Journal Article
. 2013 Jun; 4(Suppl):S12.
doi: 10.4103/2153-3539.109870.

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

Humayun Irshad 1 Sepehr Jalali  Ludovic Roux  Daniel Racoceanu  Lim Joo Hwee  Gilles Le Naour  Frédérique Capron  
  • PMID: 23766934
  •     6 References
  •     13 citations


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.

Keywords: Classification; Hierarchical Model and X; Scale-invariant feature transform; histopathology; mitosis detection; texture analysis.

Histopathological image analysis: a review.
Metin N Gurcan, Laura E Boucheron, +3 authors, B Yener.
IEEE Rev Biomed Eng, 2009 Jan 01; 2. PMID: 20671804    Free PMC article.
Highly Cited. Review.
Computer-aided prognosis of neuroblastoma: detection of mitosis and karyorrhexis cells in digitized histological images.
Olcay Sertel, Umit V Catalyurek, Hiroyuki Shimada, Metin N Gurcan.
Annu Int Conf IEEE Eng Med Biol Soc, 2009 Dec 08; 2009. PMID: 19963746
Histological grading and prognosis in breast cancer; a study of 1409 cases of which 359 have been followed for 15 years.
Br J Cancer, 1957 Sep 01; 11(3). PMID: 13499785    Free PMC article.
Highly Cited.
Multi-resolution graph-based analysis of histopathological whole slide images: application to mitotic cell extraction and visualization.
Vincent Roullier, Olivier Lézoray, Vinh-Thong Ta, Abderrahim Elmoataz.
Comput Med Imaging Graph, 2011 May 24; 35(7-8). PMID: 21600733
Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up.
C W Elston, I O Ellis.
Histopathology, 1991 Nov 01; 19(5). PMID: 1757079
Highly Cited.
The problems and promise of central pathology review: development of a standardized procedure for the Children's Oncology Group.
Lisa A Teot, Richard Sposto, +4 authors, Children's Oncology Group.
Pediatr Dev Pathol, 2007 May 31; 10(3). PMID: 17535088
Automated mitosis detection in histopathology using morphological and multi-channel statistics features.
Humayun Irshad.
J Pathol Inform, 2013 Jul 17; 4. PMID: 23858385    Free PMC article.
A Novel CAD System for Mitosis detection Using Histopathology Slide Images.
Ashkan Tashk, Mohammad Sadegh Helfroush, Habibollah Danyali, Mojgan Akbarzadeh.
J Med Signals Sens, 2014 Apr 25; 4(2). PMID: 24761378    Free PMC article.
Automated discrimination of lower and higher grade gliomas based on histopathological image analysis.
Hojjat Seyed Mousavi, Vishal Monga, Ganesh Rao, Arvind U K Rao.
J Pathol Inform, 2015 Apr 04; 6. PMID: 25838967    Free PMC article.
Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features.
Haibo Wang, Angel Cruz-Roa, +6 authors, Anant Madabhushi.
J Med Imaging (Bellingham), 2015 Jul 15; 1(3). PMID: 26158062    Free PMC article.
Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images.
Jun Xu, Lei Xiang, +4 authors, Anant Madabhushi.
IEEE Trans Med Imaging, 2015 Jul 25; 35(1). PMID: 26208307    Free PMC article.
Highly Cited.
Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study.
Ezgi Mercan, Selim Aksoy, +3 authors, Joann G Elmore.
J Digit Imaging, 2016 Mar 11; 29(4). PMID: 26961982    Free PMC article.
Image Montaging for Creating a Virtual Pathology Slide: An Innovative and Economical Tool to Obtain a Whole Slide Image.
Spoorthi Ravi Banavar, Prashanthi Chippagiri, +2 authors, Premalatha Bidadi Rajashekaraiah.
Anal Cell Pathol (Amst), 2016 Oct 18; 2016. PMID: 27747147    Free PMC article.
Computer-based image analysis in breast pathology.
Ziba Gandomkar, Patrick C Brennan, Claudia Mello-Thoms.
J Pathol Inform, 2017 Jan 10; 7. PMID: 28066683    Free PMC article.
An alternative reference space for H&E color normalization.
Mark D Zarella, Chan Yeoh, David E Breen, Fernando U Garcia.
PLoS One, 2017 Mar 30; 12(3). PMID: 28355298    Free PMC article.
Maximized Inter-Class Weighted Mean for Fast and Accurate Mitosis Cells Detection in Breast Cancer Histopathology Images.
Ramin Nateghi, Habibollah Danyali, Mohammad Sadegh Helfroush.
J Med Syst, 2017 Aug 16; 41(9). PMID: 28808813
A Multi-Classifier System for Automatic Mitosis Detection in Breast Histopathology Images Using Deep Belief Networks.
K Sabeena Beevi, Madhu S Nair, G R Bindu.
IEEE J Transl Eng Health Med, 2017 Oct 12; 5. PMID: 29018640    Free PMC article.
Deep Learning for Semantic Segmentation vs. Classification in Computational Pathology: Application to Mitosis Analysis in Breast Cancer Grading.
Gabriel Jiménez, Daniel Racoceanu.
Front Bioeng Biotechnol, 2019 Jul 10; 7. PMID: 31281813    Free PMC article.
Machine learning-assisted imaging analysis of a human epiblast model.
Agnes M Resto Irizarry, Sajedeh Nasr Esfahani, +3 authors, Jianping Fu.
Integr Biol (Camb), 2021 Jul 31; 13(9). PMID: 34327532    Free PMC article.