Name
Abstract | ||
|---|---|---|
Image enhancement is an important problem in image processing and image analysis, especially for low quality X-ray images with both low-illumination and low-contrast. This paper proposes a novel X-ray image enhancement method, which utilizes the maximum fuzzy sure entropy, fuzzy c-partition, and involutive fuzzy complements. In our proposed method, an image is partitioned into dark part and bright part by fuzzy c-partition and the involutive fuzzy complements are obtained, then the exhausted search approach is used to attain the optimal pair and based on the maximum fuzzy sure entropy. In a LabVIEW system platform, many X-ray images have been experimented by the proposed method, and the comparisons of those experimental results show that the proposed scheme has better performance over the traditional algorithms. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/BMEI.2012.6513007 | BMEI |
Keywords | Field | DocType |
fuzzy set theory,labview,x-ray image,involutive fuzzy complements,shannon entropy,low-contrast,image enhencement,image analysis,low quality x-ray images,entropy,low-illumination,image enhancement,x-ray imaging,fuzzy c-partition,maximum fuzzy sure entropy,medical image processing,x-ray image enhancement | Computer vision,Pattern recognition,Computer science,Fuzzy logic,Image processing,Fuzzy set,Artificial intelligence | Conference |
Volume | Issue | ISSN |
null | null | null |
ISBN | Citations | PageRank |
978-1-4673-1183-0 | 0 | 0.34 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ce Li | 1 | 56 | 9.28 |
| Yannan Zhou | 2 | 4 | 0.74 |
| Chengsu Ouyang | 3 | 0 | 0.34 |
| Lihua Tian | 4 | 0 | 0.68 |