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Proceedings Paper

Computer-aided detection of microcalcifications in digital mammograms using ANN classifier
Author(s): Ruiping Wang; Baikun Wan; Xuchen Cao; Zhenhe Ma
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Paper Abstract

Clustered microcalcifications (MCCs) in mammograms are an important sign in the detection of breast cancer. Nevertheless, it is a complex and difficult task for radiologists to detect the clustered MCCs from the tissue background of mammograms only by naked eyes. This paper presents a prototype of a computer-aided detection system to automatically detect MCCs in mammograms. The detection algorithm mainly comprises three modules. The first module, called the mammogram pre-progressing module, inputs and digitizes mammograms into 8-bit images of size 2048x2048, normalizes the images, manually extracts the breast region from the background. The second module, called the feature extraction module, is achieved by using mixed features consisting of two wavelet features and two gray level statistical features. The wavelet features are generated by a five-level wavelet decomposition and reconstruction algorithm. The gray level statistical features used in this paper are median contrast and normalized gray level value. Finally, the third module, called the MCCs detection module, discovers MCCs in the images by using a classifier. This paper uses a three-layer artificial neural network (ANN) as a classifier to segment MCCs from the processing image. The ANN takes these four features generated in the second module as inputs. The output of the ANN corresponding to the true MCC pixels is then thresholded to segment out the true MCC pixels. One advantage of the designed system is that each module is a separate component that can be individually upgraded to improve the whole system. The algorithm is tested with a series of clinical mammograms. A sensitivity of more than 78% is obtained at a relatively low false-positive (FP) detection of 2.09 per image. The results are compared with the judgement of radiological experts, and they are very encouraging.

Paper Details

Date Published: 30 August 2002
PDF: 4 pages
Proc. SPIE 4925, Electronic Imaging and Multimedia Technology III, (30 August 2002); doi: 10.1117/12.481545
Show Author Affiliations
Ruiping Wang, Tianjin Univ. (China)
Baikun Wan, Tianjin Univ. (China)
Xuchen Cao, Tianjin Medical Univ. Tumor Hospital (China)
Zhenhe Ma, Tianjin Univ. (China)


Published in SPIE Proceedings Vol. 4925:
Electronic Imaging and Multimedia Technology III
LiWei Zhou; Chung-Sheng Li; Yoshiji Suzuki, Editor(s)

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