Abstract—This paper proposes a method that can enhance
the performance of Computer Aided Diagnosis (CAD) by
automatically detecting and classifying the microcalcifications
(MCs) in mammogram image accurately and efficiently using
multi statistical filters and wavelet decomposition transform.
The proposed method is divided to two main stages. In first
stage, the potential MCs region (PMR) is detected based on
visual characteristics of the MCs in the mammogram images.
Then wavelet decomposition transform is implemented to
classify the PMR to true positive and false positive regions
based on extraction four wavelet features for the mammogram
image. This novel method was found to be sensitive in detecting
MCs in mammogram images by achieving a high true positive
percentage of 98.1% and a low false positive rate 0.63
cluster/image for both MIAS and USF databases.
Index Terms—Mammogram, microcalcifications, wavelet
transform, wavelet features.
Ayman A. AbuBaker is with the Electrical and Computer Engineering
Department, Applied Science University, Amman, Jordan (e-mail:
a_abubaker@asu.edu.jo).
[PDF]
Cite:Ayman A. AbuBaker, "Automatic Microcalcification Detection Using Wavelet Transform," International Journal of Computer Theory and Engineering vol. 7, no. 1, pp. 40-45, 2015.
