Automatic Segmentation of Breast MR Images Through a Markov Random Field Statistical Model

• Published in: IEEE transactions on medical imaging 2014-10, Vol.33 (10), p.1986-1996
• Main Authors: Ribes, S., Didierlaurent, D., Decoster, N., Gonneau, E., Risser, L., Feillel, V., Caselles, O.
• Format: Article
• Language: English
• Subjects: Algorithms; Automatic segmentation; Automation; Breast; Breast - anatomy & histology; Breast cancer; Clustering algorithms; Coefficients; Computer Science; Databases, Factual; denoising; Engineering Sciences; Estimation; Female; Fields (mathematics); Humans; Image Processing, Computer-Assisted - methods; Image segmentation; Imaging; Machine Learning; magnetic resonance imaging (MRI); Magnetic Resonance Imaging - methods; Markov Chains; Markov models; Markov random fields; Mathematics; Medical Imaging; Models, Statistical; NMR; Noise; Noise reduction; Nuclear magnetic resonance; Segmentation; Signal and Image processing; Statistics
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2014.2329019

An algorithm dedicated to automatic segmentation of breast magnetic resonance images is presented in this paper. Our approach is based on a pipeline that includes a denoising step and statistical segmentation. The noise removal preprocessing relies on an anisotropic diffusion scheme, whereas the statistical segmentation is conducted through a Markov random field model. The continuous updating of all parameters governing the diffusion process enables automatic denoising, and the partial volume effect is also addressed during the labeling step. To assess the relevance, the Jaccard similarity coefficient was computed. Experiments were conducted on synthetic data and breast magnetic resonance images extracted from a high-risk population. The relevance of the approach for the dataset is highlighted, and we demonstrate accuracy superior to that of traditional clustering algorithms. The results emphasize the benefits of both denoising guided by input data and the inclusion of spatial dependency through a Markov random field. For example, the Jaccard coefficient for the clinical data was increased by 114%, 109%, and 140% with respect to a K-means algorithm and, respectively, for the adipose, glandular and muscle and skin components. Moreover, the agreement between the manual segmentations provided by an experienced radiologist and the automatic segmentations performed with this algorithm was good, with Jaccard coefficients equal to 0.769, 0.756, and 0.694 for the above-mentioned classes.