A new preprocessing filter for digital mammograms

Authors:
Peyman Rahmati;Ghassan Hamarneh;Doron Nussbaum;Andy Adler
Affiliations:
Dept. of System and computer Engineering, Carleton University, ON, Canada;School of Computing Science, Simon Fraser University, BC, Canada;Dept. of Computer Science, Carleton University, ON, Canada;Dept. of System and computer Engineering, Carleton University, ON, Canada
Venue:
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Year:
2010

Citing 2
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Abstract

This paper presents a computer-aided approach to enhancing suspicious lesions in digital mammograms. The developed algorithm improves on a well-known preprocessor filter named contrast-limited adaptive histogram equalization (CLAHE) to remove noise and intensity inhomogeneities. The proposed preprocessing filter, called fuzzy contrast-limited adaptive histogram equalization (FCLAHE), performs non-linear enhancement. The filter eliminates noise and intensity inhomogeneities in the background while retaining the natural gray level variations of mammographic images within suspicious lesions. We applied Catarious segmentation method (CSM) to compare the segmentation accuracy in two scenarios: when there is no preprocessing filter, and when the proposed preprocessing filter is applied to the original image. The proposed filter has been evaluated on 50 real mammographic images and the experimental results show an average increase of segmentation accuracy by 14.16% when the new filter is applied.