Histopathological analysis is crucial for the diagnosis of a large number of cancer types. A lot of progress has been made in the development of molecular based assays, but many of the cases still require the careful analysis of the stained tissue under a bright-field microscope and its analysis. This procedure is costly and time-consuming. We present a novel method for classification of cancer cells in lymph node images. It is based on the measurement of the spectral image of hematoxylin and eosin stained sample under the microscope and the analysis of the acquired data using state of the art machine learning techniques. The method is based on the analysis of the spectral information of the cells as well as their morphological properties. A large number of descriptors is extracted for each cell location, which are used to train a supervised classifier which discriminates between normal and cancer cells. We show that a reliable analysis can be made with detection rate (recall) of 81%-100% for the cancer class.
|Title of host publication
|2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 13 Oct 2016
|38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States
Duration: 16 Aug 2016 → 20 Aug 2016
|Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
|38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
|16/08/16 → 20/08/16
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© 2016 IEEE.