Statistical Snake Model And Its Application In Medical Image Segmentation

Image processing and computer vision have become important within the past two decades. Image segmentation, or identification of the boundaries of objects in images, is one of the most important problems in computer vision and image processing. The applications of segmentation techniques range from medicine (e.g., locating a lesion) to industry (e.g., robotic vision) and the military (e.g., target detection). With medical imaging playing an increasingly prominent role in the diagnosis and treatment of disease, segmentation techniques ,have been applied for extracting clinically useful information.In this article, an image segmentation algorithm which is based on active region model technique is proposed . This is a region enlarging model based on pressure snake. The region energy generates a pressure force that makes the model expand and contract to fit a homogeneous region. At the same time, it can combine with all the features of multiple images. Compared with the traditional balloon model, active region model is smoother. Using a multidimensional linear goodness function to deal with the R, G, B accordingly, better effects will be got than active contour model. This algorithm overcomes some mistakes of active model, such as the oscillation phenomena; too slow to close the character. Experiments on real-world images show the robustness and good performance of the method.