CONTEXT: Mammography is the most effective procedure for an early detection of the breast abnormalities. Masses are a type of abnormality, which are very difficult to be visually detected on mammograms. AIMS: In this paper an efficient method for detection of masses in mammograms is implemented. SETTINGS AND DESIGN: The proposed mass detector consists of two major steps. In the first step, several suspicious regions are extracted from the mammograms using an adaptive thresholding technique. In the second step, false positives originating by the previous stage are reduced by a machine learning approach. MATERIALS AND METHODS: All modules of the mass detector were assessed on mini-MIAS database. In addition, the algorithm was tested on INBreast database for more validation. RESULTS: According to FROC analysis, our mass detection algorithm outperforms other competing methods. CONCLUSIONS: We should not just insist on sensitivity in the segmentation phase because if we forgot FP rate, and our goal was just higher sensitivity, then the learning algorithm would be biased more toward false positives and the sensitivity would decrease dramatically in the false positive reduction phase. Therefore, we should consider the mass detection problem as a cost sensitive problem because misclassification costs are not the same in this type of problems.
CONTEXT: Mammography is the most effective procedure for an early detection of the breast abnormalities. Masses are a type of abnormality, which are very difficult to be visually detected on mammograms. AIMS: In this paper an efficient method for detection of masses in mammograms is implemented. SETTINGS AND DESIGN: The proposed mass detector consists of two major steps. In the first step, several suspicious regions are extracted from the mammograms using an adaptive thresholding technique. In the second step, false positives originating by the previous stage are reduced by a machine learning approach. MATERIALS AND METHODS: All modules of the mass detector were assessed on mini-MIAS database. In addition, the algorithm was tested on INBreast database for more validation. RESULTS: According to FROC analysis, our mass detection algorithm outperforms other competing methods. CONCLUSIONS: We should not just insist on sensitivity in the segmentation phase because if we forgot FP rate, and our goal was just higher sensitivity, then the learning algorithm would be biased more toward false positives and the sensitivity would decrease dramatically in the false positive reduction phase. Therefore, we should consider the mass detection problem as a cost sensitive problem because misclassification costs are not the same in this type of problems.
Authors: Afsaneh Jalalian; Syamsiah Mashohor; Rozi Mahmud; Babak Karasfi; M Iqbal B Saripan; Abdul Rahman B Ramli Journal: EXCLI J Date: 2017-02-20 Impact factor: 4.068