Abstract
A comparing study for detection microcalcifications in digital mammogram using wavelets is proposed. Microcalcifications are early sign of breast cancer appeared as isolated bright spots in mammograms, however, they are difficult to detect due to their small size (0.05 to 1 mm of diameter). From a signal processing point of view, microcalcifications are high frequency components in mammograms. To enhance the detection performance of the microcalcifications in the mammograms we use the wavelet transform. Due to the multi-resolution decomposition capacity of the wavelet transform, we can decompose the image into different resolution levels which are sensitive to different frequency bands. By choosing an appropriate wavelet with a right resolution level, we can effectively detect the microcalcifications in digital mammogram. In this paper, several normal wavelet family functions are studied comparably, and for each wavelet function, different resolution levels are explored for detecting the microcalcifications. Experimental results show that the Daubechies wavelet with 4th level decomposition achieves the best detecting result of 95% TP rate with FP rate of 0.3 clusters per image.
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Yang, J.C., Shin, J.W., Park, D.S. (2004). Comparing Study for Detecting Microcalcifications in Digital Mammogram Using Wavelets. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_60
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DOI: https://doi.org/10.1007/978-3-540-28651-6_60
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