Detection of Clustered Microcalcifications Using Wavelets.
AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH
Pagination or Media Count:
An automated method for detecting microcalcification clusters is presented. The algorithm begins with a digitized mammogram and outputs the center coordinates of regions of interest ROIs that contain microcalcification clusters. The method presented uses a 12-tap Least Asymmetric Daubechies LAD12 wavelet in a tree structured filter bank to increase the signal to noise level of microcalcifications. The signal to noise level gain achieved by the filtering allows subsequent thresholding to eliminate on average 90 of the image from further consideration without eliminating actual microcalcifications 95 of the time. A novel approach to isolating individual calcifications from background tissue through non-stationary noise reduction, lowhi thresholding, and morphological filtering is demonstrated this technique reduces the number of false detections by an average of 5 per image. Several features are extracted from each potential calcification, including two newly proposed correlation features, to distinguish actual microcalcifications from correlated background tissue. Altogether, the method successfully detected 44 of 53 microcalcification clusters 83 with an average of 2.3 false positive clusters per image. A cluster is considered detected if it contains 3 or more microcalcifications within a 6.4 mm by 6.4 mm area. Although the emphasis is placed on detecting microcalcification clusters for further examination by a radiologist with no attempt made to diagnose the cluster as malignant or benign, the method successfully detected 13 of 14 93 malignant cases.
- Medicine and Medical Research