UH Biocomputation Group - Image segmentationhttp://biocomputation.herts.ac.uk/2024-01-22T14:06:02+00:00A probabilistic meta-heuristic optimisation algorithm for image multi-level thresholding2024-01-22T14:06:02+00:002024-01-22T14:06:02+00:00Mohammad Tayaraninajarantag:biocomputation.herts.ac.uk,2024-01-22:/2024/01/22/a-probabilistic-meta-heuristic-optimisation-algorithm-for-image-multi-level-thresholding.html<p class="first last">Mohammad Tayaraninajaran's Journal Club session where he will talk about his paper "A probabilistic meta-heuristic optimisation algorithm for image multi-level thresholding".</p>
<p>On this week's Journal Club session, Mohammad Tayaraninajaran will talk about his paper "A probabilistic meta-heuristic optimisation algorithm for image multi-level thresholding".</p>
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<p>The spread of the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) which
causes CoronaVirus Disease 2019 (COVID-19) has challenged many countries. To curb the
effect of the pandemic requires the development of low-cost and rapid tools for detecting
and diagnosing the patients. In this regard, chest X-ray scan images provide a reliable
way of detecting the patients. One limitation, however, is the need for experts to analyse
the images and identify the cases which can be a burden, when a large number of images are
to be processed. The aim of this paper is to propose a method to extract rapidly, from the
X-ray images, the regions in which there exist indications of COVID-19 infection. To
identify the regions, image segmentation is required which is performed in this paper with
a novel optimization algorithm. The proposed optimization algorithm uses probabilistic
representation for the solutions. To improve the optimization process, we propose a
diversity preserving operator. For multi-level image thresholding via optimization
algorithms, different fitness functions have been proposed in the literature. In the
proposed method in this paper, we use three fitness functions to benefit from the
advantages of all. A fitness swapping scheme is proposed which swaps between the fitness
functions in the optimization process. Also, a diversity preserving operator is proposed
in this paper which compares the individuals and reinitializes the similar ones to inject
diversity in the population. The proposed algorithm is tested on a number of COVID-19
benchmark images and experimental analysis suggest better performance for the proposed
algorithm.</p>
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<p>Papers:</p>
<ul class="simple">
<li>M. Najaran, <a class="reference external" href="https://doi.org/10.1007/s10710-023-09460-4">"A probabilistic meta-heuristic optimisation algorithm for image multi-level thresholding"</a>, 2023, Genetic Programming and Evolvable Machines, 24, 14</li>
</ul>
<p><strong>Date:</strong> 2024/01/26 <br />
<strong>Time:</strong> 14:00 <br />
<strong>Location</strong>: C258 & online</p>