Affiliations: [a] University of Kragujevac, Kragujevac, Serbia | [b] Research and Development Centre for Bioengineering, Kragujevac, Serbia | [c] Clinical Center Kragujevac, Kragujevac, Serbia
Abstract: BACKGROUND: Reading mammograms is a difficult task and for this
reason any development that may improve the performance in breast cancer
screening is of great importance.
OBJECTIVE: We proposed optimized computer aided diagnosis (CAD)
system, equipped with reliability estimate module, for mass detection on
digitized mammograms.
METHODS: Proposed CAD system consists of four major steps:
preprocessing, segmentation, feature extraction and classification. We
propose a simple regression function as a threshold function for extraction
of potential masses. By running optimization procedure we estimate
parameters of the preprocessing and segmentation steps thus ensuring maximum
mass detection sensitivity. In addition to the classification, where we
tested seven different classifiers, the CAD system is equipped with
reliability estimate module.
RESULTS: By performing segmentation 91.3% of masses were
correctly segmented with 4.14 false positives per image (FPpi). This result
is improved in the classification phase where, among the seven tested
classifiers, multilayer perceptron neural network achieved the best result
including 77.4% sensitivity and 0.49 FPpi.
CONCLUSION: By using the proposed regression function and
parameter optimization we were able to improve segmentation results
comparing to the literature. In addition, we showed that CAD system has high
potential for being equipped with reliability estimate module.
Keywords: Breast cancer, computer-assisted image processing, data mining, mammography, optimization
