Perbandingan Pegukuran Volume Tumor Brain MRI Menggunakan Teknik Manual Dan Metode Active Contour

Maizza Nadia Putri, I. Katili, Ahmad Hariri, Tri Asih Budiarti, G. M. Wibowo
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引用次数: 1

Abstract

Background: A brain tumor is a mass of brain cells that grow abnormally. In radiological terms, a brain tumor is called a space occupying lesion (SOL) which generally means a lump. Radiologists or radiology specialists in identifying brain tumors will analyze the results of Magnetic Resonance Imaging (MRI) Brain images with post-processing techniques using a menu in a 3D editor called the region growing technique.Methods:This type of research is a quasi-experimental research design using Posttest Only Without Control Group Design. The research plan will be carried out at Hermina Hospital Bekasi using 32 samples of brain tumor MRI images, the sample size is obtained by the sample size formula for two paired populations according to Sastroasmoro (2011). Bivariate data analysis, if the data is normally distributed (p value 0.05), then the Paired T-test statistical test is performed and if the data is not normally distributed (p value 0.05) the Wilcoxon statistical test is performed.Results: The results of the analysis of brain tumors are followed by manual measurement of tumor volume using the region growing technique. It requires sufficient expertise and experience so that the diagnosis of tumor volume is given precisely and accurately so that its handling can be carried out wisely Evaluation of MRI images requires high accuracy, but doctors can make mistakes because the diagnosis is still done manually, such as errors in diagnosing the location of the tumor and the size of the object. The very complex structure of the human brain also presents its own difficulties in identifying brain tumors. Subjective factors can also affect manual doctor evaluations such as fatigue and uncontrolled time in evaluating an MRI image so that a digital image processing program is needed that can be done with a computational machine to assist doctors in evaluating an MRI image automatically. The active contour method can solve the problem of topological changes in a brain tumor image.Conclusion: The active contour method is able to classify images with high accuracy. So that it can increase the accuracy of the segmentation process for easy and fast medical diagnosis. The calculation of the volume of brain tumors can be done using the binaryization method which has been segmented through the final image produced by the active contour method. Tumor segmentation and automatic tumor volume calculation have great potential in clinical treatment by freeing doctors from the burden of manual labeling, digital image processing of brain tumors using the active contour method can be used as a complement to the MRI modality that radiologists can use in calculating brain tumor mass volume calculations
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脑大幅度比较MRI使用手工技术和活性试样方法
背景:脑肿瘤是一团生长异常的脑细胞。在放射学术语中,脑肿瘤被称为占位性病变(SOL),通常意味着肿块。识别脑肿瘤的放射科医生或放射科专家将使用称为区域生长技术的3D编辑器中的菜单,通过后处理技术分析磁共振成像(MRI)大脑图像的结果。方法:本研究采用后测无对照组设计的准实验研究设计。研究计划将在Bekasi Hermina医院进行,使用32个脑肿瘤MRI图像样本,样本量根据Sastroasmoro(2011)的两对人群的样本量公式获得。双变量数据分析,如果数据为正态分布(p值0.05),则进行配对t检验统计检验,如果数据为非正态分布(p值0.05),则进行Wilcoxon统计检验。结果:脑肿瘤分析结果后,采用区域生长技术人工测量肿瘤体积。它需要足够的专业知识和经验,才能准确准确地给出肿瘤体积的诊断,从而明智地进行处理。对MRI图像的评估需要很高的准确性,但由于诊断仍然是人工完成的,医生可能会犯错误,例如诊断肿瘤的位置和物体的大小。人脑非常复杂的结构也给脑肿瘤的识别带来了困难。主观因素也会影响医生的人工评估,如评估MRI图像时的疲劳和不受控制的时间,因此需要一个数字图像处理程序,可以用计算机来完成,以帮助医生自动评估MRI图像。主动轮廓法可以解决脑肿瘤图像的拓扑变化问题。结论:活动轮廓法具有较高的图像分类精度。从而提高分割过程的准确性,方便快捷地进行医学诊断。通过活动轮廓法生成的最终图像进行分割,利用二值化方法进行脑肿瘤体积的计算。肿瘤分割和自动计算肿瘤体积在临床治疗中具有很大的潜力,使医生摆脱了人工标注的负担,利用活动轮廓法对脑肿瘤进行数字图像处理,可以作为放射科医生计算脑肿瘤质量体积的MRI模式的补充
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