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Decision support system for breast lesions via dynamic contrast enhanced magnetic resonance imaging

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Abstract

The presented study aims to design a computer-aided detection and diagnosis system for breast dynamic contrast enhanced magnetic resonance imaging. In the proposed system, the segmentation task is performed in two stages. The first stage is called breast region segmentation in which adaptive noise filtering, local adaptive thresholding, connected component analysis, integral of horizontal projection, and breast region of interest detection algorithms are applied to the breast images consecutively. The second stage of segmentation is breast lesion detection that consists of 32-class Otsu thresholding and Markov random field techniques. Histogram, gray level co-occurrence matrix and neighboring gray tone difference matrix based feature extraction, Fisher score based feature selection and, tenfold and leave-one-out cross-validation steps are carried out after segmentation to increase the reliability of the designed system while decreasing the computational time. Finally, support vector machines, k- nearest neighbor, and artificial neural network classifiers are performed to separate the breast lesions as benign and malignant. The average accuracy, sensitivity, specificity, and positive predictive values of each classifier are calculated and the best results are compared with the existing similar studies. According to the achieved results, the proposed decision support system for breast lesion segmentation distinguishes the breast lesions with 86%, 100%, 67%, and 85% accuracy, sensitivity, specificity, and positive predictive values, respectively. These results show that the proposed system can be used to support the radiologists during a breast cancer diagnosis.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Faculty of Engineering, Sakarya University, Sakarya, Turkey

    Gökçen Çetinel

  2. Internal Medical Sciences, Radiology Department, Education and Research Hospital, Sakarya University, Sakarya, Turkey

    Fuldem Mutlu

  3. Department of Electronics and Automation, Adapazarı Vocational High School, Sakarya University, Sakarya, Turkey

    Sevda Gül

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  1. Gökçen Çetinel
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  2. Fuldem Mutlu
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Correspondence to Gökçen Çetinel.

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Authors Dr. Gökçen Çetinel is the coordinator, Dr. Fuldem Mutlu is the researcher and Sevda Gül is the scholarship student in the project that is supported by The Scientific and Technical Research Council of Turkey (TUBITAK) through The Research Support Programs Directorate (ARDEB) with Project Number of 118E201.

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The study was approved by the Ethical Committee of Sakarya University Medical Sciences Faculty. All procedures in studies involving human participants were in accordance with the ethical standards of the institutional and/or national search committee.

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Çetinel, G., Mutlu, F. & Gül, S. Decision support system for breast lesions via dynamic contrast enhanced magnetic resonance imaging. Phys Eng Sci Med 43, 1029–1048 (2020). https://doi.org/10.1007/s13246-020-00902-2

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