Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
A Variational Framework for Active and Adaptative Segmentation of Vector Valued Images
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
International Journal of Computer Vision
IEEE Transactions on Image Processing
Histology image analysis for carcinoma detection and grading
Computer Methods and Programs in Biomedicine
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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 & Vese's (CV) and Rousson and Deriche's (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.