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2009
IEEE

Lesion Detection and Segmentation in Uterine Cervix Images Using an Arc-Level MRF

12 years 13 hour ago
Lesion Detection and Segmentation in Uterine Cervix Images Using an Arc-Level MRF
This study develops a procedure for automatic extraction and segmentation of a class-specific object (or region) by learning class-specific boundaries. We present and evaluate the method with a specific focus on the detection of lesion regions in uterine cervix images. The watershed map of the input image is modeled using MRF in which watershed regions correspond to binary random variables indicating whether the region is part of the lesion tissue or not. The local pairwise factors on the arcs of the watershed map indicate whether the arc is part of the object boundary. The factors are based on supervised learning of a visual word distribution. Final lesion region segmentation is obtained using a loopy belief propagation applied to the watershed arc-level MRF. Experimental results on real data show state-of-the-art segmentation results in this very challenging task. If needed, the results can be interactively even improved.
Amir Alush, Hayit Greenspan, Jacob Goldberger
Added 19 May 2010
Updated 19 May 2010
Type Conference
Year 2009
Where ISBI
Authors Amir Alush, Hayit Greenspan, Jacob Goldberger
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