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Image segmentation is an essential component in many image processing andcomputer vision tasks. The primary goal of image segmentation is to simplify animage for easier analysis, and there are two broad approaches for achievingthis: edge based methods, which extract the boundaries of specific knownobjects, and region based methods, which partition the image into regions thatare statistically homogeneous. One of the more prominent edge finding methods,known as the level set method, evolves a zero-level contour in the image planewith gradient descent until the contour has converged to the object boundaries.While the classical level set method and its variants have proved successful insegmenting real images, they are susceptible to becoming stuck in noisy regionsof the image plane without a priori knowledge of the image and they are unableto provide details beyond object outer boundary locations. We propose amodification to the variational level set image segmentation method that canquickly detect object boundaries by making use of random point initialization.We demonstrate the efficacy of our approach by comparing the performance of ourmethod on real images to that of the prominent Canny Method.

A Random Point Initialization Approach to Image Segmentation with Variational Level-sets