Contrast enhancement in dense breast images to aid clustered microcalcifications detection

Abstract

This paper presents a method to provide contrast enhancement in dense breast digitized images, which are difficult cases in testing of computer-aided diagnosis (CAD) schemes. Three techniques were developed, and data from each method were combined to provide a better result in relation to detection of clustered microcalcifications. Results obtained during the tests indicated that, by combining all the developed techniques, it is possible to improve the performance of a processing scheme designed to detect microcalcification clusters. It also allows operators to distinguish some of these structures in low-contrast images, which were not detected via conventional processing before the contrast enhancement. This investigation shows the possibility of improving CAD schemes for better detection of microcalcifications in dense breast images.

Fig 1

Scheme showing the resulting energy after attenuation in parts of the breast with and without microcalcification.

Fig 2

Fluxogram for redistributing the gray levels in the ACE technique.

Fig 3

Scheme representing the procedure FPR.

Fig 4

Graphics comparing the detections in original images and in images enhanced by the HTE technique: (a) percentage of true-positive detections, (b) percentage of false-positive detections.

Fig 5

Graphics comparing the detections in original images and in images enhanced by the ACE technique: (a) percentage of true-positive detections, (b) percentage of false-positive detections.

Fig 6

Graphics comparing the detections in original images and in images enhanced by the CCE technique: (a) percentage of true-positive detections, (b) percentage of false-positive detections.

Fig 7

ROC curves relative to the techniques HTE, ACE, and CCE, applied individually to the images set.

Fig 8

Graphic showing the effect of the FPR procedure on the processed images.

Fig 9

Diagram corresponding to the final configuration for the processing scheme intended to detect clustered microcalcifications in dense breast images.

Fig 10

ROC curve relative to the final configuration of the CAD scheme for the first images set.

Fig 11

Example of processing of the first images set with the final scheme configuration: (a) original image, (b) image after CCE, (c) image b after ACE, (d) image b after HTE, (e) image c after segmentation, (f) image d after segmentation, (g) image resulting from the union between e and f, (h) image g after APT, (i) image h after FPR, (j) resulting image after cluster detection.