.png){kind=icon}
.png){kind=icon}
Return
A medical image retrieval method based on texture block coding tree
.png){kind=icon}
PRE
AI
DOI：10.1016/j.image.2017.06.013.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Content-based medical image retrieval (CBMIR) has been widely studied for computer aided diagnosis. Accurate and comprehensive retrieval results are effective to facilitate diagnosis and treatment. Texture is one of the most important features used in CBMIR. Most of existing methods utilize the distances between matching point pairs for texture similarity measurement. However, the distance based similarity measurements are of low tolerance to slight texture shifts, which result in an excessive sensitivity. Furthermore, with the increase of the number of texture points, their time complexity is in explosive growth. In this paper, a new medical image retrieval model is presented based on an iterative texture block coding tree. The corresponding methods for coarse-grained and fine-grained similarity matching are also proposed. Moreover, a multi-level index structure is designed to enhance the retrieval efficiency. Experimental results show that, our methods are of high efficiency and appropriate tolerance on slight shifts, and achieve a relative better retrieval performance in comparison of other existing methods. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Content-based image retrieval (CBIR)
Texture block coding
Image processing
Medical image
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
S
IF:
2.7
Papers:
2.8K
Citations:
4.2K