Cervical cancer is one of the common malignant tumors and is a major health threat for women. The accurate
segmentation of the cervical cancer is of important clinical significant for prevention, diagnosis and treatment of cervical
cancer. Due to the complexity of the structure of human abdomen, the images in a single imaging modality T2-weighted
MR images can not sufficiently show the precise information of the cervical cancer. In this paper, we present an
automatic segmentation framework of cervical cancer, making use of the information provided by both T2-weighted
magnetic resonance (MR) images and diffusion weighted magnetic resonance (DW-MR) images of cervical cancer. This
framework consists of the following steps. Firstly, the DW-MR images are registered to T2-weighted MR images using
mutual information method; then classification operation is executed in the registered DW-MR images to localize the
tumor. Secondly, T2-weighted MR images are filtered by P-M nonlinear anisotropic diffusion filtering technique; and
then bladder and rectum are segmented and excluded, so the Region of Interest (ROI) containing tumor is extracted.
Finally, the tumor is accurately segmented by Confederative Maximum a Posterior (CMAP) algorithm combining with
the results of T2-weighted MR images and DW-MR images. We tested this framework on 5 different cervical cancer
patients. Compared with the results outlined manually by the experienced radiologists, it is demonstrated effectiveness of
our proposed segmentation framework.