Preliminary Study of Chronic Liver Classification on Ultrasound Images Using an Ensemble Model
Preliminary Study of Chronic Liver Classification on Ultrasound Images Using an Ensemble Model
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DOI：
10.1177/0161734618787447
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发表时间：
2018-11-01
影响因子：
2.3
通讯作者：
Ananthasivan, Rupa
中科院分区：
文献类型：
--
作者：
Bharti, Puja;Mittal, Deepti;Ananthasivan, Rupa
慢性肝病是发展中国家第五大致死原因。早期诊断对及时治疗和抢救生命至关重要。为了检查肝脏的异常，超声成像是最常用的方式。然而，慢性肝脏和肝硬化之间的视觉区分以及肝细胞癌（HCC）的存在是困难的，因为它们在超声图像上看起来几乎相似。在本文中，为了解决这一困难的可视化问题，我们开发了一种方法，将肝脏分为四个阶段，即正常、慢性、肝硬化和从肝硬化演变而来的HCC。该方法是用经过层次特征融合得到的一组选定的“手工”纹理特征来制定的。利用ranklet、灰度差矩阵和灰度共生矩阵等方法提取肝脏表面的多分辨率、高阶特征，以表征肝脏表面的回声纹理和粗糙度。然后，将这些特征应用于所提出的集成分类器上，该集成分类器采用投票算法设计，并结合k-最近邻（k-NN）、支持向量机（SVM）和旋转森林三种分类器。进行实验以评估(a)“手工制作”纹理特征的有效性，(b)所提出的集成模型的性能，(c)所提出的集成策略的有效性，(d)不同分类器的性能，以及(e)基于卷积神经网络（CNN）特征的所提出的集成模型区分四个肝脏阶段的性能。这些实验是在临床获得的超声图像形成的754个感兴趣的分割区域数据库上进行的。结果表明，该分类器模型的分类准确率达到96.6%。
Chronic liver diseases are fifth leading cause of fatality in developing countries. Their early diagnosis is extremely important for timely treatment and salvage life. To examine abnormalities of liver, ultrasound imaging is the most frequently used modality. However, the visual differentiation between chronic liver and cirrhosis, and presence of heptocellular carcinomas (HCC) evolved over cirrhotic liver is difficult, as they appear almost similar in ultrasound images. In this paper, to deal with this difficult visualization problem, a method has been developed for classifying four liver stages, that is, normal, chronic, cirrhosis, and HCC evolved over cirrhosis. The method is formulated with selected set of "handcrafted" texture features obtained after hierarchal feature fusion. These multiresolution and higher order features, which are able to characterize echotexture and roughness of liver surface, are extracted by using ranklet, gray-level difference matrix and gray-level co-occurrence matrix methods. Thereafter, these features are applied on proposed ensemble classifier that is designed with voting algorithm in conjunction with three classifiers, namely, k-nearest neighbor (k-NN), support vector machine (SVM), and rotation forest. The experiments are conducted to evaluate the (a) effectiveness of "handcrafted" texture features, (b) performance of proposed ensemble model, (c) effectiveness of proposed ensemble strategy, (d) performance of different classifiers, and (e) performance of proposed ensemble model based on Convolutional Neural Networks (CNN) features to differentiate four liver stages. These experiments are carried out on database of 754 segmented regions of interest formed by clinically acquired ultrasound images. The results show that classification accuracy of 96.6% is obtained by use of proposed classifier model.