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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >Computerized Liver Segmentation from CT Images using Probabilistic Level Set Approach
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Computerized Liver Segmentation from CT Images using Probabilistic Level Set Approach

机译：从CT图像使用概率水平设定方法的计算机化肝脏分割

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摘要

Accurate segmentation of patient's liver from his/her computed tomography-angiography (CTA) images is the preliminary component for a reliable computerized liver evaluation system. Flawlessness in liver diagnosis relies upon the precision in the segmentation of liver region from all the slices/images in a given patient dataset. Nevertheless, with the challenges like intensity similarity, partial volume effect of liver with its adjacent abdominal organs and liver shape variability across patients, achieving automated optimal liver region segmentation from acquired CT scans is difficult. This paper proposes a semisupervised liver segmentation technique, which adjusts the segmentation parameters for each patient through continuous learning of patient's CTA dataset properties in a Bayesian level set framework to address all the aforementioned challenges. In this framework, Bayesian probability model with spatial prior is utilized to initiate the level set and to derive an enhanced variable force and edge indication function that helps level set evolution to reach genuine liver boundaries in reduced time. The proposed model has been validated on standard MICCAI liver dataset, producing accuracy score of 79.
机译：从他/她的计算机断层造影 - 血管造影（CTA）图像中，患者肝脏的精确分割是可靠的计算机化肝脏评估系统的初步分量。肝脏诊断的无瑕疵依赖于给定患者数据集中的所有切片/图像的肝脏区域的细分。然而，由于强度相似性等挑战，肝脏与其相邻腹部器官的部分体积效应和对患者的肝脏形状变异性，难以获得从获取的CT扫描实现自动最佳肝脏区分割。本文提出了一种半培育的肝脏分段技术，它通过在贝叶斯级别集框架中的患者的CTA数据集属性进行患者的CTA数据集属性来调整每位患者的分割参数，以解决所有上述挑战。在该框架中，利用空间前的贝叶斯概率模型启动级别集并导出增强的可变力和边缘指示功能，从而有助于水平集进化以在缩短时间内达到真正的肝边界。所提出的模型已在标准Miccai肝脏数据集上验证，精确得分为79。

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