School of Electronics Engineering, Kalinga Institute of Industrial Technology (KIIT) University, Bhubaneswar, Odissa, India.
MIT College of Railway Engineering and Research, Barshi, Solapur, Maharashtra, India.
J Digit Imaging. 2018 Aug;31(4):477-489. doi: 10.1007/s10278-018-0050-6.
The detection of a brain tumor and its classification from modern imaging modalities is a primary concern, but a time-consuming and tedious work was performed by radiologists or clinical supervisors. The accuracy of detection and classification of tumor stages performed by radiologists is depended on their experience only, so the computer-aided technology is very important to aid with the diagnosis accuracy. In this study, to improve the performance of tumor detection, we investigated comparative approach of different segmentation techniques and selected the best one by comparing their segmentation score. Further, to improve the classification accuracy, the genetic algorithm is employed for the automatic classification of tumor stage. The decision of classification stage is supported by extracting relevant features and area calculation. The experimental results of proposed technique are evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on segmentation score, accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 92.03% accuracy, 91.42% specificity, 92.36% sensitivity, and an average segmentation score between 0.82 and 0.93 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 93.79% dice similarity index coefficient, which indicates better overlap between the automated extracted tumor regions with manually extracted tumor region by radiologists.
从现代成像模式中检测和对脑瘤进行分类是首要关注点，但这是一项由放射科医生或临床主管执行的耗时且乏味的工作。放射科医生对肿瘤阶段的检测和分类的准确性仅取决于他们的经验，因此计算机辅助技术对于提高诊断准确性非常重要。在这项研究中，为了提高肿瘤检测的性能，我们研究了不同分割技术的比较方法，并通过比较它们的分割得分来选择最佳的方法。此外，为了提高分类准确性，我们使用遗传算法对肿瘤阶段进行自动分类。通过提取相关特征和区域计算来支持分类阶段的决策。基于分割得分、准确性、灵敏度、特异性和骰子相似性系数，对所提出的技术的实验结果进行了评估和验证，以分析磁共振脑图像的性能和质量。实验结果达到了 92.03%的准确率、91.42%的特异性、92.36%的灵敏度和 0.82 到 0.93 之间的平均分割得分，证明了该技术从脑磁共振图像中识别正常和异常组织的有效性。实验结果还获得了平均 93.79%的骰子相似性系数，这表明自动提取的肿瘤区域与放射科医生手动提取的肿瘤区域之间有更好的重叠。
