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Proceedings Paper

Detection of clustered microcalcifications in masses on mammograms by artificial neural networks
Author(s): Xuejun Zhang; Takeshi Hara; Hiroshi Fujita; Takuji Iwase; Tokiko Endo
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Paper Abstract

The existence of a cluster of microcalcifications in mass area on mammogram is one of important features for distinguishing the breast cancer between benign and malignant. However, missed detections often occur because of its low subject contrast in denser background and small quantity of microcalcifications. To get a higher performance of detecting the cluster in mass area, we combined the shift-invariant artificial neural network (SIANN) with triple-ring filter (TRF) method in our computer-aided diagnosis (CAD) system. 150 region-of- interests around mass containing both of positive and negative microcalcifications were selected for training the network by a modified error-back-propagation algorithm. A variable-ring filter was used for eliminating the false- positive (FP) detections after the outputs of SIANN and TRF. The remained Fps were then reduced by a conventional three layer artificial neural network. Finally, the program identified clustered microcalcifications form individual microcalcifications. In a practical detection of 30 cases with 40 clusters in masses, the sensitivity of detecting clusters was improved form 90% by our previous method to 95% by using both SIANN and TRF, while the number of FP clusters was decreased from 0.85 to 0.40 cluster per image.

Paper Details

Date Published: 3 July 2001
PDF: 8 pages
Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); doi: 10.1117/12.431063
Show Author Affiliations
Xuejun Zhang, Gifu Univ. (Japan)
Takeshi Hara, Gifu Univ. (Japan)
Hiroshi Fujita, Gifu Univ. (Japan)
Takuji Iwase, Aichi Cancer Ctr. Hospital (Japan)
Tokiko Endo, National Hospital of Nagoya (Japan)


Published in SPIE Proceedings Vol. 4322:
Medical Imaging 2001: Image Processing
Milan Sonka; Kenneth M. Hanson, Editor(s)

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