DocumentCode :
2707081
Title :
Infiltrative breast cancer initial detection based on double-scale sech template matching
Author :
Ke, Li ; Chen, Yingying ; Li, Nan ; Kang, Yan
Author_Institution :
Inst. of Biomed. & Electromagn. Eng., Shenyang Univ. of Technol., Shenyang, China
fYear :
2012
fDate :
6-8 June 2012
Firstpage :
887
Lastpage :
890
Abstract :
Breast mass is a significant indicator of breast cancer. It is difficult for CAD (Computer Aided Diagnosis, CAD) system to detect infiltrative breast masses accurately because the masses are quite subtle, often occurred in dense areas of breast tissues, change various in shape and size and always infiltrate into breast tissues. In this paper, we proposed a new algorithm based on double-scale Sech template for early infiltrative masses initial detection from mammographic images. This algorithm includes the breast region extraction, breast pectoral-muscle remove and suspected mass regions detection. The proposed algorithm was tested on a database of 60 mammograms which masses had previously been marked by experienced radiologists. The detection sensitivity in this method is 100%, and average 2.77 false positive regions in every image. These results show the effectiveness of the proposed algorithm.
Keywords :
cancer; feature extraction; mammography; medical image processing; object detection; patient diagnosis; CAD; breast pectoral-muscle remove; breast region extraction; breast tissues; computer aided diagnosis; detection sensitivity; early infiltrative masses initial detection; infiltrative breast cancer initial detection; mammographic images; on double-scale sech template matching; suspected mass regions detection; Algorithm design and analysis; Breast cancer; Breast tissue; Computers; Design automation; Image segmentation; CAD; breast cancer; infiltrative breast masses; template matching;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Automation (ICIA), 2012 International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4673-2238-6
Electronic_ISBN :
978-1-4673-2236-2
Type :
conf
DOI :
10.1109/ICInfA.2012.6246907
Filename :
6246907
Link To Document :
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