Computerized segmentation method for individual calcifications within clustered microcalcifications while maintaining their shapes on magnification mammograms

Abstract

In a computer-aided diagnosis (CADx) scheme for evaluating the likelihood of malignancy of clustered microcalcifications on mammograms, it is necessary to segment individual calcifications correctly. The purpose of this study was to develop a computerized segmentation method for individual calcifications with various sizes while maintaining their shapes in the CADx schemes. Our database consisted of 96 magnification mammograms with 96 clustered microcalcifications. In our proposed method, a mammogram image was decomposed into horizontal subimages, vertical subimages, and diagonal subimages for a second difference at scales 1 to 4 by using a filter bank. The enhanced subimages for nodular components (NCs) and the enhanced subimages for both nodular and linear components (NLCs) were obtained from analysis of a Hessian matrix composed of the pixel values in those subimages for the second difference at each scale. At each pixel, eight objective features were given by pixel values in the subimages for NCs at scales 1 to 4 and the subimages for NLCs at scales 1 to 4. An artificial neural network with the eight objective features was employed to enhance calcifications on magnification mammograms. Calcifications were finally segmented by applying a gray-level thresholding technique to the enhanced image for calcifications. With the proposed method, a sensitivity of calcifications within clustered microcalcifications and the number of false positives per image were 96.5% (603/625) and 1.69, respectively. The average shape accuracy for segmented calcifications was also 91.4%. The proposed method with high sensitivity of calcifications while maintaining their shapes would be useful in the CADx schemes.

Fig. 1

Schematic diagram of the proposed method for the segmentation of individual calcifications within clustered microcalcifications on magnification mammograms

Fig. 2

Filter bank for analyzing both the size information and the shape information (from scales 1 to 3)

Fig. 3

Subimages obtained from the analysis bank of the filter bank at scales 1 to 4

Fig. 4

Example of enhanced subimages for NCs and for NLCs at scales 1 to 4, which were obtained from a ROI with clustered microcalcifications

Fig. 5

Example of enhanced subimages for NCs and for NLCs at scales 1 to 4, which were obtained from a ROI with blood vessels

Fig. 6

Example of an enhanced image for calcifications. a Original image, b enhanced image

Fig. 7

Relationship between N features and NL features a at scale 1, b at scale 2, c at scale 3, and d at scale 4

Fig. 8

FROC curves obtained by three different computerized methods with N features and NL features, N features, and NL features at scales 1 to 4

Fig. 9

Change in the average shape accuracies for segmented calcifications by the proposed method

Fig. 10

Example of a segmented image for calcifications. a Original image, b true calcification regions determined by an experienced radiologist, c segmented calcifications by the proposed method

Fig. 11

FROC curves obtained by three different computerized methods with the N features and the NL features at scales 1–3, scales 1–4, and scales 1–5

Fig. 12

Change in the average shape accuracies for segmented calcifications obtained by three different computerized methods