Automatic segmentation of breast lesions on ultrasound.
In this paper we present a computationally efficient segmentation algorithm for breast masses on sonography that is based on maximizing a utility function over partition margins defined through gray-value thresholding of a preprocessed image. The performance of the segmentation algorithm is evaluated on a database of 400 cases in two ways. Of the 400 cases, 124 were complex cysts, 182 were benign solid lesions, and 94 were malignant lesions. In the first evaluation, the computer-delineated margins were compared to manually delineated margins. At an overlap threshold of 0.40, the segmentation algorithm correctly delineated 94% of the lesions. In the second evaluation, the performance of our computer-aided diagnosis method on the computer-delineated margins was compared to the performance of our method on the manually delineated margins. Round robin evaluation yielded Az values of 0.90 and 0.87 on the manually delineated margins and the computer-delineated margins, respectively, in the task of distinguishing between malignant and nonmalignant lesions.
Horsch
K
K
Department of Radiology, University of Chicago, Illinois 60637, USA.
Giger
M L
ML
Venta
L A
LA
Vyborny
C J
CJ
D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D019992:Databases as Topic; D003936:Diagnosis, Computer-Assisted; D007091:Image Processing, Computer-Assisted; D008962:Models, Theoretical; D012372:ROC Curve; D012984:Software; D014463:Ultrasonography / Q000379:methods*