Glioma detection using EHO based FLAME clustering in MR brain images

IF 3 4区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Imaging Systems and Technology Pub Date : 2023-07-03 DOI:10.1002/ima.22937

Baiju Karun, T. Arun Prasath, M. Pallikonda Rajasekaran, Rakhee Makreri

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引用次数: 0

Abstract

MRI is a popular imaging method for examining brain tumours. The ability to precisely segment tumours from MRI is absolutely essential for medical diagnostics and surgical planning. Manual tumour segmentation might be unrealistic for more comprehensive studies. Deep learning is the most widely used technique in medical diagnosis. For effective tumour dissection from brain MRI, this paper proposed a novel combination of FLAME and EHO Algorithm. FLAME is a type of clustering method that groups the most similar pixels in to a single cluster. EHO algorithm is one of the nature-inspired metaheuristic optimization algorithms based on the social herding behaviour of elephants and swimming search methods. The proposed methodology's efficiency is validated through testing on various BraTS challenge datasets. The average computational time, mean squared error, peak signal to noise ratio, tanimoto coefficient, and dice score - obtained are 23.3775 s, 0.213, 54.9669 dB, 54.6148%, and 84.053%, respectively.

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利用基于 EHO 的 FLAME 聚类技术检测 MR 脑图像中的胶质瘤

核磁共振成像是检查脑肿瘤的常用成像方法。从核磁共振成像中精确分割肿瘤的能力对于医学诊断和手术规划来说绝对是至关重要的。对于更全面的研究来说，人工分割肿瘤可能并不现实。深度学习是医学诊断中应用最广泛的技术。为了有效地从脑部核磁共振成像中解剖肿瘤，本文提出了一种新颖的 FLAME 和 EHO 算法组合。FLAME 是一种聚类方法，可将最相似的像素归为一个群组。EHO 算法是受自然启发的元启发优化算法之一，基于大象的社会性群居行为和游泳搜索方法。通过在各种 BraTS 挑战数据集上进行测试，验证了所提出方法的效率。得出的平均计算时间、均方误差、峰值信噪比、塔尼莫托系数和骰子得分分别为 23.3775 秒、0.213、54.9669 dB、54.6148% 和 84.053%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Imaging Systems and Technology 工程技术-成像科学与照相技术

CiteScore

6.90

自引率

6.10%

发文量

138

审稿时长

3 months

期刊介绍： The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals. IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging. The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered. The scope of the journal includes, but is not limited to, the following in the context of biomedical research: Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.; Neuromodulation and brain stimulation techniques such as TMS and tDCS; Software and hardware for imaging, especially related to human and animal health; Image segmentation in normal and clinical populations; Pattern analysis and classification using machine learning techniques; Computational modeling and analysis; Brain connectivity and connectomics; Systems-level characterization of brain function; Neural networks and neurorobotics; Computer vision, based on human/animal physiology; Brain-computer interface (BCI) technology; Big data, databasing and data mining.