Far East Journal of Experimental and Theoretical Artificial Intelligence
Volume 6, Issue 1-2, Pages 25 - 41
(November 2010)
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DETECTION OF CLUSTERED MICROCALCIFICATIONS USING BOOTSTRAP PIXCALS ALGORITHM
S. Deva Arul and W. Jai Singh
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Abstract:
Detection of microcalcifications in mammograms has received much attention from researchers and public health practitioners in recent years. Microcalcifications appear in a mammogram as fine, granular clusters, which are tedious to identify in a raw mammogram. A variety of techniques have been proposed in the literature to enhance and automatically detect microcalcifications, but none of the methods gives complete detection and clinically acceptable results. Many mammograms do not follow any type of available statistical distributions. Hence in this paper, we propose bootstrap Pixcals algorithm to detect microcalcifications. A distribution free, non-parametric bootstrap technique is embedded in an algorithm to detect microcalcifications. The proposed system is able to classify an image as normal or abnormal, and also for an abnormal image it indicates the suspected area which contains microcalcifications. The different kinds of images have been considered and tested using the proposed algorithm. The efficiency of algorithm is measured using ROC and the results are compared with existing one. |
| Keywords and phrases: breast cancer, mammogram, microcalcifications, region of interest, bootstrap, clusters, box plot, k-means, mammography. |
| Communicated by Shun-Feng Su |
| Number of Downloads: 350 | Number of Views: 402 |
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