Jaya算法指导脑MRI肿瘤分割

IF 1 Q3 ENGINEERING, MULTIDISCIPLINARY
S. Satapathy, V. Rajinikanth
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引用次数: 45

摘要

脑异常是造成人类社会主要危险因素之一,发病率较高。早期发现肿瘤对于提供必要的治疗程序以挽救患者至关重要。本文采用Jaya算法(JA)和Otsu 's Function (OF)引导方法对Flair和T2模态记录的脑MRI不规则切片进行挖掘。这项工作实现了两步的过程,从二维(2D) MRI切片的轴向,矢状面和冠状面检查脑肿瘤。本文详细评价了不同阈值水平(Th=2,3,4,5)的阈值分割过程,阈值分割前后的颅骨剥离过程,以及基于Chan-Vese方法的肿瘤提取。在文献中发现的其他突出的启发式方法中,JA的优越性得到了证实。实施的研究结果证实,Jaya算法指导方法能够在BRATS 2015数据集上呈现优越的Jaccard-Index、Dice-Coefficient、灵敏度、特异性、准确度和精密度值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Jaya Algorithm Guided Procedure to Segment Tumor from Brain MRI
Brain abnormality is a cause for the chief risk factors in human society with larger morbidity rate. Identification of tumor in its early stage is essential to provide necessary treatment procedure to save the patient. In this work, Jaya Algorithm (JA) and Otsu’s Function (OF) guided method is presented to mine the irregular section of brain MRI recorded with Flair and T2 modality. This work implements a two-step process to examine the brain tumor from the axial, sagittal, and coronal views of the two-dimensional (2D) MRI slices. This paper presents a detailed evaluation of thresholding procedure with varied threshold levels (Th=2,3,4,5), skull stripping process before/after the thresholding practice, and the tumor extraction based on the Chan-Vese approach. Superiority of JA is confirmed among other prominent heuristic approaches found in literature. The outcome of implemented study confirms that Jaya Algorithm guided method is capable of presenting superior values of Jaccard-Index, Dice-Coefficient, sensitivity, specificity, accuracy, and precision on the BRATS 2015 dataset.
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来源期刊
Journal of Optimization
Journal of Optimization ENGINEERING, MULTIDISCIPLINARY-
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