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An Automated Approach towards Detection of Mitosis in Histopathological Images

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Anand Raj Ulle, T. N. Nagabhushan, Nandini Manoli

Anand Raj Ulle, T N Nagabhushan and Nandini Manoli. An Automated Approach towards Detection of Mitosis in Histopathological Images. International Journal of Computer Applications 180(35):1-7, April 2018. BibTeX

	author = {Anand Raj Ulle and T. N. Nagabhushan and Nandini Manoli},
	title = {An Automated Approach towards Detection of Mitosis in Histopathological Images},
	journal = {International Journal of Computer Applications},
	issue_date = {April 2018},
	volume = {180},
	number = {35},
	month = {Apr},
	year = {2018},
	issn = {0975-8887},
	pages = {1-7},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2018916886},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Generally, the grade of a breast cancer is considered as an ”aggressive potential” in the growth of a tumor. Breast cancer grading is characterized by three important factors, gland formation, nuclear pleomorphism, and mitosis count. In this research, an automated detection of mitosis from histopathological images is presented. From initial experiments, it has been observed that detection of mitosis becomes challenging, due to the similarity in size and shape compared to nonmitosis nuclei. Towards this end, several contributions have been made to automatically detect mitosis nuclei. From an Exhaustive experimentation, it is clear that mitotic texture shows discriminative features when compared to nonmitotic nuclei. To validate the performance of mitosis detection, two datasets from the MITOSIS-ATYPIA-14 challenge is considered. The proposed method is able to achieve 97% overall accuracy after feature reduction


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Histopathological Images, Mitosis, Texture features, Patch extraction, Digital Pathology