Abstract

This chapter proposes a novel approach towards extraction of brain tumor images from T1-type magnetic resonance imaging (MRI) scan images. The algorithm includes segmentation of the scan image using a rough set-based K-means algorithm. It is followed by the use of global thresholding and morphological operations to extract an image of the tumor-affected region in the scan. This algorithm has been found to extract tumor images more accurately compared than existing algorithms.

BibTeX Citation

@Inbook{Dobe2019,
author="Dobe, Oyendrila
and Sarkar, Apurba
and Halder, Amiya",
editor="Krishna, A.N.
and Srikantaiah, K.C.
and Naveena, C.",
title="Rough K-Means and Morphological Operation-Based Brain Tumor Extraction",
bookTitle="Integrated Intelligent Computing, Communication and Security",
year="2019",
publisher="Springer Singapore",
address="Singapore",
pages="661--667",
abstract="This chapter proposes a novel approach towards extraction of brain tumor images from T1-type magnetic resonance imaging (MRI) scan images. The algorithm includes segmentation of the scan image using a rough set-based K-means algorithm. It is followed by the use of global thresholding and morphological operations to extract an image of the tumor-affected region in the scan. This algorithm has been found to extract tumor images more accurately compared than existing algorithms.",
isbn="978-981-10-8797-4",
doi="10.1007/978-981-10-8797-4_67",
url="https://doi.org/10.1007/978-981-10-8797-4_67"
}