Automated detection of architectural distortion using improved adaptive Gabor filter

Ruriha Yoshikawa, Atsushi Teramoto, Tomoko Matsubara, Hiroshi Fujita

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

16 Citations (Scopus)


Architectural distortion in mammography is the most missing finding for radiologists, despite high malignancy. Many research groups have developed methods for automated detection of architectural distortion. However, improvement of their detection performance is desired. In this study, we developed a novel method for automated detection of architectural distortion in mammograms. To detect the mammary gland structure, we used an adaptive Gabor filter, whichconsists of three Gabor filters created by changing the combination of parameters. The filter that is best matched to the mammary gland structure pixel by pixel in the mammogram is selected. After detecting the mammary gland, enhancement of the concentrated region and false positive reduction are performed. In the experiments, we verified the detection performance of our method using 50 mammograms. The true positive rate was found to be 82.45%, and the number of false positive per image was 1.06. These results are similar to or better than those of existing methods. Therefore, the proposed method may be useful for detecting architectural distortion in mammograms.

Original languageEnglish
Title of host publicationBreast Imaging - 12th International Workshop, IWDM 2014, Proceedings
PublisherSpringer Verlag
Number of pages6
ISBN (Print)9783319078861
Publication statusPublished - 2014
Event12th International Workshop on Breast Imaging, IWDM 2014 - Gifu City, Japan
Duration: 29-06-201402-07-2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8539 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other12th International Workshop on Breast Imaging, IWDM 2014
CityGifu City

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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