A probabilistic meta-heuristic optimisation algorithm for image multi-level thresholding

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".

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.


Papers:

Date: 2024/01/26
Time: 14:00
Location: C258 & online

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