Telemedicine is growing and there is an increased demand for faster image processing and transmitting diagnostic medical images. A region is a popular technique for image segmentation. We introduce a new approach that overcomes the close boundary initialization problem by reformulating the external energy term. We treat the contour as a mean curve of the probability density function. A widely used approach to image segmentation is to define corresponding segmentation energies and to compute shapes that are minimizes of these energies. In many medical image segmentation applications identifying and extracting the region of interest (ROI) accurately is an important step .We present a new image segmentation process, which can segment images with different image intensity distributions efficiently. To accomplish this, we construct a function that is evaluated along the evolving curve. In this cost, the value at each point on the curve is based on the analysis of interior and exterior means in a local neighborhood around that point.