Abstract
Good feature design is important to achieve effective image classification. This paper presents a novel feature design with two main contributions. First, prior to computing the feature descriptors, we propose to transform the images with learning-based filters to obtain more representative feature descriptors. Second, we propose to transform the computed descriptors with another set of learning-based filters to further improve the classification accuracy. In this way, while generic feature descriptors are used, data-adaptive information is integrated into the feature extraction process based on the optimization objective to enhance the discriminative power of feature descriptors. The feature design is applicable to different application domains, and is evaluated on both lung tissue classification in high-resolution computed tomography (HRCT) images and apoptosis detection in time-lapse phase contrast microscopy image sequences. Both experiments show promising performance improvements over the state-of-the-art.
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Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: Binary Robust Independent Elementary Features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010)
Cheng, L., Ye, N., Yu, W., Cheah, A.: Discriminative Segmentation of Microscopic Cellular Images. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 637–644. Springer, Heidelberg (2011)
Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: CVPR, pp. 886–893 (2005)
Depeursinge, A., de Ville, D.V., Platon, A., Geissbuhler, A., Poletti, P.A., Muller, H.: Near-Affine-Invariant Texture Learning for Lung Tissue Analysis Using Isotropic Wavelet Frames. IEEE Trans. Inf. Technol. Biomed. 16(4), 665–675 (2012)
Huh, S., Ker, D.F.E., Su, H., Kanade, T.: Apoptosis Detection for Adherent Cell Populations in Time-Lapse Phase-Contrast Microscopy Images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 331–339. Springer, Heidelberg (2012)
Jacobs, C., Sánchez, C.I., Saur, S.C., Twellmann, T., de Jong, P.A., van Ginneken, B.: Computer-Aided Detection of Ground Glass Nodules in Thoracic CT Images Using Shape, Intensity and Context features. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 207–214. Springer, Heidelberg (2011)
Kumar, R., Banerjee, A., Vemuri, B.C.: Voterrafaces: Discriminant Analysis using Volterra Kernels. In: CVPR, pp. 150–155 (2009)
Lei, Z., Yi, D., Li, S.Z.: Discriminant Image Filter Learning for Face Recognition with Local Binary Pattern Like Representation. In: CVPR, pp. 2512–2517 (2012)
Ling, H., Okada, K.: Diffusion Distance for Histogram Comparison. In: CVPR, pp. 246–253 (2006)
Song, Y., Cai, W., Zhou, Y., Feng, D.D.: Feature-based Image Patch Approximation for Lung Tissue Classification. IEEE Trans. Med. Imag., 1–12 (2013)
Sorensen, L., Shaker, S.B., de Bruijne, M.: Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns. IEEE Trans. Med. Imag. 29(2), 559–569 (2010)
Xu, Y., Zhang, J., Chang, E.I.-C., Lai, M., Tu, Z.: Context-Constrained Multiple Instance Learning for Histopathology Image Segmentation. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 623–630. Springer, Heidelberg (2012)
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Song, Y. et al. (2013). Discriminative Data Transform for Image Feature Extraction and Classification. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40763-5_56
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DOI: https://doi.org/10.1007/978-3-642-40763-5_56
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