Markov Random Field driven Region-Based Active Contour Model (MaRACel): Application to Medical Image Segmentation
(0) In this paper we present a Markov random field (MRF) driven region-based active contour model (MaRACel) for medical image segmentation. State-of-the-art region-based active contour (RAC) models assume that every spatial location in the image is statistically independent of the others, thereby ignoring valuable contextual information. To address this shortcoming we incorporate a MRF prior into the AC model, further generalizing Chan & Veses (CV) and Rousson and Deriches (RD) AC models. This incorporation requires a Markov prior that is consistent with the continuous variational framework characteristic of active contours; consequently, we introduce a continuous analogue to the discrete Potts model. To demonstrate the effectiveness of MaRACel, we compare its performance to those of the CV and RD AC models in the following scenarios: (1) the qualitative segmentation of a cancerous lesion in a breast DCE-MR image and (2) the qualitative and quantitative segmentations of prostatic acini (glands) in 200 histopathology images. Across the 200 prostate needle core biopsy histology images, MaRACel yielded an average sensitivity, specificity, and positive predictive value of 71％, 95％, 74％ with respect to the segmented gland boundaries; the CV and RD models have corresponding values of 19％, 81％, 20％ and 53％, 88％, 56％, respectively
Segmentation medical imaging prostate cancer breast cancer MRI digital pathology
Jun Xu James P.Monaco Anant Madabhushi
Department of Biomedical Engineering, Rutgers University, USA
国际会议
北京
英文
197–204
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）