Abstract
This paper presents a classification scheme for interstitial lung disease (ILD) pattern using patch-based approach and artificial neural network (ANN) classifier. A new feature descriptor, Multi-Scale Directional Mask Pattern (MSDMP), is proposed for feature extraction. Proposed MSDMP extracts microstructure information from a (31 × 31) size patches of the region of interest (ROI) which were marked by the radiologists. A two-layer feed-forward neural network is used for classification of ILD patterns. Also, proposed MSDMP feature descriptor has been tested on medical image retrieval system to check its robustness. Two benchmark medical datasets are used to evaluate the proposed descriptor. Performance analysis shows that the proposed feature descriptor outperforms the other existing state-of-the-art methods in terms of average recognition rate (ARR) and F-score.
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VIA/ELCAP CT Lung Image Dataset, available from [online]: https://veet.via.cornell.edu/lungdb.html.
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Dudhane, A.A., Talbar, S.N. (2018). Multi-Scale Directional Mask Pattern for Medical Image Classification and Retrieval. In: Chaudhuri, B., Kankanhalli, M., Raman, B. (eds) Proceedings of 2nd International Conference on Computer Vision & Image Processing . Advances in Intelligent Systems and Computing, vol 703. Springer, Singapore. https://doi.org/10.1007/978-981-10-7895-8_27
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