DocumentCode :
518927
Title :
A breast region extraction scheme for digital mammograms using gradient vector flow Snake
Author :
Yu, Shyr-Shen ; Tsai, Chung-Yen ; Liu, Chen-Chung
Author_Institution :
Dept. of Comput. Sci. & Eng., Nat. Chung-Hsing Univ., Taichung, Taiwan
fYear :
2010
fDate :
11-13 May 2010
Firstpage :
715
Lastpage :
720
Abstract :
Extracting the breast region accurately from a mammogram is a kernel stage for the breast mass analysis. It significantly influences the overall analysis accuracy and processing speed of the whole breast mass analysis. This paper presents an algorithm to extract the breast region from a mammogram. The presented algorithm first combines the median filtering, the morphological erosion processing, Sobel edge detector, and thresholding to find the rough boundary of the breast boundary, then uses the gradient vector flow snake (GVF- Snake) with gradient map adjustment to obtain the accurate breast boundary from the rough breast boundary. The presented algorithm is tested on the digital mammograms from the Mammogram Image Analysis Society (MIAS) database. The experimental results show that the breast region extracted by the presented algorithm approximately follows that extracted by an expert radiologist.
Keywords :
edge detection; feature extraction; gradient methods; image segmentation; mammography; median filters; medical image processing; Sobel edge detector; breast mass analysis; breast region extraction scheme; digital mammograms; gradient map adjustment; gradient vector flow Snake; mammogram image analysis society database; median filtering; morphological erosion processing; Active contours; Breast; Computer science; Deformable models; Detectors; Filtering algorithms; Histograms; Image edge detection; Kernel; Testing; GVF-snake; Mammogram; breast; extrapolation; mean error;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
New Trends in Information Science and Service Science (NISS), 2010 4th International Conference on
Conference_Location :
Gyeongju
Print_ISBN :
978-1-4244-6982-6
Electronic_ISBN :
978-89-88678-17-6
Type :
conf
Filename :
5488523
Link To Document :
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