Automatic Segmentation of Rat Mammary Glands from Serial MRI Images

Abstract

A novel framework for automatic segmentation of rat mammary glands in MRI image sequences is presented in this paper. The Cartoon-Texture model is utilized in serial image segmentation to decompose the image into cartoon image and texture image. Then two-phase direct energy segmentation based on Chan-Vese active contour model is implemented on the cartoon image to partition the image into a set of regions. Seeds searching technology is applied iteratively on the texture image to find valid seeds for extracting the whole gland boundary points from the generated regions by a tracing algorithm we proposed. In iteration every time, texture images and features of the image patch around the seed are updated for new seeds searching and segmentation. Our segmentation approach does not require that the number of glands be identical or the location of the glands be close among consecutive images. Experiments show that our method is effective and efficient.