Image segmentation is typically used to locate objects and boundaries in images. The objective of this application is the segmentation of different medical images. Edge detection significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. Medical image segmentation is one of the most important parts of clinical diagnostic tools. However, the process of accurate segmentation of these images is very important and crucial for a correct diagnosis by clinical tools. In this paper canny operator based Distance Regularization Level Set Evolution Algorithm (DRLSE) is used. The distance regularization effect eliminates the need for reinitialization and thereby avoids its induced numerical errors. First canny operator is used to determine the edges and edge directions. Then a new variation level set formulation is used in which the regularity of the level set function is intrinsically maintained during the level set evolution. The level set evolution is derived as the gradient flow that minimizes energy functional with a distance regularization term and an external energy that drives the motion of the zero level set toward desired locations. The distance regularization term is defined with a potential function such that the derived level set evolution has a unique forward-and-backward (FAB) diffusion effect, which is able to maintain a desired shape of the level set function, particularly a signed distance profile near the zero level set. Proposed algorithm is used for database of different medical images such as Brain, Eye & Comparison of the same algorithm for those images by using different parameters is done.

Canny operator Distance Regularization Level Set Evolution Algorithm (DRLSE)