Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICMLA.2012.21
Title: SVM-based framework for the robust extraction of objects from histopathological images using color, texture, scale and geometry
Authors: Veillard, A.
Bressan, S. 
Racoceanu, D. 
Keywords: breast cancer grading
computer vision
digital histopathology
marked point process
object detection and extraction
support vector machine
Issue Date: 2012
Source: Veillard, A.,Bressan, S.,Racoceanu, D. (2012). SVM-based framework for the robust extraction of objects from histopathological images using color, texture, scale and geometry. Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012 1 : 70-75. ScholarBank@NUS Repository. https://doi.org/10.1109/ICMLA.2012.21
Abstract: The extraction of nuclei from Haematoxylin and Eosin (H&E) stained biopsies present a particularly steep challenge in part due to the irregularity of the high-grade (most malignant) tumors. To your best knowledge, although some existing solutions perform adequately with relatively predictable low-grade cancers, solutions for the problematic high-grade cancers have yet to be proposed. In this paper, we propose a method for the extraction of cell nuclei from H&E stained biopsies robust enough to deal with the full range of histological grades observed in daily clinical practice. The robustness is achieved by combining a wide range of information including color, texture, scale and geometry in a multi-stage, Support Vector Machine (SVM) based framework to replace the original image with a new, probabilistic image modality with stable characteristics. The actual extraction of the nuclei is performed from the new image using Mark Point Processes (MPP), a state-of-the-art stochastic method. An empirical evaluation on clinical data provided and annotated by pathologists shows that our method greatly improves detection and extraction results, and provides a reliable solution with high grade cancers. Moreover, our method based on machine learning can easily adapt to specific clinical conditions. In many respects, our method contributes to bridging the gap between the computer vision technologies and their actual clinical use for breast cancer grading. © 2012 IEEE.
Source Title: Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
URI: http://scholarbank.nus.edu.sg/handle/10635/41705
ISBN: 9780769549132
DOI: 10.1109/ICMLA.2012.21
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