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首页> 美国卫生研究院文献>International Journal of Biomedical Imaging >Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM
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Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM

机译：基于生物启发的BWT和SVM的基于MRI的脑肿瘤检测和特征提取的图像分析

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The segmentation, detection, and extraction of infected tumor area from magnetic resonance (MR) images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated Berkeley wavelet transformation (BWT) based brain tumor segmentation. Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM) based classifier, relevant features are extracted from each segmented tissue. The experimental results of proposed technique have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 96.51% accuracy, 94.2% specificity, and 97.72% sensitivity, demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 0.82 dice similarity index coefficient, which indicates better overlap between the automated (machines) extracted tumor region with manually extracted tumor region by radiologists. The simulation results prove the significance in terms of quality parameters and accuracy in comparison to state-of-the-art techniques.
机译：从磁共振（MR）图像中分割，检测和提取受感染的肿瘤区域是一个主要问题，但是放射科医生或临床专家执行的繁琐且耗时的任务，其准确性仅取决于其经验。因此，使用计算机辅助技术来克服这些限制变得非常必要。在这项研究中，为了提高医学图像分割过程的性能并降低其复杂性，我们研究了基于伯克利小波变换（BWT）的脑肿瘤分割方法。此外，为了提高基于支持向量机（SVM）的分类器的准确性和质量率，从每个分割的组织中提取相关特征。基于准确性，敏感性，特异性和骰子相似性指数系数，对所提出技术的实验结果进行了评估和验证，以用于磁共振脑图像的性能和质量分析。实验结果达到了96.51％的准确度，94.2％的特异性和97.72％的灵敏度，证明了所提出的从大脑MR图像识别正常和异常组织的技术的有效性。实验结果还获得了平均0.82个骰子相似性指数系数，这表明放射线医生自动（机器）提取的肿瘤区域与手动提取的肿瘤区域之间有更好的重叠。与最新技术相比，仿真结果证明了质量参数和准确性的重要性。

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