13 March 2013 An automatic tumor segmentation framework of cervical cancer in T2-weighted and diffusion weighted magnetic resonance images
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Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86693A (2013) https://doi.org/10.1117/12.2006190
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
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.
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Yueying Kao, Wu Li, Huadan Xue, Cui Ren, Jie Tian, "An automatic tumor segmentation framework of cervical cancer in T2-weighted and diffusion weighted magnetic resonance images", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86693A (13 March 2013); doi: 10.1117/12.2006190; https://doi.org/10.1117/12.2006190
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