A hybrid detection scheme of architectural distortion in mammograms using iris filter and Gabor filter

Mizuki Yamazaki, Atsushi Teramoto, Hiroshi Fujita

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


Architectural distortion in mammograms is the most frequently missed finding among breast cancer findings, the improvement of detection accuracy in existing commercial CAD software remains a challenge. In this study, in order to improve the detection accuracy of architectural distortion in mammography, we propose a hybrid automatic detection method that combines with the enhancement method of the concentration of line structure and massive pattern. In the method, the detection of the concentration of the line structure is conducted by the adaptive Gabor filter, and the enhancement of the massive pattern is performed by the iris filter. The concentration index is calculated from these filtered images; the lesion candidate regions are obtained. As for false positive (FP) reduction, 15 shape features are calculated from the candidate regions. Then, they are given to the support vector machine; the candidate regions are classified either as true positive or FP. In the experiment, we compared the results of the proposed method and physician interpretation report using 200 images (63 architectural distortions) from a digital database of screening mammography. Experimental results indicate that our method may be effective to improve the performance of computer aided detection in mammography.

Original languageEnglish
Title of host publicationBreast Imaging - 13th International Workshop, IWDM 2016, Proceedings
EditorsKristina Lang, Anders Tingberg, Pontus Timberg
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319415451
Publication statusPublished - 2016
Event13th International Workshop on Breast Imaging, IWDM 2016 - Malmo, Sweden
Duration: 19-06-201622-06-2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other13th International Workshop on Breast Imaging, IWDM 2016

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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