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Proceedings Paper

Improving mass detection performance by use of 3D difference filter in a whole breast ultrasonography screening system
Author(s): Yuji Ikedo; Daisuke Fukuoka; Takeshi Hara; Hiroshi Fujita; Etsuo Takada; Tokiko Endo; Takako Morita
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Paper Abstract

Ultrasonography is one of the most important methods for breast cancer screening in Japan. Several mechanical whole breast ultrasound (US) scanners have been developed for mass screening. We have reported a computer-aided detection (CAD) scheme for the detection of masses in whole breast US images. In this study, the method of detecting mass candidates and the method of reducing false positives (FPs) were improved in order to enhance the performance of this scheme. A 3D difference (3DD) filter was newly developed to extract low-intensity regions. The 3DD filter is defined as the difference of pixel values between the current pixel value and the mean pixel value of 17 neighboring pixels. Low-intensity regions were efficiently extracted by use of 3DD filter values, and FPs were reduced using a FP reduction method employing the rule-based technique and quadratic discriminant analysis with the filter values. The performance of our previous and improved CAD schemes indicated a sensitivity of 80.0% with 16.8 FPs and 9.5 FPs per breast, respectively. The FPs of the improved scheme were reduced by 44% as compared to the previous scheme. The 3DD filter was useful for the detection of masses in whole breast US images.

Paper Details

Date Published: 17 March 2008
PDF: 8 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 691523 (17 March 2008); doi: 10.1117/12.769301
Show Author Affiliations
Yuji Ikedo, Gifu Univ. (Japan)
Daisuke Fukuoka, Gifu Univ. (Japan)
Takeshi Hara, Gifu Univ. (Japan)
Hiroshi Fujita, Gifu Univ. (Japan)
Etsuo Takada, Dokkyo Medical Univ. School of Medicine (Japan)
Tokiko Endo, Nagoya Medical Ctr. (Japan)
Takako Morita, Chunichi Hospital (Japan)


Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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