基于ct扫描肺脏蒙古纳坎检测肺脏的方法距离正则化水平集进化(drlse)

L. Trisnawati, L. Hakim, M. Kom.
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引用次数: 3

摘要

肺是呼吸系统中的一个器官,它是血液中氧气与二氧化碳交换的地方。肺部紊乱会导致患者呼吸困难、活动困难、缺氧,实际上如果不及时发现可导致死亡。确定病人疾病的症状一般是进行实验室检查,这些检查费用相当昂贵,有时会造成伤害,结果有时要很久才能知道x射线图像,这导致许多人表示患有肺病。以往的ct扫描肺部图像分割方法对边缘检测的确定困难或不明确,对ct扫描肺部图像的处理仍需要较长的时间。因此,本研究采用边缘检测sobel和距离正则化水平集进化方法对ct扫描肺部图像进行分割,以加速医学图像的分割。该方法能够对ct扫描肺部进行图像分割,平均准确率为95.08%,相对工作特征曲线(ROC)上的平均曲线下面积(AUC)达90.66%
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SEGMENTASI CITRA CT SCAN LUNG MENGGUNAKAN DETEKSI TEPI SOBEL DAN METODE DISTANCE REGULARIZED LEVEL SET EVOLUTION (DRLSE)
The lung is one of the organs in the respiratory system that serves as a place to exchange oxygen with carbon dioxide in the blood. Disturbance the lung causes the patient difficult breathing, difficult to activity, lack of oxygen in fact if not quickly detected can cause death. To determine the symptoms of a patient's illness is generally carried out laboratory tests, where these tests are quite expensive and sometimes cause injuries and the result was sometimes long to be known x-ray images, this causes many people who indicated suffering from lung disease. The previous method capable of performing image segmentation ct scan lung difficult or not clarified to determination of edge detection and it still takes a long time for processing on ct scan lung image. Therefore, in this study to implement segmentation on ct scan lung image by using edge detection sobel and Distance Regularized Level Set Evolution Method to accelerate the segmentation of medical images. This method is capable of performing image segmentation ct scan lung with average an accuracy of 95.08% and average the Area Under the Curve (AUC) on relaive operating characteristic curve (ROC) amounted to 90.66%
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