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A novel multithresholding algorithm for segmentation of the MRI images

By
Bhavna Kaushik Pancholi ,
Bhavna Kaushik Pancholi

The Maharaja Sayajirao University of Baroda, Vadodara, Department of Electrical Engineering, Gujarat

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Pramodkumar Sevantilal Modi ,
Pramodkumar Sevantilal Modi

The Maharaja Sayajirao University of Baroda, Vadodara, Department of Electrical Engineering, Gujarat

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Nehal Gitesh Chitaliya ,
Nehal Gitesh Chitaliya

The Maharaja Sayajirao University of Baroda, Vadodara, Department of Electrical Engineering, Gujarat

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Abstract

Segmentation is a crucial stage in picture evaluation techniques. Brain magnetic resonance imaging has been accurately segmented, extensively studied because the use of these types of methods allows the detection and recognition of a wide range of disorders. Thresholding is a simple and effective method for segmenting images. But depending on how many thresholds are employed for segmentation, thresholding-based techniques have a tendency to cost more to compute. As a result, metaheuristic algorithms are a crucial tool for multilevel thresholding that aid in determining the best values. Using a novel cuckoo search (NCS) algorithm, we have suggested a method for segmenting MRI images that is more efficient. Three different objective functions (Otsu's method, Kapur entropy, and Tsallis entropy function) were utilised by comparing the output of the projected strategy with the Cuckoo Search (CS) algorithm.

How to Cite

1.
Kaushik Pancholi B, Sevantilal Modi P, Gitesh Chitaliya N. A novel multithresholding algorithm for segmentation of the MRI images. Salud, Ciencia y Tecnología [Internet]. 2023 Jun. 14 [cited 2024 Mar. 5];3:408. Available from: https://revista.saludcyt.ar/ojs/index.php/sct/article/view/408

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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