Unsupervised detection of mammogram regions of interest

Authors:
Michal Haindl;Stanislav Mikeš;Giuseppe Scarpa
Affiliations:
Dep. of Pattern Recognition, Institute of Information Theory and Automation, Academy of Sciences CR, Prague, Czech Republic;Dep. of Pattern Recognition, Institute of Information Theory and Automation, Academy of Sciences CR, Prague, Czech Republic;University Federico II, Naples, Italy
Venue:
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Year:
2007

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Abstract

We present an unsupervised method for fully automatic detection of regions of interest containing fibroglandular tissue in digital screening mammography. The unsupervised segmenter is based on a combination of several unsupervised segmentation results, each in different resolution, using the sum rule. The mammogram tissue textures are locally represented by four causal monospectral random field models recursively evaluated for each pixel. The single-resolution segmentation part of the algorithm is based on the underlying Gaussian mixture model and starts with an over segmented initial estimation which is adaptively modified until the optimal number of homogeneous mammogram segments is reached. The performance of the presented method is extensively tested on the Digital Database for Screening Mammography (DDSM) from the University of South Florida as well as on the Prague Texture Segmentation Benchmark using the commonest segmentation criteria and where it compares favourably with several alternative texture segmentation methods.