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Region Based Multiple Features for an Effective Content Based Access Medical Image Retrieval an Integrated with Relevance Feedback Approach

机译:基于区域的多种功能,可实现基于内容的有效访问医学图像检索,并具有相关性反馈方法

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An efficient Content-based medical image retrieval (CBMIR) system is imperative to browse the entire database to locate required medical image. This paper proposes an effecChapHead:/Authortive scheme includes the detection of the boundary of the image followed by exploring the content of the interior boundary region with the help of multiple features. The proposed technique integrates the Texture, Shape features and the relevance feedback mechanism. Differentiate of Gabor Filter used for Texture feature extraction and Moments extract the Region based shape features. The Euclidean distance is used for similarity measure and then these distances are sorted out and ranked. The Recall rate of the medical retrieval system has been enhanced by adapting Relevance Feedback mechanism. The efficiency of the proposed method has been evaluated by using a huge data base by employing multiple features and integrating with Relevance feedback approach. Correspondingly, the Recall Rate has been enormously enhanced and Error Rate has been reduced as compared to the existing classical retrieval methods.
机译:必须使用有效的基于内容的医学图像检索(CBMIR)系统来浏览整个数据库以找到所需的医学图像。本文提出了一个effecChapHead:/ Authorative方案,该方案包括检测图像边界,然后借助多个特征探索内部边界区域的内容。所提出的技术集成了纹理,形状特征和相关性反馈机制。用于纹理特征提取的Gabor滤波器的微分和矩提取基于区域的形状特征。欧几里得距离用于相似性度量,然后对这些距离进行分类和排序。通过采用相关性反馈机制,可以提高医疗检索系统的召回率。该方法的效率已通过使用庞大的数据库并采用多种功能并与相关性反馈方法集成在一起进行了评估。相应地,与现有的经典检索方法相比,召回率得到了极大的提高,错误率得到了降低。

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