Image segmentation based on active contours without edges

There are a lot of image segmentation techniques that try to differentiate between background and object pixels, but many of them fail to discriminate between different objects that are close to each other. Some image characteristics like low contrast between background and foreground or inhomogeneity within the objects increase the difficulty of correctly segmenting images. We designed a new segmentation algorithm based on active contours without edges. We also used other image processing techniques such as nonlinear anisotropic diffusion and adaptive thresholding in order to overcome the images' problems stated above. Our algorithm was tested on very noisy images, and the results were compared to those obtained with known methods, like segmentation using active contours without edges and graph cuts. The new technique led to very good results, but the time complexity was a drawback. However, this drawback was significantly reduced with the use of graphical programming. Our segmentation method has been successfully integrated in a software application whose aim is to segment the bones from CT datasets, extract the femur and produce personalized prostheses in hip arthroplasty.