TY - GEN
T1 - SVM-based framework for the robust extraction of objects from histopathological images using color, texture, scale and geometry
AU - Veillard, Antoine
AU - Bressan, Stephane
AU - Racoceanu, Daniel
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - breast cancer grading
KW - computer vision
KW - digital histopathology
KW - marked point process
KW - object detection and extraction
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=84873591190&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2012.21
DO - 10.1109/ICMLA.2012.21
M3 - Conference contribution
AN - SCOPUS:84873591190
SN - 9780769549132
T3 - Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
SP - 70
EP - 75
BT - Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
T2 - 11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012
Y2 - 12 December 2012 through 15 December 2012
ER -