An Automatic Recognition, Identification and Classification of Mitotic Cells for the Diagnosis of Breast Cancer Stages

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Shwetha S.V. 1,* Dharmanna L 1

1. Sri Dharmasthala Manjunatheshwara Institute of Technology, Ujire – 574 240, Visvesvaraya Technological University, Belagavi, Karnataka, India

* Corresponding author.


Received: 20 Aug. 2021 / Revised: 7 Sep. 2021 / Accepted: 25 Sep. 2021 / Published: 8 Dec. 2021

Index Terms

Histopathology, mitotic cell, Otsu segmentation, CAD, ICPR 2012 database, Bit Plane slicing


The identification of breast cancer stages plays a vital role for understanding the aggressiveness of cancer disease and the patient survival as an outcome. The main parameter of breast cancer staging is counting the mitotic cells in biopsy samples of breast cancer tissues. In the present scenario the manually counting of the mitotic cells in histopathology image slides of the tissue examined by the expert under clinical microscope is 10X, 20X ,40X ,100X,400X magnification of the sample. The manual process is laborious, inaccurate, erroneous and tedious, hence the traditional method demands the computerized approach to recognize and identify the cancer stages for the expert to come up with robust decision. In this work we proposed a novel approach for automatic recognition and identification through computer aided diagnosis systems (CAD). In this CAD proposed model the work is divided into five stages. In the first stage histopathological image are preprocessed to enhance the contrast of the mitotic cells and non mitotic cells using image adjustment technique. In second stage the foreground and background is segmented using Otsu segmentation algorithm. In the third stage the Bit plane slicing is applied to separate the mitotic and non mitotic cells. In the fourth stage the number of mitotic cells is counted in the samples. In the fifth stage of the work, based on the number of mitotic cells the cancer stages are determined. In this work, ICPR 2012 database images are adopted for the experimentation. The diagnosis of the stage of the cancer will help the oncologist to take proper decision and also reduces the burden of the work.

Cite This Paper

Shwetha S.V., Dharmanna L., "An Automatic Recognition, Identification and Classification of Mitotic Cells for the Diagnosis of Breast Cancer Stages", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.13, No.6, pp. 1-11, 2021. DOI: 10.5815/ijigsp.2021.06.01


[1]Wang, Haibo, et al. "Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features." Journal of Medical Imaging 1.3 (2014): 034003.

[2]Albayrak, Abdulkadir, and Gokhan Bilgin. "Mitosis detection using convolutional neural network based features." 2016 IEEE 17th International Symposium on Computational Intelligence and Informatics (CINTI). IEEE, 2016.

[3]Myung Jae Lim, et. al. , “Deep Convolution Neural Networks for Medical Image Analysis”,International Journal of Engineering & Technology,7(3.33)(2018)115-119.

[4]Tashk, Ashkan, et al. "Automatic detection of breast cancer mitotic cells based on the combination of textural, statistical and innovative mathematical features." Applied Mathematical Modelling 39.20 (2015): 6165-6182.

[5]R.Geetha et. al.,”Automated Mitotic Cell detection and classification for Breast Cancer Histopathological Images”,international Journal of Engineering  and Advanced Technology,(IJEAT),ISSN:2249-8958,Volume -8,Issue -2S,December 2018.

[6]Lu, Cheng, and Mrinal Mandal. "Toward automatic mitotic cell detection and segmentation in multispectral histopathological images." IEEE Journal of Biomedical and Health Informatics 18.2 (2013): 594-605.

[7]Jian, Tan Xiao, et al. "Segmentation Based Classification for Mitotic Cells Detection on Breast Histopathological Images." Journal of Telecommunication, Electronic and Computer Engineering (JTEC) 10.1-16 (2018): 1-4.

[8]Phinyomark, A., et al. "Texture analysis of breast cancer cells in microscopic images using critical exponent analysis method." Procedia Engineering 32 (2012): 232-238.

[9]Gupta, Kanika, et al. "Sonographic features of invasive ductal breast carcinomas predictive of malignancy grade." The Indian journal of radiology & imaging 28.1 (2018): 123.

[10]Sankar, Deepa, and Tessamma Thomas. "Fractal features based on differential box counting method for the categorization of digital mammograms." Journal of Computer Information Systems and Industrial Management Applications 2 (2010): 11-19.

[11]Hassan K. Albahadily,V. Yu.Tsviatkou,V.K.Kanapelka, “Grayscale Image Compression using Bit Plane Slicing and developed RLE algorithm”, International Journal of Advanced Research in Computer & Communication Engineering,vol. 6 ,Issue-12,ISSN:2278-1021,pp:309-314,Feb 2017

[12]Ms. Shwetha S V, Dr. Dharmanna L and Dr. Basavaraj S Anami ,presented the Research Paper entitled "Design & analysis of algorithm for the enhancement of breast tumor images" at International Conference on Recent Development on Robotics, Embedded and Internet of Things(ICRDREIOT2020) held on 16th and 17th October 2020(Track 1,17th October),CIT,Chennai-600069.

[13]Ms. Shwetha S V, Dr. Dharmanna L and Dr. Basavaraj S Anami , ”Design and Analysis of an Algorithm for Breast Tumor Segmentation in Mammogram and Ultrasound Images”, IJCTE, ISSN: 1793-8201.

[14]Pushpam Kumar Sinha. " Modifying one of the Machine Learning Algorithms kNN to Make it Independent of the Parameter k by Re-defining Neighbor ", International Journal of Mathematical Sciences and Computing, Vol.6, No.4, pp.12-25, 2020.

[15]Hanan A. Al-Jubouri,”Integration Colour and Texture Features for Content-based Image Retrieval, International Journal of Modern Education and Computer Science, Vol.12, No.2, pp. 10-18, 2020.

[16]Kuldip Acharya, Dibyendu Ghoshal, " Central Moment and Multinomial Based Sub Image Clipped Histogram Equalization for Image Enhancement", International Journal of Image, Graphics and Signal Processing, Vol.13, No.1, pp. 1-12, 2021.

[17]Avijit Kumar Chaudhuri, Dilip K. Banerjee, Anirban Das, "A Dataset Centric Feature Selection and Stacked Model to Detect Breast Cancer", International Journal of Intelligent Systems and Applications, Vol.13, No.4, pp.24-37, 2021.