Retrieval of Brain Tumors by Adaptive Spatial Pooling and Fisher Vector Representation.
Retrieval of Brain Tumors by Adaptive Spatial Pooling and Fisher Vector Representation.
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通过自适应空间池和 Fisher 向量表示检索脑肿瘤
DOI：
10.1371/journal.pone.0157112
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Chen W
中科院分区：
文献类型：
--
作者：
Cheng J;Yang W;Huang M;Huang W;Jiang J;Zhou Y;Yang R;Zhao J;Feng Y;Feng Q;Chen W
基于内容的图像检索（CBIR）技术目前在医学领域越来越受欢迎，因为它们可以使用大量有价值的存档图像来支持临床决策。在本文中，我们专注于开发 CBIR 系统，用于在 T1 加权对比增强 MRI 图像中检索脑肿瘤。具体地，当用户粗略勾勒出查询图像的肿瘤区域时，期望返回数据库中相同病理类型的脑肿瘤图像。我们提出了一种新颖的特征提取框架来提高检索性能。拟议的框架由三个步骤组成。首先，我们增强肿瘤区域并使用增强的肿瘤区域作为感兴趣的区域来合并丰富的上下文信息。其次，通过基于强度顺序的自适应空间划分方法将增强的肿瘤区域划分为子区域；在每个子区域内，我们提取原始图像块作为局部特征。第三，我们应用 Fisher 内核框架将每个子区域的局部特征聚合成各自的单个向量表示，并将这些每个子区域的向量表示连接起来以获得图像级签名。特征提取后，应用封闭式度量学习算法来测量查询图像和数据库图像之间的相似度。在包含 3604 张图像的大型数据集上进行了广泛的实验，其中涉及三种类型的脑肿瘤，即脑膜瘤、神经胶质瘤和垂体瘤。平均准确率可达94.68%。实验结果证明了所提出的算法在同一数据集上相对于一些相关的最先进方法的能力。
Content-based image retrieval (CBIR) techniques have currently gained increasing popularity in the medical field because they can use numerous and valuable archived images to support clinical decisions. In this paper, we concentrate on developing a CBIR system for retrieving brain tumors in T1-weighted contrast-enhanced MRI images. Specifically, when the user roughly outlines the tumor region of a query image, brain tumor images in the database of the same pathological type are expected to be returned. We propose a novel feature extraction framework to improve the retrieval performance. The proposed framework consists of three steps. First, we augment the tumor region and use the augmented tumor region as the region of interest to incorporate informative contextual information. Second, the augmented tumor region is split into subregions by an adaptive spatial division method based on intensity orders; within each subregion, we extract raw image patches as local features. Third, we apply the Fisher kernel framework to aggregate the local features of each subregion into a respective single vector representation and concatenate these per-subregion vector representations to obtain an image-level signature. After feature extraction, a closed-form metric learning algorithm is applied to measure the similarity between the query image and database images. Extensive experiments are conducted on a large dataset of 3604 images with three types of brain tumors, namely, meningiomas, gliomas, and pituitary tumors. The mean average precision can reach 94.68%. Experimental results demonstrate the power of the proposed algorithm against some related state-of-the-art methods on the same dataset.