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
2019 ◽  
Vol 8 (3) ◽  
pp. 5584-5588 ◽  

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


2014 ◽  
Vol 513-517 ◽  
pp. 2871-2875
Author(s):  
Xin Rui Wang ◽  
Yun Feng Yang

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.


2021 ◽  
Vol 69 ◽  
pp. 101981
Author(s):  
Jiansheng Fang ◽  
Huazhu Fu ◽  
Jiang Liu

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