Unsupervised Detection of Fibrosis in Microscopy Images Using Fractals and Fuzzy c-Means Clustering

Abstract : The advances in improved fluorescent probes and better cameras in collaboration with the advent of computers in imaging and image analysis, assist the task of diagnosis in many fields of biologic and medical research. In this paper, we introduce a computer-assisted image characterization tool based on a Fuzzy clustering method for the quantification of degree of Idiopathic Pulmonary Fibrosis (IPF) in medical images. The implementation of this algorithmic strategy is very promising concerning the issue of the automated assessment of microscopic images of lung fibrotic regions.
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Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-381 (Part I), pp.385-394, 2012, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-33409-2_40〉
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S. Tasoulis, Ilias Maglogiannis, V. Plagianakos. Unsupervised Detection of Fibrosis in Microscopy Images Using Fractals and Fuzzy c-Means Clustering. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-381 (Part I), pp.385-394, 2012, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-33409-2_40〉. 〈hal-01521436〉

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