An Effective Microcalcification Detection in Digitized Mammograms Using Morphological Analysis and Multi-stage Neural Network 


Vol. 29,  No. 3, pp. 374-386, Mar.  2004


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  Abstract

The mammogram provides the way to observe detailed internal organization of breasts to radiologists for the early detection.
This paper is mainly focused on efficiently detecting the Microcalcification's Region Of Interest(ROI)s. Breast cancers can be caused from either microcalcifications or masses. Microcalcifications are appeared in a digital mammogram as tiny dots that have a lit시e higher gray levels than their surrounding pixels. We can roughly determine the area which possibly contain microcalifications. In general, it is very challenging to find all the microcalcifications in a digital mammogram, because they are similar to some tissue paris of a breast.
To efficiently detect microcalcifications ROI, we used four sequential processes; preprocessing for breast area detection, modified multilevel thresholding, ROI selection using simple thresholding filters and final ROI selection with two stages of neural networks.
The filtering process with boundary conditions removes easily-distinguishable tissues while keeping all microcalcifications so that it cleans the thresholded mammogram images and speeds up the later processing by the average of 86%. The first neural network shows the average of 96.66% recognition rate. The second neural network performs better by showing the average recognition rate 98.26%. By removing all tissues while keeping microcalcifications as much as possible, the next paris of a CAD system for detecting breast cancers can become much simpler.

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  Cite this article

[IEEE Style]

J. Shin, S. Yoon, D. Park, "An Effective Microcalcification Detection in Digitized Mammograms Using Morphological Analysis and Multi-stage Neural Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 29, no. 3, pp. 374-386, 2004. DOI: .

[ACM Style]

Jin-Wook Shin, Sook Yoon, and Dong-Sun Park. 2004. An Effective Microcalcification Detection in Digitized Mammograms Using Morphological Analysis and Multi-stage Neural Network. The Journal of Korean Institute of Communications and Information Sciences, 29, 3, (2004), 374-386. DOI: .

[KICS Style]

Jin-Wook Shin, Sook Yoon, Dong-Sun Park, "An Effective Microcalcification Detection in Digitized Mammograms Using Morphological Analysis and Multi-stage Neural Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 29, no. 3, pp. 374-386, 3. 2004.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880