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An efficient computerized decision support system for the analysis and 3D visualization of brain tumor

机译：用于脑肿瘤分析和3D可视化的高效计算机决策支持系统

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摘要

The quality of health services provided by medical centers varies widely, and there is often a large gap between the optimal standard of services when judged based on the locality of patients (rural or urban environments). This quality gap can have serious health consequences and major implications for patient's timely and correct treatment. These deficiencies can manifest, for example, as a lack of quality services, misdiagnosis, medication errors, and unavailability of trained professionals. In medical imaging, MRI analysis assists radiologists and surgeons in developing patient treatment plans. Accurate segmentation of anomalous tissues and its correct 3D visualization plays an important role inappropriate treatment. In this context, we aim to develop an intelligent computer-aided diagnostic system focusing on human brain MRI analysis. We present brain tumor detection, segmentation, and its 3D visualization system, providing quality clinical services, regardless of geographical location, and level of expertise of medical specialists. In this research, brain magnetic resonance (MR) images are segmented using a semi-automatic and adaptive threshold selection method. After segmentation, the tumor is classified into malignant and benign based on a bag of words (BoW) driven robust support vector machine (SVM) classification model. The BoW feature extraction method is further amplified via speeded up robust features (SURF) incorporating its procedure of interest point selection. Finally, 3D visualization of the brain and tumor is achieved using volume marching cube algorithm which is used for rendering medical data. The effectiveness of the proposed system is verified over a dataset collected from 30 patients and achieved 99% accuracy. A subjective comparative analysis is also carried out between the proposed method and two state-of-the-art tools ITK-SNAP and 3D-Doctor. Experimental results indicate that the proposed system performed better than existing systems and assists radiologist determining the size, shape, and location of the tumor in the human brain.
机译：医疗中心提供的卫生服务质量差异很大，根据患者所在地区（农村或城市环境）判断最佳服务标准之间通常存在很大差距。这种质量差距可能会对健康产生严重影响，并对患者的及时正确治疗产生重大影响。例如，这些缺陷可能表现为缺乏优质服务，误诊，用药错误以及训练有素的专业人员不可用。在医学成像中，MRI分析可协助放射科医生和外科医生制定患者治疗计划。异常组织的正确分割及其正确的3D可视化在不适当的治疗中起着重要的作用。在这种情况下，我们旨在开发一种专注于人脑MRI分析的智能计算机辅助诊断系统。我们提供脑肿瘤检测，分割及其3D可视化系统，无论地理位置和医疗专家的专业水平如何，均可以提供优质的临床服务。在这项研究中，使用半自动和自适应阈值选择方法对脑磁共振（MR）图像进行分割。分割后，基于词袋（BoW）驱动的鲁棒支持向量机（SVM）分类模型，将肿瘤分为恶性和良性。 BoW特征提取方法通过结合其兴趣点选择过程的快速鲁棒特征（SURF）得到进一步放大。最后，使用用于渲染医学数据的体积行进立方体算法实现了大脑和肿瘤的3D可视化。通过从30位患者收集的数据集验证了所提出系统的有效性，并达到了99％的准确性。在所提出的方法与两个最新的工具ITK-SNAP和3D-Doctor之间也进行了主观比较分析。实验结果表明，所提出的系统比现有系统具有更好的性能，并且可以帮助放射科医生确定肿瘤在人脑中的大小，形状和位置。

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