TY - GEN
T1 - DoG-based detection of architectural distortion in mammographic images for computer-aided detection
AU - Handa, Takeshi
AU - Zhang, Xiaoyong
AU - Homma, Noriyasu
AU - Ishibashi, Tadashi
AU - Kawasumi, Yusuke
AU - Abe, Makoto
AU - Sugita, Norihiro
AU - Yoshizawa, Makoto
PY - 2012
Y1 - 2012
N2 - We propose a new method for accurate detection of architectural distortion that is a typical sign of breast cancer lesions in mammograms and necessary to be detected and diagnosed properly at an early stage for improvement of the survival rate of patients. An essential core of the proposed method is to efficiently extract a new general feature of the architectural distortions whose lesional intensities are not only higher than those of the surroundings as well known, but also often lower. While conventional features such as radial lines and higher intensities are difficult to be extracted and/or insufficient for accurate detection, the candidate area with such a new feature can be extracted accurately by using a difference of Gaussian (DoG)-based filter and after that a thresholding technique can reduce the number of false positives. The detection based on the new feature is expected to be more accurate than conventional ones because it reflects more general characteristics of the lesion. The experimental result using the database commonly tested worldwide shows that performance of the proposed method is superior to those of conventional ones.
AB - We propose a new method for accurate detection of architectural distortion that is a typical sign of breast cancer lesions in mammograms and necessary to be detected and diagnosed properly at an early stage for improvement of the survival rate of patients. An essential core of the proposed method is to efficiently extract a new general feature of the architectural distortions whose lesional intensities are not only higher than those of the surroundings as well known, but also often lower. While conventional features such as radial lines and higher intensities are difficult to be extracted and/or insufficient for accurate detection, the candidate area with such a new feature can be extracted accurately by using a difference of Gaussian (DoG)-based filter and after that a thresholding technique can reduce the number of false positives. The detection based on the new feature is expected to be more accurate than conventional ones because it reflects more general characteristics of the lesion. The experimental result using the database commonly tested worldwide shows that performance of the proposed method is superior to those of conventional ones.
KW - Mammography
KW - architectural distortion
KW - breast cancer
KW - computer-aided diagnosis and detection
KW - difference of Gaussians
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M3 - Conference contribution
AN - SCOPUS:84869404596
SN - 9781467322591
T3 - Proceedings of the SICE Annual Conference
SP - 762
EP - 767
BT - 2012 Proceedings of SICE Annual Conference, SICE 2012
PB - Society of Instrument and Control Engineers (SICE)
T2 - 2012 51st Annual Conference on of the Society of Instrument and Control Engineers of Japan, SICE 2012
Y2 - 20 August 2012 through 23 August 2012
ER -