J Korean Soc Med Inform.  2009 Mar;15(1):165-172.

Detection of Microcalcifications in Digital Mammograms Using Foveal Method

• Oh WV
• Kim K
• Kim YJ
• Kang H
• Ro J
• Moon W

Abstract

OBJECTIVE
Breast cancer represents themost frequently diagnosed cancer in women. In order to reduce mortality, early detection of breast cancer is important, because diagnosis is more likely to be successful in the early stages of the disease. On the average, the reader's sensitivity can be increased by 10%with the assistance of computer-aided diagnosis (CAD) system. This paper presents a CAD system for the automatic detection of clustered micro-calcifications in digitized mammograms.
METHODS
The proposed system consists of three main steps. First, breast region is segmented from original mammogram using contrast property of grey level co-occurrence matrix(GLCM). Second, potential micro-calcification pixels in the mammograms are detected by foveal method. Third, in order to reduce false-positive rate, individual micro-calcifications are detected by a set of 8 features extracted from the potential individual micro-calcification objects.
RESULTS
In the result, Specificity and sensitivity are used to evaluate the detection performance of micro-calcifications.(sensitivity : 93.1%, specificity : 87.5%).
CONCLUSION
This study could be a useful method for diagnosis of breast cancer as a CAD system.

Keyword

Microcalcification; CAD; Biomedical Imaging

MeSH Terms

Breast
Breast Neoplasms
Diagnosis
Female
Humans
Mortality
Sensitivity and Specificity

Figure

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  Figure 1 An example of micro-calcification in mammogram image

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  Figure 2 The flow chart of our CAD system for detection of micro-calcification.

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  Figure 3 The result of breast region segmentation

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  Figure 4 The foveal masks used for the computation of µ0, µN and µB. The O is the size of the kernel object, N its neighborhood and B the background

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  Figure 5 (a) Mean (b) Standard deviation (c) Foreground background difference (d) Difference ratio (e) GLCM contrast (f) GLCM correlation (g) GLCM entropy (h) Fractal dimension hurst coefficient

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  Figure 6 The result image of proposed method (a),(c) original image, (b),(d) result image

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