Lattice vector quantisation for indexing and retrieval of medical images using texture features based on 2-D Wold decomposition

2015 ◽  
Vol 1 (1) ◽  
pp. 23
Author(s):  
A.N. Krishna ◽  
B.G. Prasad
Keyword(s):  
Medical Images ◽  
Texture Features ◽  
Wold Decomposition ◽  
Lattice Vector ◽  
Vector Quantisation ◽  
Indexing And Retrieval

Wavelet transform image coding using lattice vector quantisation

1994 ◽  
Vol 30 (18) ◽  
pp. 1477-1478 ◽  
Author(s):  
D.G. Sampson ◽  
M. Ghanbari ◽  
E.A.B. da Silva
Keyword(s):  
Wavelet Transform ◽  
Image Coding ◽  
Lattice Vector ◽  
Vector Quantisation

Fast dead zone lattice vector quantisation

2008 ◽  
Vol 44 (3) ◽  
pp. 191 ◽  
Author(s):  
Y. Gaudeau ◽  
L. Guillemot ◽  
J.M. Moureaux
Keyword(s):  
Dead Zone ◽  
Lattice Vector ◽  
Vector Quantisation

scholarly journals Medical Image Retrieval Approach by Texture Features Fusion Based on Hausdorff Distance

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Sun Xiaoming ◽  
Zhang Ning ◽  
Wu Haibin ◽  
Yu Xiaoyang ◽  
Wu Xue ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Hausdorff Distance ◽  
Medical Image ◽  
Medical Information ◽  
Medical Images ◽  
Brain Mri ◽  
Texture Features ◽  
Image Feature ◽  
Medical Image Retrieval

Medical images play an important role in the hospital diagnosis and treatment, which include a lot of valuable medical information. Manually annotated viewing is obviously not effective in managing large amounts of medical imaging data. Hence it is an important task to establish an efficient and accurate medical image retrieval system. In this paper, a medical image retrieval approach based on Hausdorff distance combining Tamura texture features and wavelet transform algorithm is proposed. The combination of Tamura texture features and wavelet transform features can extract the texture features of medical images more effectively, and Hausdorff distance can reflect the overall similarity of medical image feature set. In this paper, 6 group experiments of brain MRI database and the lung CT database were conducted separately. Experiments show that the proposed approach has higher accuracy than a single feature texture algorithm and is also higher than the approach of Tamura texture features and wavelet transform features combined with Euclidean distance.

Research and Application of Key Technologies for Medical Image Intelligence Knowledge Discovery and Data Processing

2020 ◽  
Vol 34 (11) ◽  
pp. 2057005
Author(s):  
Ruo Hu ◽  
Ming Li ◽  
Hong Xu ◽  
Hui Min Zhao
Keyword(s):  
Knowledge Discovery ◽  
Medical Image ◽  
Medical Images ◽  
Image Data ◽  
Texture Features ◽  
Feature Representation ◽  
Experimental Results ◽  
Key Technologies ◽  
Medical Image Data ◽  
The Stability

Hospitals have accumulated a large amount of medical image data which need to be analyzed and integrated so as to be able to find the needed medical image in time, which is the basis of key technologies such as intelligent diagnosis of diseases. Meanwhile, through the analysis and integrated processing of medical images, the potential value of existing medical image data can be fully explored. In this paper, the key technologies in the intelligent image knowledge discovery system and the characteristics of medical image data are studied and improved. In this paper, the characteristics of knowledge discovery and medical image data are comprehensively considered, and RDM texture features are selected as the feature representation of medical images. An improved RDM operator is proposed and proved by experimental results. Experimental results show that the improved RDM coding method can improve the stability of medical image data expression.

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