Segmentation of Brain MRI using an Altruistic Harris Hawks' Optimization
algorithm
- URL: http://arxiv.org/abs/2109.08688v1
- Date: Fri, 17 Sep 2021 17:51:34 GMT
- Title: Segmentation of Brain MRI using an Altruistic Harris Hawks' Optimization
algorithm
- Authors: Rajarshi Bandyopadhyay, Rohit Kundu, Diego Oliva, Ram Sarkar
- Abstract summary: An efficient segmentation of brain Magnetic Resonance Images (MRIs) is of prime concern to radiologists.
Thresholding is a popular method for segmentation that uses the histogram of an image to label different homogeneous groups of pixels into different classes.
In this paper, we perform the multi-level thresholding using an evolutionary metaheuristic.
- Score: 29.895517914678816
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Segmentation is an essential requirement in medicine when digital images are
used in illness diagnosis, especially, in posterior tasks as analysis and
disease identification. An efficient segmentation of brain Magnetic Resonance
Images (MRIs) is of prime concern to radiologists due to their poor
illumination and other conditions related to de acquisition of the images.
Thresholding is a popular method for segmentation that uses the histogram of an
image to label different homogeneous groups of pixels into different classes.
However, the computational cost increases exponentially according to the number
of thresholds. In this paper, we perform the multi-level thresholding using an
evolutionary metaheuristic. It is an improved version of the Harris Hawks
Optimization (HHO) algorithm that combines the chaotic initialization and the
concept of altruism. Further, for fitness assignment, we use a hybrid objective
function where along with the cross-entropy minimization, we apply a new
entropy function, and leverage weights to the two objective functions to form a
new hybrid approach. The HHO was originally designed to solve numerical
optimization problems. Earlier, the statistical results and comparisons have
demonstrated that the HHO provides very promising results compared with
well-established metaheuristic techniques. In this article, the altruism has
been incorporated into the HHO algorithm to enhance its exploitation
capabilities. We evaluate the proposed method over 10 benchmark images from the
WBA database of the Harvard Medical School and 8 benchmark images from the
Brainweb dataset using some standard evaluation metrics.
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