Geometry-Based Pectoral Muscle Segmentation from MLO Mammogram Views

Saeid Asgari Taghanaki*, Yonghuai Liu, Brandon Miles, Ghassan Hamarneh

*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

42 Dyfyniadau (Scopus)

Crynodeb

Computer-aided diagnosis systems (CADx) play a major role in the early diagnosis of breast cancer. Extracting the breast region precisely from a mammogram is an essential component of CADx for mammography. The appearance of the pectoral muscle on medio-lateral oblique (MLO) views increases the false positive rate in CADx. Therefore, the pectoral muscle should be identified and removed from the breast region in an MLO image before further analysis. None of the previous pectoral muscle segmentation methods address all breast types based on the breast imaging-reporting and data system tissue density classes. In this paper, we deal with this deficiency by introducing a new simple yet effective method that combines geometric rules with a region growing algorithm to support the segmentation of all types of pectoral muscles (normal, convex, concave, and combinatorial). Experimental segmentation accuracy results were reported for four tissue density classes on 872 MLO images from three publicly available datasets. An average Jaccard index and Dice similarity coefficient of 0.972 ± 0.003 and 0.985 ± 0.001 were obtained, respectively. The mean Hausdorff distance between the contours detected by our method and the ground truth is below 5 mm for all datasets. An average acceptable segmentation rate of ∼95% was achieved outperforming several state-of-the-art competing methods. Excellent results were obtained even for the most challenging class of extremely dense breasts.

Iaith wreiddiolSaesneg
Rhif yr erthygl7831358
Tudalennau (o-i)2662-2671
Nifer y tudalennau10
CyfnodolynIEEE Transactions on Biomedical Engineering
Cyfrol64
Rhif cyhoeddi11
Dyddiad ar-lein cynnar24 Ion 2017
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 01 Tach 2017

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