Abstract
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 language | English |
|---|---|
| Title of host publication | Breast Imaging - 12th International Workshop, IWDM 2014, Proceedings |
| Publisher | Springer Verlag |
| Pages | 606-611 |
| Number of pages | 6 |
| ISBN (Print) | 9783319078861 |
| DOIs | |
| Publication status | Published - 01-01-2014 |
| Event | 12th International Workshop on Breast Imaging, IWDM 2014 - Gifu City, Japan Duration: 29-06-2014 → 02-07-2014 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 8539 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 12th International Workshop on Breast Imaging, IWDM 2014 |
|---|---|
| Country | Japan |
| City | Gifu City |
| Period | 29-06-14 → 02-07-14 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Theoretical Computer Science
Cite this
}
Automated detection of architectural distortion using improved adaptive Gabor filter. / Yoshikawa, Ruriha; Teramoto, Atsushi; Matsubara, Tomoko; Fujita, Hiroshi.
Breast Imaging - 12th International Workshop, IWDM 2014, Proceedings. Springer Verlag, 2014. p. 606-611 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8539 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Automated detection of architectural distortion using improved adaptive Gabor filter
AU - Yoshikawa, Ruriha
AU - Teramoto, Atsushi
AU - Matsubara, Tomoko
AU - Fujita, Hiroshi
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84903987902&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84903987902&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-07887-8_84
DO - 10.1007/978-3-319-07887-8_84
M3 - Conference contribution
AN - SCOPUS:84903987902
SN - 9783319078861
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 606
EP - 611
BT - Breast Imaging - 12th International Workshop, IWDM 2014, Proceedings
PB - Springer Verlag
ER -