Medical image retrieval system using GGRE framework

J. Yogapriya, I. Vennila
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引用次数: 6

Abstract

This paper seeks to focus on Medical Image Retrieval based on Feature extraction, Classification and Similarity Measurements which will aid for computer assisted diagnosis. The selected features are Shape(Generic Fourier Descriptor (GFD)and Texture(Gabor Filter(GF)) that are extracted and classified as positive and negative features using a classification technique called Relevance Vector Machine (RVM) that provides a natural way to classify multiple features of images. The similarity model is used to measure the relevance between the query image and the target images based on Euclidean Distance(ED). This type of Medical Image Retrieval System framework is called GGRE. The retrieval algorithm performances are evaluated in terms of precision and recall. The results show that the multiple feature classifier system yields good retrieval performance than the retrieval systems based on the individual features.
医学图像检索系统采用GGRE框架
本文旨在研究基于特征提取、分类和相似度量的医学图像检索,这将有助于计算机辅助诊断。所选择的特征是形状(通用傅里叶描述符(GFD))和纹理(Gabor滤波器(GF)),它们被提取并使用一种称为相关向量机(RVM)的分类技术分类为正特征和负特征,该技术提供了一种自然的方法来对图像的多个特征进行分类。相似度模型基于欧几里得距离(ED)度量查询图像与目标图像之间的相关性。这种类型的医学图像检索系统框架被称为GGRE。从查准率和查全率两个方面评价了检索算法的性能。结果表明,多特征分类器系统比基于单个特征的检索系统具有更好的检索性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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