Segmentation Of Spiculated Masses In Mammographic Images
Résumé
Breast cancer is among the leading causes of cancer deaths for
women. In 2012, 522 000 deaths have been recorded worldwide,
representing a 14% increase compared to 2008. Generally,
the benign masses class is associated with the presence
of circular or oval shapes, while spiculated masses are more
likely to belong to the malignant masses class. Thus, spicule
is a leading discriminant factor in the classification of various
masses. Its extraction is a complex task because of their low
contrast, variable widths and the overlapping of blood vessels,
fibers and ducts.
With the increase of images obtained during the screening,
mammography interpretation by radiologists is becoming
more difficult, time-consuming, and leads sometimes the
increase the ratio of false positives due to tissue superimposition.
Hence, to help radiologists improve detection and diagnosis
accuracy the design of computer aided detection systems
(CADe) known as a great leap forward in recent years
due to their ability to provide an objective and reproductible
second opinion. The CADe are structured in three steps: segmentation
of the region of interest which contains the mass
and description of the segmented mass.
Based on these steps, the proposed method for automatic
breast mass detection can be described as follows: the segmentation
is based on MRF using the Pickard random field
(PRF) which is much faster, more robust and nearly unsupervised
compare to most of MRF-based methods, which require
complex and time-consuming computations [1, 2]. The
description step presents the main contribution of this paper,
since, to the best of our knowledge, we present the first attempt
to extract the spicules with the mixture of a Markovian
framework and an a contrario model.
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