Journal of Pathology Informatics

SYMPOSIUM - ORIGINAL RESEARCH
Year
: 2013  |  Volume : 4  |  Issue : 2  |  Page : 12-

Automated mitosis detection using texture, SIFT features and HMAX biologically inspired approach


Humayun Irshad1, Sepehr Jalali2, Ludovic Roux1, Daniel Racoceanu3, Lim Joo Hwee4, Gilles Le Naour5, Frédérique Capron5 
1 University of Joseph Fourier, Grenoble, France
2 National University of Singapore, Singapore
3 University Pierre and Marie Curie, France
4 Institute of Infocomm Research (I2R), Singapore
5 Pitié Salpêtrière Hospital, Paris, France

Correspondence Address:
Humayun Irshad
University of Joseph Fourier, Grenoble
France

Context: According to Nottingham grading system, mitosis count in breast cancer histopathology is one of three components required for cancer grading and prognosis. Manual counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. Aims: The aim is to investigate the various texture features and Hierarchical Model and X (HMAX) biologically inspired approach for mitosis detection using machine-learning techniques. Materials and Methods: We propose an approach that assists pathologists in automated mitosis detection and counting. The proposed method, which is based on the most favorable texture features combination, examines the separability between different channels of color space. Blue-ratio channel provides more discriminative information for mitosis detection in histopathological images. Co-occurrence features, run-length features, and Scale-invariant feature transform (SIFT) features were extracted and used in the classification of mitosis. Finally, a classification is performed to put the candidate patch either in the mitosis class or in the non-mitosis class. Three different classifiers have been evaluated: Decision tree, linear kernel Support Vector Machine (SVM), and non-linear kernel SVM. We also evaluate the performance of the proposed framework using the modified biologically inspired model of HMAX and compare the results with other feature extraction methods such as dense SIFT. Results: The proposed method has been tested on Mitosis detection in breast cancer histological images (MITOS) dataset provided for an International Conference on Pattern Recognition (ICPR) 2012 contest. The proposed framework achieved 76% recall, 75% precision and 76% F-measure. Conclusions: Different frameworks for classification have been evaluated for mitosis detection. In future work, instead of regions, we intend to compute features on the results of mitosis contour segmentation and use them to improve detection and classification rate.


How to cite this article:
Irshad H, Jalali S, Roux L, Racoceanu D, Hwee LJ, Naour GL, Capron F. Automated mitosis detection using texture, SIFT features and HMAX biologically inspired approach.J Pathol Inform 2013;4:12-12


How to cite this URL:
Irshad H, Jalali S, Roux L, Racoceanu D, Hwee LJ, Naour GL, Capron F. Automated mitosis detection using texture, SIFT features and HMAX biologically inspired approach. J Pathol Inform [serial online] 2013 [cited 2019 Dec 14 ];4:12-12
Available from: http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2013;volume=4;issue=2;spage=12;epage=12;aulast=Irshad;type=0