Discriminative Subtree Selection for NBI Endoscopic Image Labeling
Discriminative Subtree Selection for NBI Endoscopic Image Labeling
复制标题
DOI：
10.1007/978-3-319-54427-4_44
复制
发表时间：
2016-11
期刊：
影响因子：
--
通讯作者：
Tsubasa Hirakawa;Toru Tamaki;Takio Kurita;B. Raytchev;K. Kaneda;Chaohui Wang;Laurent Najman;T. Koide;S. Yoshida;H. Mieno;Shinji Tanaka
中科院分区：
文献类型：
--
作者：
Tsubasa Hirakawa;Toru Tamaki;Takio Kurita;B. Raytchev;K. Kaneda;Chaohui Wang;Laurent Najman;T. Koide;S. Yoshida;H. Mieno;Shinji Tanaka
在本文中，我们提出了一种新的方法标记的结直肠窄带成像（NBI）内窥镜图像的基础上的形状树。通过简单地对形状树中所有节点的直方图特征进行分类，可以得到标记结果，然而，由于小节点的直方图特征不具有足够的区分性，因此难以获得满意的结果。为了获得判别子树，我们提出了一种方法，最佳选择判别子树。我们建立了一个目标函数模型，其中包括分类器的参数和选择子树的阈值。然后，通过将子树的节点的分类结果映射到那些对应的图像区域来进行标记。在63幅NBI内窥镜图像数据集上的实验结果表明，该方法在定性和定量方面都优于现有方法。
In this paper, we propose a novel method for image labeling of colorectal Narrow Band Imaging (NBI) endoscopic images based on a tree of shapes. Labeling results could be obtained by simply classifying histogram features of all nodes in a tree of shapes, however, satisfactory results are difficult to obtain because histogram features of small nodes are not enough discriminative. To obtain discriminative subtrees, we propose a method that optimally selects discriminative subtrees. We model an objective function that includes the parameters of a classifier and a threshold to select subtrees. Then labeling is done by mapping the classification results of nodes of the subtrees to those corresponding image regions. Experimental results on a dataset of 63 NBI endoscopic images show that the proposed method performs qualitatively and quantitatively much better than existing methods.