|
Segmentation of masses is the first step in most computer-aided diagnosis (CAD) systems for characterization of breast
masses as malignant or benign. In this study, we designed an automated method for segmentation of masses on
ultrasound (US) images. The method automatically estimated an initial contour based on a manually-identified point
approximately at the mass center. A two-stage active contour (AC) method iteratively refined the initial contour and
performed self-examination and correction on the segmentation result. To evaluate our method, we compared it with
manual segmentation by an experienced radiologists (R1) on a data set of 226 US images containing biopsy-proven
masses from 121 patients (44 malignant and 77 benign). Four performance measures were used to evaluate the
segmentation accuracy; two measures were related to the overlap between the computer and radiologist segmentation,
and two were related to the area difference between the two segmentation results. To compare the difference between the
segmentation results by the computer and R1 to inter-observer variation, a second radiologist (R2) also manually
segmented all masses. The two overlap measures between the segmentation results by the computer and R1 were
0.87+
0.16 and 0.73+ 0.17 respectively, indicating a high agreement. However, the segmentation results between two
radiologists were more consistent. To evaluate the effect of the segmentation method on classification accuracy, three
feature spaces were formed by extracting texture, width-to-height, and posterior shadowing features using the computer
segmentation, R1's manual segmentation, and R2's manual segmentation. A linear discriminant analysis classifier using
stepwise feature selection was tested and trained by a leave-one-case-out method to characterize the masses as malignant
or benign. For case-based classification, the area Az under the test receiver operating characteristic (ROC) curve was
0.90±0.03, 0.87±0.03 and 0.87±0.03 for the feature sets based on computer segmentation, R1's manual segmentation,
and R2's manual segmentation, respectively.
|
