干旱区生态环境知识资源中心(Arid): An efficient content-based medical image retrieval based on a new Canny steerable texture filter and Brownian motion weighted deep learning neural network

Arid

DOI	10.1007/s00371-022-02446-w
	An efficient content-based medical image retrieval based on a new Canny steerable texture filter and Brownian motion weighted deep learning neural network
	Rao, R. Varaprasada; Prasad, T. Jaya Chandra
通讯作者	Rao, RV
来源期刊	VISUAL COMPUTER
ISSN	0178-2789
EISSN	1432-2315
出版年	2023
卷号	39 期号:5 页码:1797-1813
英文摘要	The increasing size of medical image repositories is due to the increasing number of digital imaging data sources. Most of the image content descriptors proposed in the literature are not suitable for the retrieval of large medical image datasets. The ability to extract features from an image is a vital criterion that should be considered to evaluate retrieval efficacy. This paper proposes an efficient image retrieval system for medical applications based on the new Canny steerable texture filter (CSTF) feature descriptor and Brownian motion weighting deep learning neural network (BMWDLNN) classifier. Initially, Modified Kuan Filter (MKF) is used to condense the noise in images. Then, the image contrast is enhanced using the Gaussian Linear Contrast Stretching Model (GLCSM) method. Then, the image features are extracted using the CSTF method. Later, the dimensionality of the extracted features is reduced by means of the Mean Correlation Coefficient Component Analysis (MCCCA) method and then the BMWDLNN classifier is applied. For the classified images, the score values are calculated using the Harmonic Mean-based Fisher Score (HMFS) method. Thereafter, various distance values are calculated for the score value of the image and are summed up to find the average. The retrieval outcome is determined by the minimum distance between database images and the query image. The proposed method obtained an average precision rate of 0.9981, 0.9992, 0.9951, and 0.9940 for EXACT-09, TCIA, NEMA-CT, and OASIS databases, respectively. The experimental results revealed that the proposed methodology outperforms the existing methods.
英文关键词	Modified Kuan Filter (MKF) Gaussian Linear Contrast Stretching Model (GLCSM) Canny steerable texture filter (CSTF) Mean Correlation Coefficient Component Analysis (MCCCA) Brownian motion weighting deep learning neural network (BMWDLNN) classifier Harmonic Mean-based Fisher Score (HMFS)
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000776062200001
WOS关键词	FEATURE DESCRIPTOR ; PATTERNS ; EXTRACTION
WOS类目	Computer Science, Software Engineering
WOS研究方向	Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398970
推荐引用方式 GB/T 7714	Rao, R. Varaprasada,Prasad, T. Jaya Chandra. An efficient content-based medical image retrieval based on a new Canny steerable texture filter and Brownian motion weighted deep learning neural network[J],2023,39(5):1797-1813.
APA	Rao, R. Varaprasada,&Prasad, T. Jaya Chandra.(2023).An efficient content-based medical image retrieval based on a new Canny steerable texture filter and Brownian motion weighted deep learning neural network.VISUAL COMPUTER,39(5),1797-1813.
MLA	Rao, R. Varaprasada,et al."An efficient content-based medical image retrieval based on a new Canny steerable texture filter and Brownian motion weighted deep learning neural network".VISUAL COMPUTER 39.5(2023):1797-1813.