Abstract
BackgroundScreening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different methods on the feature selection process in terms of the learning performances and selected features.ResultsFor each ROI, we extracted textural features on Haar wavelet decompositions and also interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg). Then a Random Forest binary classifier is trained on a subset of a sub-set features selected by two different kinds of feature selection techniques, such as filter and embedded methods. We tested the proposed model on 260 ROIs extracted from digital mammograms of the BCDR public database. The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16% and 92.08%, and an accuracy of 97.31% and 88.46%, respectively. The experimental result was comparable with related work performance.ConclusionsThe best performing result obtained with embedded method is more parsimonious than the filter one. The SURF and MinEigen algorithms provide a strong informative content useful for the characterization of microcalcification clusters.
Highlights
Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage
For each Region of interest (ROI) a set of well-defined textural features, such standard statistical features, on a multiscale decomposition of the image based on the Haar wavelet transform [27, 28] are extracted
It was trained on statistical features calculated on the multiscale decomposition of the image based on the Haar wavelet transform, and on interest points and corners detected by using two known algorithms, Speeded Up Robust Feature (SURF) and MinEigenAlg
Summary
Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of microcalcifications is usually based on radiologists expertise resulting in some cases in inaccurate lesion detection [4,5,6] or in performing unnecessary breast biopsies on benign calcification clusters. Fanizzi et al BMC Bioinformatics 2020, 21(Suppl 2): becomes more evident in women with dense breast tissue that can hide lesions causing cancer to be detected at later stages [7, 8] To overcome such limits, a solution is represented by the double blind reading of the mammograms by two radiologists [9] with a consequent higher workload and cost. A more interesting solution could be represented by using intelligent techniques to automatize the process of identification, normal vs abnormal tissue, and diagnosis, benign vs malignant, of clustered microcalcifications
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