Mammogram mass classification with temporal features and multiple kernel learning

Fei Ma, Limin Yu, Mariusz Bajger, Murk Bottema

    Research output: Contribution to conferencePaper

    Abstract

    Based on previous work on regional temporal mammogram registration, this study investigates the combination of image features measured from single regions (single features) and image features measured from the matched regions of temporal mammograms (temporal features) for the classification of malignant masses. Three SVM kernels, the multilayer perceptron kernel, the polynomial kernel, and the gaussian radial basis function kernel, and the combination of these kernels, the multiple kernel learning method, were applied to both single and temporal features for the mass classification. To combine the two types of features, 3 combination rules, Linear combination, Max and Min, were used to combine classification results obtained on single and temporal features. The results showed that combining the MKL classification results on single features, and MKL classification results on temporal features, with Min rule produces the best classification results. The experiment result indicates that incorporating the temporal change information in mammography mass classification can improve the performance detection.

    Original languageEnglish
    DOIs
    Publication statusPublished - 1 Jan 2015
    Event2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) -
    Duration: 23 Nov 2015 → …

    Conference

    Conference2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
    Period23/11/15 → …

    Keywords

    • Mass classification
    • Multiple kernel learning
    • Temporal features
    • Temporal mammogram

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  • Cite this

    Ma, F., Yu, L., Bajger, M., & Bottema, M. (2015). Mammogram mass classification with temporal features and multiple kernel learning. Paper presented at 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), . https://doi.org/10.1109/DICTA.2015.7371282