Improved Patch-Based Automated Liver Lesion Classification by Separate Analysis of the Interior and Boundary Regions.
Improved Patch-Based Automated Liver Lesion Classification by Separate Analysis of the Interior and Boundary Regions.
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DOI：
10.1109/jbhi.2015.2478255
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发表时间：
2016-11
影响因子：
7.7
通讯作者：
Rubin DL
中科院分区：
文献类型：
--
作者：
Diamant I;Hoogi A;Beaulieu CF;Safdari M;Klang E;Amitai M;Greenspan H;Rubin DL
视觉词袋（BoVW）方法与视觉词的单个字典的建设已被用于各种分类任务，在医学成像，包括肝脏病变的诊断。在本文中，我们描述了一种新的方法，用于自动诊断肝病变的门脉期计算机断层扫描（CT）图像，提高了单字典BoVW方法，通过使用图像补丁表示的内部和边界区域的病变。我们的方法捕获病变边缘和病变内部的特征，通过创建两个单独的字典的边缘和病变的内部区域（“双字典”的视觉单词）。基于这些字典，为病变及其边缘内的每个感兴趣区域（ROI）生成视觉单词直方图。为了验证我们的方法，我们使用了来自两个不同机构的两个数据集，包含194个肝脏病变（61个囊肿，80个转移瘤和53个血管瘤）的CT图像。每个病变的最终诊断由放射科医生确定。两个机构的图像分类准确率分别为99%和88%，合并数据集的分类准确率为93%。我们使用双字典的新BoVW方法显示出有希望的结果。我们相信我们的方法的好处可以推广到放射学的其他应用领域。
The bag-of-visual-words (BoVW) method with construction of a single dictionary of visual words has been used previously for a variety of classification tasks in medical imaging, including the diagnosis of liver lesions. In this paper, we describe a novel method for automated diagnosis of liver lesions in portal-phase computed tomography (CT) images that improves over single-dictionary BoVW methods by using an image patch representation of the interior and boundary regions of the lesions. Our approach captures characteristics of the lesion margin and of the lesion interior by creating two separate dictionaries for the margin and the interior regions of lesions (“dual dictionaries” of visual words). Based on these dictionaries, visual word histograms are generated for each region of interest (ROI) within the lesion and its margin. For validation of our approach, we used two datasets from two different institutions, containing CT images of 194 liver lesions (61 cysts, 80 metastasis and 53 hemangiomas). The final diagnosis of each lesion was established by radiologists. The classification accuracy for the images from the two institutions was 99% and 88%, respectively, and 93% for a combined dataset. Our new BoVW approach that uses dual dictionaries shows promising results. We believe the benefits of our approach may generalize to other application domains within radiology.