Journal Article
. 2014 Apr; 4(2):139-49.

A Novel CAD System for Mitosis detection Using Histopathology Slide Images

Ashkan Tashk 1 Mohammad Sadegh Helfroush 1 Habibollah Danyali 1 Mojgan Akbarzadeh 2 
  • PMID: 24761378
  •     14 References
  •     1 citations


Histopathology slides are one of the most applicable resources for pathology studies. As observation of these kinds of slides even by skillful pathologists is a tedious and time-consuming activity, computerizing this procedure aids the experts to have faster analysis with more case studies per day. In this paper, an automatic mitosis detection system (AMDS) for breast cancer histopathological slide images is proposed. In the proposed AMDS, the general phases of an automatic image based analyzer are considered and in each phase, some special innovations are employed. In the pre-processing step to segment the input digital histopathology images more precisely, 2D anisotropic diffusion filters are applied to them. In the training segmentation phase, the histopathological slide images are segmented based on RGB contents of their pixels using maximum likelihood estimation. Then, the mitosis and non-mitosis candidates are processed and hence that their completed local binary patterns are extracted object-wise. For the classification phase, two subsequently non-linear support vector machine classifiers are trained pixel-wise and object-wise, respectively. For the evaluation of the proposed AMDS, some object and region based measures are employed. Having computed the evaluation criteria, our proposed method performs more efficient according to f-measure metric (70.94% for Aperio XT scanner images and 70.11% for Hamamatsu images) than the methods proposed by other participants at Mitos-ICPR2012 contest in breast cancer histopathological images. The experimental results show the higher performance of the proposed AMDS compared with other competitive systems proposed in Mitos-ICPR2012 contest.

Keywords: Automatic mitosis detection system; completed local binary pattern; histopathology; maximum likelihood estimation; object- and pixel-wise feature extraction; support vector machine.

Other Links

Free PMC article 
Automated mitosis detection using texture, SIFT features and HMAX biologically inspired approach.
Humayun Irshad, Sepehr Jalali, +4 authors, Frédérique Capron.
J Pathol Inform, 2013 Jun 15; 4(Suppl). PMID: 23766934    Free PMC article.
Mitosis detection in breast cancer histology images with deep neural networks.
Dan C Cireşan, Alessandro Giusti, Luca M Gambardella, Jürgen Schmidhuber.
Med Image Comput Comput Assist Interv, 2014 Mar 01; 16(Pt 2). PMID: 24579167
Highly Cited.
Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer.
Sokol Petushi, Fernando U Garcia, +2 authors, Aydin Tozeren.
BMC Med Imaging, 2006 Oct 31; 6. PMID: 17069651    Free PMC article.
Mitosis detection using generic features and an ensemble of cascade adaboosts.
F Boray Tek.
J Pathol Inform, 2013 Jul 17; 4. PMID: 23858387    Free PMC article.
Automated mitosis detection of stem cell populations in phase-contrast microscopy images.
Seungil Huh, Dai Fei Elmer Ker, +2 authors, Takeo Kanade.
IEEE Trans Med Imaging, 2011 Mar 02; 30(3). PMID: 21356609
A gamma-gaussian mixture model for detection of mitotic cells in breast cancer histopathology images.
Adnan Mujahid Khan, Hesham Eldaly, Nasir M Rajpoot.
J Pathol Inform, 2013 Jul 17; 4. PMID: 23858386    Free PMC article.
Classification of mitotic figures with convolutional neural networks and seeded blob features.
Christopher D Malon, Eric Cosatto.
J Pathol Inform, 2013 Jul 17; 4. PMID: 23858384    Free PMC article.
A framework for evaluating image segmentation algorithms.
Jayaram K Udupa, Vicki R Leblanc, +5 authors, James Woodburn.
Comput Med Imaging Graph, 2006 Apr 06; 30(2). PMID: 16584976
Histology image analysis for carcinoma detection and grading.
Lei He, L Rodney Long, Sameer Antani, George R Thoma.
Comput Methods Programs Biomed, 2012 Mar 23; 107(3). PMID: 22436890    Free PMC article.
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
A completed modeling of local binary pattern operator for texture classification.
Zhenhua Guo, Lei Zhang, David Zhang.
IEEE Trans Image Process, 2010 Mar 11; 19(6). PMID: 20215079
Method for counting mitoses by image processing in Feulgen stained breast cancer sections.
T K ten Kate, J A Beliën, A W Smeulders, J P Baak.
Cytometry, 1993 Jan 01; 14(3). PMID: 8472602
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.
Morphometric analysis of white matter lesions in MR images: method and validation.
A P Zijdenbos, B M Dawant, R A Margolin, A C Palmer.
IEEE Trans Med Imaging, 1994 Jan 01; 13(4). PMID: 18218550
Highly Cited.
Influence of Texture and Colour in Breast TMA Classification.
M Milagro Fernández-Carrobles, Gloria Bueno, +3 authors, Lucía González-López.
PLoS One, 2015 Oct 30; 10(10). PMID: 26513238    Free PMC article.