Automated detection of masses in mammograms by local adaptive thresholding

Authors:
Guillaume Kom;Alain Tiedeu;Martin Kom
Affiliations:
LETS, GRETMAT, Ecole Nationale Supérieure Polytechnique, BP 8390, Yaoundé, Cameroun;LETS, GRETMAT, Ecole Nationale Supérieure Polytechnique, BP 8390, Yaoundé, Cameroun;LETS, GRETMAT, Ecole Nationale Supérieure Polytechnique, BP 8390, Yaoundé, Cameroun
Venue:
Computers in Biology and Medicine
Year:
2007

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Abstract

In this paper, an algorithm for detection of suspicious masses from mammographic images is presented. The proposed algorithm was tested on a database of 61 mammograms on which masses had previously been marked by experienced radiologists. Results show that the proposed method exhibits for mass detection, a sensitivity of 95.91%. The area under receiver operating characteristic (ROC) Az was 0.946 when enhancement of the original image was performed before detection and 0.938 otherwise. Furthermore in some cases, we could detect some masses that the radiologists were not able to mark out.