Abstract
Acute Lymphoblastic Leukemia (ALL) is a cancer of the blood or bone marrow. Detection of ALL is usually done by skilled pathologists, automatic detection of leukemia will reduce the diagnosis time and will also be independent of the skills of the pathologist. In this paper, we propose using texture descriptors extracted from the nucleus image for detection of ALL. The disease causes change in the chromatin distribution of the nucleus, which can be observed in the form of texture. We have used two texture features, namely Local Binary pattern and Gray Level Co-occurrence Matrix for automatic detection of ALL. A comparative analysis of both the features is presented. It is seen that LBP features perform better than GLCM features.
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References
Haworth, C., Hepplestone, A., Jones, P. M., Campbell, R., Evans, D., & Palmer, M. P. (1981). Routine bone marrow examination in the management of acute Lymphoblastic Leukaemia of childhood. Journal of Clinical Pathology, 34, 483–485.
Asadi, M., Vahedi, A., & Amindavar, H. (2006). Leukemia cell recognition with Zernike moments of holographic images. In Proceedings of the 7th Nordic Signal Processing Symposium (NORSIG) (pp. 214–217).
Sawyers, C. L., Denny, C. T., & Witte, O. N. (1991). Leukemia and the disruption of normal hematopoiesis. Cell, 64, 337–350.
Mohapatra, S., Patra, D., & Satpathy, S. (2013). An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images. In Neural Computing and Applications (pp. 1–18).
Madhloom, H., Kareem, S., & Ariffin, H. (2012). A robust feature extraction and selection method for the recognition of Lymphocytes versus acute Lymphoblastic Leukemia. In International Conference on Advanced Computer Science Applications and Technologies (pp. 330—335).
Singhal, V., & Singh, P. (2014). Local binary pattern for detection of Acute Lymphoblastic Leukemia. In National Conference on Communication (NCC) (pp. 1–5).
Nor Hazlyna, H., Mashor, M., Mokhtar, N. R., Aimi Salihah, A., Hassan, R., Raof, R. A. A., & Osman, M. (2010). Comparison of acute leukemia Image segmentation using HSI and RGB color space. In 10th International Conference on Information Sciences Signal Processing and their Applications (ISSPA) (pp. 749–752).
Ojala, T., Pietikäinen, M., & Mäenpää, T. (2002). Multiresolution gray-scale and rotation invariant texture classi_cation with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 971–987.
Nanni, L., Lumini, A., & Brahnam, S. (2010). Local binary patterns variants as texture descriptors for medical image analysis. In US National Library of Medicine National Institutes of Health (pp. 117–125).
Haralick, R., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics, 610—621.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (pp. 1137–1143).
Labati, R., Piuri, V., & Scotti, F. (2011). All-IDB: The acute lymphoblastic leukemia image database for image processing. In 18th IEEE International Conference on Image Processing (ICIP) (pp. 2045–2048).
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: An update. SIGKDD Explorations Newsletter, 11, 10–18.
ALL-IDB. Acute Lymphoblastic Leukemia Image Database for Image Processing. http://www.dti.unimi.it/fscotti/all/results.php.
Scotti, F. (2005). Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images. In IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, (CIMSA) (pp. 96–101).
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Singhal, V., Singh, P. (2016). Texture Features for the Detection of Acute Lymphoblastic Leukemia. In: Satapathy, S., Joshi, A., Modi, N., Pathak, N. (eds) Proceedings of International Conference on ICT for Sustainable Development. Advances in Intelligent Systems and Computing, vol 409. Springer, Singapore. https://doi.org/10.1007/978-981-10-0135-2_52
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DOI: https://doi.org/10.1007/978-981-10-0135-2_52
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