NDLI: Automated Localization of Brain Tumors in MRI Using Potential-K-Means Clustering Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Cabria, I. Gondra, I.
Copyright Year	2015
Description	Author affiliation: Dept. de Fis. Teor., Univ. de Valladolid, Valladolid, Spain (Cabria, I.) \|\| Dept. of Math., Stat. & Comput. Sci., St. Francis Xavier Univ., Antigonish, NS, Canada (Gondra, I.)
Abstract	The manual localization and precise segmentation of brain tumours from magnetic resonance images (MRI) is time-consuming and error-prone. In T2 and FLAIR MRI, tumours appear as bright areas of higher signal intensity than their surroundings. In this paper we view the intensity of a pixel as equal to its "workload" and employ an unsupervised learning algorithm called potential-K-means that generates a balanced distribution of the pixels into clusters of approximately equal total intensity. The algorithm is based on an analogy with the gravitational force exerted by masses. This balancing requirement introduces a search bias that tends to generate either small clusters of higher intensity pixels, which overlap with the tumour area, or large clusters of lower intensity pixels. We evaluate the proposed algorithm on the publicly available brain tumour image segmentation (BRATS) MRI benchmark by comparing the centre of the cluster that overlaps with the tumour, with the centre of the tumour in the corresponding ground truth segmentation. We compare the proposed algorithm with the well-known K-means and with the Force clustering algorithm by Calamari et al. (2009), which follows a different Physics analogy, but it is also based on a balancing criteria. Experimental results show that K-means is not suitable for tumour localization and that potential-K-means and Kalantari's approach are comparable. However, the performance of Kalantari's approach is highly dependent on a parameter whose value needs to be set a priori, but without an informed way of doing so, which makes the present proposed method more practical.
Starting Page	125
Ending Page	132
File Size	625159
Page Count	8
File Format	PDF
ISBN	9781479919864
DOI	10.1109/CRV.2015.51
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-03
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	image processing K-means Force brain MRI gravitational potential Image segmentation tumor detection Magnetic resonance imaging Clustering algorithms potential-K-means segmentation clustering Electrostatics medical imaging Tumors Biomedical imaging
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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