Brain Tumor Segmentation using Hierarchical Combination of Fuzzy Logic and Cellular Automata.

IF 1.1 Q4 ENGINEERING, BIOMEDICAL

Journal of Medical Signals & Sensors Pub Date : 2022-07-26 eCollection Date: 2022-07-01 DOI:10.4103/jmss.jmss_128_21

Roqaie Kalantari, Roqaie Moqadam, Nazila Loghmani, Armin Allahverdy, Mohammad Bagher Shiran, Arash Zare-Sadeghi

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引用次数: 1

Abstract

Background: Magnetic resonance (MR) image is one of the most important diagnostic tools for brain tumor detection. Segmentation of glioma tumor region in brain MR images is challenging in medical image processing problems. Precise and reliable segmentation algorithms can be significantly helpful in the diagnosis and treatment planning.

Methods: In this article, a novel brain tumor segmentation method is introduced as a postsegmentation module, which uses the primary segmentation method's output as input and makes the segmentation performance values better. This approach is a combination of fuzzy logic and cellular automata (CA).

Results: The BraTS online dataset has been used for implementing the proposed method. In the first step, the intensity of each pixel is fed to a fuzzy system to label each pixel, and at the second step, the label of each pixel is fed to a fuzzy CA to make the performance of segmentation better. This step repeated while the performance saturated. The accuracy of the first step was 85.8%, but the accuracy of segmentation after using fuzzy CA was obtained to 99.8%.

Conclusion: The practical results have shown that our proposed method could improve the brain tumor segmentation in MR images significantly in comparison with other approaches.

Abstract Image

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基于模糊逻辑和元胞自动机层次组合的脑肿瘤分割。

背景:磁共振(MR)图像是脑肿瘤检测的重要诊断工具之一。脑磁共振图像中胶质瘤区域的分割是医学图像处理中的一个难题。精确可靠的分割算法对诊断和治疗计划有重要的帮助。方法:本文引入一种新的脑肿瘤分割方法作为后分割模块，将原分割方法的输出作为输入，使分割的性能值更好。这种方法是模糊逻辑和元胞自动机(CA)的结合。结果:BraTS在线数据集已被用于实现所提出的方法。第一步，将每个像素的强度馈送到模糊系统对每个像素进行标记，第二步，将每个像素的标签馈送到模糊CA以提高分割性能。在性能饱和时重复此步骤。第一步的分割准确率为85.8%，使用模糊CA后的分割准确率达到99.8%。结论:实际结果表明，与其他方法相比，我们提出的方法可以显著提高MR图像中脑肿瘤的分割效果。

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

Journal of Medical Signals & Sensors ENGINEERING, BIOMEDICAL-

CiteScore

2.30

自引率

0.00%

发文量

审稿时长

33 weeks

期刊介绍： JMSS is an interdisciplinary journal that incorporates all aspects of the biomedical engineering including bioelectrics, bioinformatics, medical physics, health technology assessment, etc. Subject areas covered by the journal include: - Bioelectric: Bioinstruments Biosensors Modeling Biomedical signal processing Medical image analysis and processing Medical imaging devices Control of biological systems Neuromuscular systems Cognitive sciences Telemedicine Robotic Medical ultrasonography Bioelectromagnetics Electrophysiology Cell tracking - Bioinformatics and medical informatics: Analysis of biological data Data mining Stochastic modeling Computational genomics Artificial intelligence & fuzzy Applications Medical softwares Bioalgorithms Electronic health - Biophysics and medical physics: Computed tomography Radiation therapy Laser therapy - Education in biomedical engineering - Health technology assessment - Standard in biomedical engineering.