Abstract
Histology image analysis is widely used in cancer studies since it preserves the tissue structure. In this paper, we propose a framework to grade metastatic liver histology images based on the spatial organization inter and intra regions. After detecting the presence of metastases, we first decompose the image into regions corresponding to the tissue types (sane, cancerous, vessels and gaps). A sample of each type is further decomposed into the contained biological objects (nuclei, stroma, gaps). The spatial relations between all the pairs of regions and objects are measured using a Force Histogram Decomposition. A specimen is described using a Bag of Words model aggregating the features measured on all its randomly acquired images. The grading is made using a Naive Bayes Classifier. Experiments on a 23 mice dataset with CT26 intrasplenic tumors highlight the relevance of the spatial relations with a correct grading rate of \(78.95\,\%\).
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References
Bloch, I.: Fuzzy spatial relationships for image processing and interpretation: a review. Image and Vision Computing 23(2), 89–110 (2005)
Freeman, J.: The modelling of spatial relations. Computer Graphics and Image Processing 4(2), 156–171 (1975)
Garnier, M., Hurtut, T., Ben Tahar, H., Cheriet, F.: Automatic multiresolution age-related macular degeneration detection from fundus images. In: SPIE Medical Imaging, p. 903532. International Society for Optics and Photonics (2014)
Garnier, M., Hurtut, T., Wendling, L.: Object description based on spatial relations between level-sets. In: Proc. DICTA, pp. 1–7 (2012)
Gurcan, M.N., Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M., Yener, B.: Histopathological image analysis: a review. IEEE Reviews in Biomedical Engineering 2, 147–171 (2009)
He, L., Long, L.R., Antani, S., Thoma, G.R.: Histology image analysis for carcinoma detection and grading. Computer Methods and Programs in Biomedicine 107(3), 538–556 (2012)
Heldmuth, L.O.: In vivo monitoring of elastic changes during cancer development and therapeutic treatment. PhD thesis, Université Paris Diderot (2012)
Illian, J., Penttinen, P.A., Stoyan, H., Stoyan, P.D.: Statistical Analysis and Modelling of Spatial Point Patterns. Statistics in Practice. John Wiley and Sons (2008)
Jiao, Y., Berman, H., Kiehl, T.R., Torquato, S.: Spatial organization and correlations of cell nuclei in brain tumors. PLoS One 6(11), e27323 (2011)
Lee, N., Laine, A.F., Smith, T.R.: Learning non-homogenous textures and the unlearning problem with application to drusen detection in retinal images. In: Proc. ISBI, pp. 1215–1218 (2008)
Matsakis, P., Wendling, L.: A new way to represent the relative position between areal objects. In: Proc. PAMI, vol. 21(7), pp. 634–643 (1999)
Mattfeldt, T., Eckel, S., Fleischer, F., Schmidt, V.: Statistical analysis of labelling patterns of mammary carcinoma cell nuclei on histological sections. Journal of Microscopy 235(1), 106–118 (2009)
Mikalsen, L.T.G., Dhakal, H.P., Bruland, Ø.S., Nesland, J.M., Olsen, D.R.: Quantification of angiogenesis in breast cancer by automated vessel identification in cd34 immunohistochemical sections. Anticancer Research 31(12), 4053–4060 (2011)
Ruifrok, A.C., Johnston, D.A.: Quantification of histochemical staining by color deconvolution. Analytical and Quantitative Cytology and Histology 23(4), 291–299 (2001)
Sertel, O., Kong, J., Catalyurek, U.V., Lozanski, G., Saltz, J.H., Gurcan, M.N.: Histopathological image analysis using model-based intermediate representations and color texture: Follicular lymphoma grading. Journal of Signal Processing Systems 55(1–3), 169–183 (2009)
Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: Proc. ICCV, pp. 1470–1477. IEEE (2003)
Swamidoss, I.N., Kårsnäs, A., Uhlmann, V., Ponnusamy, P., Kampf, C., Simonsson, M., Wählby, C., Strand, R.: Automated classification of immunostaining patterns in breast tissue from the human protein atlas. Journal of Pathology Informatics 4(Suppl.) (2013)
Tabbone, S., Wendling, L., Tombre, K.: Matching of graphical symbols in line-drawing images using angular signature information. Document Analysis and Recognition 6(2), 115–125 (2003)
Wai, P.Y., Mi, Z., Guo, H., Sarraf-Yazdi, S., Gao, C., Wei, J., Marroquin, C.E., Clary, B., Kuo, P.C.: Osteopontin silencing by small interfering rna suppresses in vitro and in vivo ct26 murine colon adenocarcinoma metastasis. Carcinogenesis 26(4), 741–751 (2005)
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Garnier, M., Alsheh Ali, M., Seguin, J., Mignet, N., Hurtut, T., Wendling, L. (2014). Grading Cancer from Liver Histology Images Using Inter and Intra Region Spatial Relations. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science(), vol 8815. Springer, Cham. https://doi.org/10.1007/978-3-319-11755-3_28
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DOI: https://doi.org/10.1007/978-3-319-11755-3_28
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