Content-Based Medical Image Retrieval Using Delaunay Triangulation Segmentation Technique

Sneha Kugunavar, C. J. Prabhakar
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引用次数: 6

Abstract

This article presents a novel technique for retrieval of lung images from the collection of medical CT images. The proposed content-based medical image retrieval (CBMIR) technique uses an automated image segmentation technique called Delaunay triangulation (DT) in order to segment lung organ (region of interest) from the original medical image. The proposed method extracts novel and discriminant features from the segmented lung region instead of extracting novel features from the whole original image. For the extraction of shape features, the authors employ edge histogram descriptor (EHD) and geometric moments (GM), and for the extraction of texture features, the authors use gray-level co-occurrence matrix (GLCM) technique. The shape and texture features are combined to form the hybrid feature which is used for retrieval of similar lung images. The proposed method is evaluated using two benchmark datasets of lung CT images. The simulation results prove that the proposed CBMIR framework shows improved performance in terms of retrieval accuracy and retrieval time.
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基于内容的Delaunay三角分割医学图像检索
本文提出了一种从医学CT图像集合中检索肺部图像的新技术。提出了一种基于内容的医学图像检索(CBMIR)技术,该技术使用一种称为Delaunay三角剖分(DT)的自动图像分割技术从原始医学图像中分割出肺器官(感兴趣区域)。该方法不需要从整个原始图像中提取新的特征,而是从分割后的肺区域中提取新的特征和判别特征。形状特征提取采用边缘直方图描述子(EHD)和几何矩(GM),纹理特征提取采用灰度共生矩阵(GLCM)技术。将形状特征和纹理特征相结合形成混合特征,用于相似肺图像的检索。使用两个肺CT图像基准数据集对所提出的方法进行了评估。仿真结果表明,所提出的CBMIR框架在检索精度和检索时间方面都有显著提高。
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