Reading: Un-supervised segmentation and quantisation of malignancy from breast MRI images

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Un-supervised segmentation and quantisation of malignancy from breast MRI images

Authors:

Rajeev Ratan ,

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About Rajeev
Department of Electronics and Communication Engineering, MVN University, Palwal, Haryana, India.
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PG Kohli,

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About PG

Department of Physiology, Punjab Institute of Medical Sciences, Jalandhar, India.

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Sanjay K Sharma,

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About Sanjay K

Department of Electronics and Communication Engineering, Thapar University, Patiala, Punjab, India.

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Amit K Kohli

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About Amit K

Department of Electronics and Communication Engineering, Thapar University, Patiala, Punjab, India.

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Abstract

In this paper, a magnetic resonance imaging (MRI) based image segmentation technique has been proposed, which uses a magnetic resonance parametric information model for breast tumor segmentation. The methodology has been developed on two dimensional MRI datasets. With the help of the proposed technique, breast tumor tissues can be segmented in 6 - 8 minutes with more precision and reproducibility than manual (supervised) segmentation, which takes more than two hours to segment breast tumor tissues. Thus, the proposed semi-automatic (un-supervised) technique can be applied to analyse MRI images, which improves the procedure for diagnosing breast cancer, and it can also be used to generate two-dimensional view of tumor in case of surgical operations. 

How to Cite: Ratan, R. et al., (2016). Un-supervised segmentation and quantisation of malignancy from breast MRI images. Journal of the National Science Foundation of Sri Lanka. 44(4), pp.437–442. DOI: http://doi.org/10.4038/jnsfsr.v44i4.8026
Published on 27 Dec 2016.
Peer Reviewed

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