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Conference Papers Year : 2016

Unsupervised Malignant Mammographic Breast Mass Segmentation Algorithm Based On Pickard Markov Random Field

Abstract

In this paper, we present a probabilistic approach to segmentation of malignant breast masses which have irregular shape, spiculated margins and which may be embedded in high density glandular tissue. First, we perform contrast enhancement of the image using a simple logarithmic transformation. Then, we derive a segmentation technique based on a specific class of Markov random fields (MRFs) known as Pickard random fields. As opposed to most MRF-based methods which require complex and time-consuming computations, our approach is simple, much faster and nearly unsupervised as it only requires specification of the number of levels on the MRF. Tests performed on 48 malignant masses extracted from the INbreast database revealed that the proposed approach yields superior results while being robust with respect to the high variability of mammograms.
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Dates and versions

hal-01367675 , version 1 (16-09-2016)

Identifiers

  • HAL Id : hal-01367675 , version 1

Cite

Sègbédji Goubalan, Yves Goussard, Hichem Maaref. Unsupervised Malignant Mammographic Breast Mass Segmentation Algorithm Based On Pickard Markov Random Field. IEEE International Conference on Image Processing (ICIP 2016), Sep 2016, Phoenix, Arizona, United States. pp.2653--2657. ⟨hal-01367675⟩
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