干旱区生态环境知识资源中心(Arid): RbQE: An Efficient Method for Content-Based Medical Image Retrieval Based on Query Expansion

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DOI	10.1007/s10278-022-00769-7
	RbQE: An Efficient Method for Content-Based Medical Image Retrieval Based on Query Expansion
	Rashad, Metwally; Afifi, Ibrahem; Abdelfatah, Mohammed
通讯作者	Rashad, M
来源期刊	JOURNAL OF DIGITAL IMAGING
ISSN	0897-1889
EISSN	1618-727X
出版年	2023
卷号	36 期号:3 页码:1248-1261
英文摘要	Systems for retrieving and managing content-based medical images are becoming more important, especially as medical imaging technology advances and the medical image database grows. In addition, these systems can also use medical images to better grasp and gain a deeper understanding of the causes and treatments of different diseases, not just for diagnostic purposes. For achieving all these purposes, there is a critical need for an efficient and accurate content-based medical image retrieval (CBMIR) method. This paper proposes an efficient method (RbQE) for the retrieval of computed tomography (CT) and magnetic resonance (MR) images. RbQE is based on expanding the features of querying and exploiting the pre-trained learning models AlexNet and VGG-19 to extract compact, deep, and high-level features from medical images. There are two searching procedures in RbQE: a rapid search and a final search. In the rapid search, the original query is expanded by retrieving the top-ranked images from each class and is used to reformulate the query by calculating the mean values for deep features of the top-ranked images, resulting in a new query for each class. In the final search, the new query that is most similar to the original query will be used for retrieval from the database. The performance of the proposed method has been compared to state-of-the-art methods on four publicly available standard databases, namely, TCIA-CT, EXACT09-CT, NEMA-CT, and OASIS-MRI. Experimental results show that the proposed method exceeds the compared methods by 0.84%, 4.86%, 1.24%, and 14.34% in average retrieval precision (ARP) for the TCIA-CT, EXACT09-CT, NEMA-CT, and OASIS-MRI databases, respectively.
英文关键词	Medical image retrieval Pre-trained learning models Query expanded Mean value Deep features
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000922950500002
WOS关键词	FEATURE DESCRIPTOR ; FEATURE-EXTRACTION ; PATTERNS ; TEXTURE ; MRI ; CLASSIFICATION ; HISTOGRAM
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/397248
推荐引用方式 GB/T 7714	Rashad, Metwally,Afifi, Ibrahem,Abdelfatah, Mohammed. RbQE: An Efficient Method for Content-Based Medical Image Retrieval Based on Query Expansion[J],2023,36(3):1248-1261.
APA	Rashad, Metwally,Afifi, Ibrahem,&Abdelfatah, Mohammed.(2023).RbQE: An Efficient Method for Content-Based Medical Image Retrieval Based on Query Expansion.JOURNAL OF DIGITAL IMAGING,36(3),1248-1261.
MLA	Rashad, Metwally,et al."RbQE: An Efficient Method for Content-Based Medical Image Retrieval Based on Query Expansion".JOURNAL OF DIGITAL IMAGING 36.3(2023):1248-1261.