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22 December 2015 A low dimensional entropy-based descriptor of several tissues in skin cancer histopathology samples
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Proceedings Volume 9681, 11th International Symposium on Medical Information Processing and Analysis; 968102 (2015) https://doi.org/10.1117/12.2211528
Event: 11th International Symposium on Medical Information Processing and Analysis (SIPAIM 2015), 2015, Cuenca, Ecuador
Abstract
The use of low-level visual features to assign high level labels in datasets of histopathology images is a possible solution to the problems derived from manual labeling by experts. However, in many cases, the visual cues are not enough. In this article we propose the use of features derived exclusively from the spatial distribution of the cell nuclei. These features are calculated using the weight of k-nn graphs constructed from the distances between cells. Results show that there are k values with enhanced discriminatory power, especially when comparing cancerous and non-cancerous tissue.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Pablo Álvarez, Germán Corredor, Juan D. García-Arteaga, and Eduardo Romero "A low dimensional entropy-based descriptor of several tissues in skin cancer histopathology samples", Proc. SPIE 9681, 11th International Symposium on Medical Information Processing and Analysis, 968102 (22 December 2015); https://doi.org/10.1117/12.2211528
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