A medical image retrieval method based on texture block coding tree

2017 ◽  
Vol 59 ◽  
pp. 131-139 ◽  
Author(s):  
Wenbo Li ◽  
Haiwei Pan ◽  
Pengyuan Li ◽  
Xiaoqin Xie ◽  
Zhiqiang Zhang
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Retrieval Method ◽  
Block Coding ◽  
Medical Image Retrieval

scholarly journals Integration of Statistical based Texture and Color Feature for Medical Image Retrieval

2019 ◽  
Vol 8 (3) ◽  
pp. 5584-5588 ◽  
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Image Retrieval ◽  
Medical Image ◽  
Spatial Information ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Retrieval Method ◽  
Medical Image Retrieval ◽  
The Common

Today, the common problem in the domain of computer vision and pattern recognition is content based image retrieval (CBIR). In this paper, a novel image retrieval method using the geometric details based on the correlation among edgels and correlation between pixels has been introduced. The autocorrelation based choridiogram descriptor has been extracted from the image to obtain geometric, texture and spatial information. Color autocorrelogram has been computed to obtain color, texture and spatial information. The proposed method is tested on benchmark heterogeneous medical image database and LIDC-IDRI-CT and VIA/I-ELCAP-CT databases and results are compared with typical CBIR system for medical image retrieval

Medical Image Retrieval Based on Simplified Multi-Wavelet Transform and Shape Feature

2014 ◽  
Vol 513-517 ◽  
pp. 2871-2875
Author(s):  
Xin Rui Wang ◽  
Yun Feng Yang
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
High Frequency ◽  
Medical Image ◽  
Shape Feature ◽  
Retrieval Strategy ◽  
Canny Operator ◽  
Retrieval Method ◽  
Medical Image Retrieval ◽  
Contour Lines

A novel medical image retrieval method based on Simplified Multi-wavelet Transform and Shape Feature was proposed in the paper, which included coarse and fine retrieval procedure. In the procedure of the coarse retrieval, Canny operator was used to extract edges of images. Moreover, contour lines were obtained by using the method of scan lines. At last, the coarse retrieval results of the images can be accomplished by using contour lines of images. In the procedure of the fine retrieval, the simplified multi-wavelet transform was used to decompose images at first, then, only the high frequency coefficients in the vertical directions were selected as retrieval objects. And hierarchical retrieval strategy was selected to accomplish the fine retrieval. This method not only can reduce the computational complexity effectively, but also can make full use of high frequency information of original images. Experiments showed that the accuracy of the retrieved results can be ensured.

A Survey on Content-Based Medical Image Retrieval

2010 ◽  
Vol 22 (4) ◽  
pp. 569-578 ◽  
Author(s):  
Ye Shen ◽  
Mindan Li ◽  
Shunren Xia
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Medical Image Retrieval

scholarly journals Deep triplet hashing network for case-based medical image retrieval

2021 ◽  
Vol 69 ◽  
pp. 101981
Author(s):  
Jiansheng Fang ◽  
Huazhu Fu ◽  
Jiang Liu
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Medical Image Retrieval ◽  
Case Based
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