Abstract
We propose a novel local image descriptor called the Extended Multi-resolution Local Patterns, and a discriminative probabilistic framework for learning its parameters together with a multi-class image classifier. Our approach uses training data with image-level labels to learn the features which are discriminative for multi-class colonoscopy image classification. Experiments on a three class (abnormal, normal, uninformative) white-light colonoscopy image dataset with 2800 images show that the proposed feature perform better than popular hand-designed features used in the medical as well as in the computer vision literature for image classification.
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Notes
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Tesla K40 GPU used for this research was donated by the NVIDIA Corporation.
References
Atkin, W., Cook, C., Cuzick, J., Edwards, R., Northover, J., Wardle, J.: Once-only flexible sigmoidoscopy screening in prevention of colorectal cancer: a multicentre randomised controlled trial. Lancet 132, 1624–1633 (2010)
Wallace, M.: Improving colorectal adenoma detection: technology or technique? Gastroenterology 132, 1221–1223 (2007)
Bressler, B., Paszat, L.F., Chen, Z., Rothwell, D.M., Vinden, C., Rabeneck, L.: Rates of new or missed colorectal cancers after colonoscopy and their risk factors: a population-based analysis. Gastroenterology 132(1), 96–102 (2007)
Arnold, M., Ghosh, A., Lacey, G., Patchett, S., Mulcahy, H.: Indistinct frame detection in colonoscopy videos. In: Machine Vision and Image Processing Conference, pp. 47–52(2009)
Karkanis, S.A., Iakovvidis, D.K., Maroulis, D.E., Karras, D.A., Tzivras, M.: Computer aided tumor detection in endoscopic video using color wavelet features. IEEE Trans. IT Biomed. 7, 141–152 (2003)
Khun, P.C., Zhuo, Z., Yang, L.Z., Liyuan, L., Jiang, L.: Feature selection and classification for wireless capsule endoscopic frames. In: Biomedical and Pharmaceutical Engineering, pp. 1–6 (2009)
Lima, C., Barbosa, D., Ramos, A., Tavares, A., Montero, L., Carvalho, L.: Classification of endoscopic capsule images by using color wavelet features, higher order statistics and radial basis functions. In: IEEE Engineering in Medicine and Biology Society, pp. 1242–1245 (2008)
Manivannan, S., Wang, R., Trucco, E., Hood, A.: Automatic normal-abnormal video frame classification for colonoscopy. In: IEEE International Symposium on Biomedical Imaging, pp. 644–647 (2013)
Becker, C.J., Rigamonti, R., Lepetit, V., Fua, P.: Supervised feature learning for curvilinear structure segmentation. In: Medical Image Computing and Computer-Assisted Intervention, pp. 526–533 (2013)
Brown, M., Hua, G., Winder, S.: Discriminative learning of local image descriptors. IEEE Pattern Anal. Mach. Intell. 33, 43–57 (2011)
Simonyan, K., Vedaldi, A., Zisserman, A.: Learning local feature descriptors using convex optimisation. IEEE Pattern Anal. Mach. Intell. 36, 1573–1585 (2014)
Philbin, J., Isard, M., Sivic, J., Zisserman, A.: Descriptor learning for efficient retrieval. In: European Conference on Computer Vision, pp. 677–691 (2010)
Matthew, Z., Rob, F.: Visualizing and understanding convolutional networks. In: European Conference on Computer Vision, pp. 818–833 (2014)
Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1717–1724 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012)
Winder, S., Hua, G., Brown, M.: Picking the best daisy. In: IEEE Computer Vision and Pattern Recognition, pp. 178–185 (2009)
Manivannan, S., Li, W., Akbar, S., Wang, R., Zhang, J., McKenna, S.J.: An automated pattern recognition system for classifying indirect immunofluorescence images of HEp-2 cells and specimens. Pattern Recogn. 51, 12–26 (2016)
Manivannan, S., Li, W., Akbar, S., Wang, R., Zhang, J., McKenna, S.J.: HEp-2 cell classification using multi-resolution local patterns and ensemble SVMs. In: I3A 1st workshop on Pattern Recognition Techniques for Indirect Immunoflurescence Images, in International Conference on Pattern Recognition (2014)
Boiman, O., Shechtman, E., Irani, M.: In defense of nearest-neighbor based image classification. In: IEEE Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Zhen, X., Shao, L., Zheng, F.: Discriminative embedding via image-to-class distances. In: British Machine Vision Conference (2014)
Wang, Z., Hu, Y., Chia, L.T.: Image-to-class distance metric learning for image classification. In: European Conference on Computer Vision, pp. 706–719 (2010)
Bingham, E., Mannila, H.: Random projection in dimensionality reduction: applications to image and text data. In: ACM Knowledge Discovery and Data Mining, pp. 245–250 (2001)
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: IEEE Computer Vision and Pattern Recognition, pp. 3360–3367 (2010)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011)
Engelhardt, S., Ameling, S., Paulus, D., Wirth, S.: Features for classification of polyps in colonoscopy. In: CEUR Workshop Proceedings (2010)
Maroulis, D.E., Iakovidis, D.K., Karkanis, S.A., Karras, D.A.: CoLD: a versatile detection system for colorectal lesions in endoscopy video-frames. Comput. Methods Programs Biomed. 70, 151–166 (2003)
Li, W., Manivannan, S., Zhang, J., Trucco, E., McKenna, S.J.: Gland segmentation in colon histology images using hand-crafted features and convolutional neural networks. In: IEEE International Symposium on Biomedical Imaging (2016)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)
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Manivannan, S., Trucco, E. (2017). Extended Multi-resolution Local Patterns - A Discriminative Feature Learning Approach for Colonoscopy Image Classification. In: Peters, T., et al. Computer-Assisted and Robotic Endoscopy. CARE 2016. Lecture Notes in Computer Science(), vol 10170. Springer, Cham. https://doi.org/10.1007/978-3-319-54057-3_5
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