Content-Based Retrieval of Medical Images by Combining Global Features

2006 ◽  
pp. 702-711 ◽  
Author(s):  
Mark O Güld ◽  
Christian Thies ◽  
Benedikt Fischer ◽  
Thomas M. Lehmann
Keyword(s):  
Medical Images ◽  
Global Features ◽  
Content Based Retrieval

Automatic categorization of medical images for content-based retrieval and data mining

2005 ◽  
Vol 29 (2-3) ◽  
pp. 143-155 ◽  
Author(s):  
Thomas M. Lehmann ◽  
Mark O. Güld ◽  
Thomas Deselaers ◽  
Daniel Keysers ◽  
Henning Schubert ◽  
...  
Keyword(s):  
Data Mining ◽  
Medical Images ◽  
Content Based Retrieval ◽  
Automatic Categorization

A Dynamic Programming Approach for Secure and Accurate Content-Based Retrieval of Medical Images (Preprint)

2020 ◽  
Author(s):  
Jinhong Sun ◽  
Liang Qi ◽  
Yinglei Song ◽  
Junfeng Qu ◽  
Mohammad Khosravi
Keyword(s):  
Dynamic Programming ◽  
Health Informatics ◽  
Medical Images ◽  
Image Features ◽  
Programming Approach ◽  
Content Based Retrieval ◽  
Retrieval Accuracy ◽  
Dynamic Programming Approach ◽  
Testing Dataset ◽  
Overall Performance

UNSTRUCTURED Recently, with the explosive growth in the number of available medical images generated by medical imaging systems, content-based retrieval of medical images has become an important method for the diagnosis and study of many diseases. Most existing methods find medical images similar to a given one based on the extraction and comparison of crucial image features. However, similarity values computed with low level visual features of an image generally do not match the similarity obtained from human observation well. The overall performance of these methods is thus often unsatisfactory. This paper proposes a dynamic programming approach for content-based retrieval of medical images. The approach represents an image with three different histograms that contain both crucial intensity and textural features of the image. The similarity between two images is evaluated with a dynamic programming approach that can optimally align the peaks in the corresponding histograms from both images. Experiments show that the proposed approach is able to generate retrieval results with high accuracy. A comparison with state-of-the-art approaches for content-based medical image retrieval shows that the proposed approach can achieve higher retrieval accuracy in the testing dataset. As a result, higher retrieval accuracy may lead to more reliable results for the diagnosis and treatment of many diseases. The proposed approach is thus potentially useful for improving the security of many applications in health informatics.

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