3D-MRI Brain Tumor Detection Model Using Modified Version of Level Set Segmentation Based on Dragonfly Algorithm

Abstract

Accurate brain tumor segmentation from 3D Magnetic Resonance Imaging (3D-MRI) is an important method for obtaining information required for diagnosis and disease therapy planning. Variation in the brain tumor’s size, structure, and form is one of the main challenges in tumor segmentation, and selecting the initial contour plays a significant role in reducing the segmentation error and the number of iterations in the level set method. To overcome this issue, this paper suggests a two-step dragonfly algorithm (DA) clustering technique to extract initial contour points accurately. The brain is extracted from the head in the preprocessing step, then tumor edges are extracted using the two-step DA, and these extracted edges are used as an initial contour for the MRI sequence. Lastly, the tumor region is extracted from all volume slices using a level set segmentation method. The results of applying the proposed technique on 3D-MRI images from the multimodal brain tumor segmentation challenge (BRATS) 2017 dataset show that the proposed method for brain tumor segmentation is comparable to the state-of-the-art methods.

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“…The best accuracy was 95.56%. Khalil et al (2020) proposed to extract the tumor edges from 3D MRIs with a dragonfly algorithm. Then, they employed a level set segmentation algorithm to get the tumor regions based on the edges.…”

An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

“…This algorithm has special characteristics including the low number of setting parameters, compatible with continuous and discrete optimizations, and access to local and global operators of the search. The movement of each dragonfly is influenced by five factors, which are explained by the following [33,34].…”

Brain Image Segmentation in Recent Years: A Narrative Review

“…The combination of the contour-based and machine learning approach can improve initialization parameters, perform further spatial constraints, direct the evolution of intensitybased pipelines, and enhance data mining algorithms by refining the process. There are several previous studies [46][47][48][49] that were conducted based on this sub-category.…”

“…Another recent work by Khalil et al [49] adapted the dragonfly algorithm (DA) to perform a clustering-based contouring approach for brain tumor segmentation. First, the two-step DA-based clustering was used to extract tumor edge as initial tumor contour for the MR image sequence.…”