation proposes a novel automatic initialization approach for parametric active models. A crucial stage that affects the ultimate active model performance is initialization. The Poisson inverse gradient (PIG) initialization method exploits a novel technique that essentially estimates the external energy field from the external force field and determines the most likely initial segmentation. the advantages of this innovation, including the ability to choose the number of active models deployed, rapid convergence, accommodation of broken edges, superior noise robustness, and segmentation accuracy.