Abstract
In recent years, histopathology images have been increasingly used as a diagnostic tool in the medical field. The process of accurately diagnosing a biopsy sample requires significant expertise in the field, and as such can be time-consuming and is prone to uncertainty and error. With the advent of digital pathology, using image recognition systems to highlight problem areas or locate similar images can aid pathologists in making quick and accurate diagnoses. In this paper, we specifically consider the encoded local projections (ELP) algorithm, which has previously shown some success as a tool for classification and recognition of histopathology images. We build on the success of the ELP algorithm as a means for image classification and recognition by proposing a modified algorithm which captures the local frequency information of the image. The proposed algorithm estimates local frequencies by quantifying the changes in multiple projections in local windows of greyscale images. By doing so we remove the need to store the full projections, thus significantly reducing the histogram size, and decreasing computation time for image retrieval and classification tasks. Furthermore, we investigate the effectiveness of applying our method to histopathology images which have been digitally separated into their hematoxylin and eosin stain components. The proposed algorithm is tested on the publicly available invasive ductal carcinoma (IDC) data set. The histograms are used to train an SVM to classify the data. The experiments showed that the proposed method outperforms the original ELP algorithm in image retrieval tasks. On classification tasks, the results are found to be comparable to state-of-the-art deep learning methods and better than many handcrafted features from the literature.
This research has been supported in part by a Natural Sciences and Engineering Research Council of Canada (NSERC) Doctoral Scholarship (AKC).
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Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)
Cruz-Roa, A., et al.: Automatic detection of invasive ductal carcinoma in whole slide images with Convolutional Neural Networks. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 9041 (2014)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, pp. 886–893 (2005)
Gurcan, M.N., Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M., Yener, B.: Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2(2), 147–171 (2009)
Janowczyk, A., Madabhushi, A.: Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J. Pathol. Inform. 7(29) (2016)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Macenko, M., et al.: A method for normalizing histology slides for quantitative analysis. In: Proceedings of IEEE International Symposium on Biomedical Imaging, Chicago, IL, pp. 1107–1110 (2009)
McCann, M.T., Majumdar, J., Peng, C., Castro, C.A., Kovacevic, J.: Algorithm and benchmark dataset for stain separation in histology images. In: Proceedings of 2014 IEEE International Conference on Image Processing (ICIP), Paris, France, pp. 3953–3957 (2014)
Mendivil, F.: Computing the monge-kantorovich distance. Comput. Appl. Math. 36(3), 1389–1402 (2017)
Molter, U., Brandt, J., Cabrelli, C.: An algorithm for the computation of the hutchinson distance. Inform. Process. Lett. 40(2), 113–117 (1991)
Ruifrok, A.C., Johnston, D.A.: Quantification of histochemical staining by color deconvolution. Anal. Quant. Cytol. Histol. 23(4), 291–299 (2001)
Tizhoosh, H.R., Babaie, M.: Representing medical images with encoded local projections. IEEE Trans. Biomed. Eng. 65(10), 2267–2277 (2018)
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Cheeseman, A.K., Tizhoosh, H., Vrscay, E.R. (2019). A Compact Representation of Histopathology Images Using Digital Stain Separation and Frequency-Based Encoded Local Projections. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11663. Springer, Cham. https://doi.org/10.1007/978-3-030-27272-2_13
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DOI: https://doi.org/10.1007/978-3-030-27272-2_13
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