Name
Abstract | ||
|---|---|---|
Medical image quality requirements have been increasingly stringent with the recent developments of medical technology. To meet clinical diagnosis needs, an effective medical image enhancement method based on convolutional neural networks (CNNs) and frequency band broadening (FBB) is proposed. Curvelet transform is used to deal with medical data by obtaining the curvelet coefficient in each scale and direction, and the generalised cross-validation is implemented to select the optimal threshold for performing denoising processing. Meanwhile, the cycle spinning scheme is used to wipe off the visible ringing effects along the edges of medical images. Then, FBB and a new CNN model based on the retinex model are used to improve the processed image resolution. Eventually, pixel-level fusion is made between two enhanced medical images from CNN and FBB. In the authors’ study, 50 groups of medical magnetic resonance imaging, X-ray, and computed tomography images in total have been studied. The experimental results indicate that the final enhanced image using the proposed method outperforms other methods. The resolution and the edge details of the processed image are significantly enhanced, providing a more effective and accurate basis for medical workers to diagnose diseases. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1049/iet-ipr.2018.6380 | IET Image Processing |
Keywords | Field | DocType |
image enhancement,biomedical MRI,image denoising,computerised tomography,medical image processing,diseases,curvelet transforms,image resolution,convolutional neural nets,image fusion | Noise reduction,Computer vision,Color constancy,Pattern recognition,Frequency band,Convolutional neural network,Ringing,Image quality,Artificial intelligence,Image resolution,Mathematics,Curvelet | Journal |
Volume | Issue | ISSN |
13 | 10 | 1751-9659 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Qiu | 1 | 18 | 4.42 |
| Chang Wen | 2 | 11 | 2.61 |
| Kai Xie | 3 | 16 | 4.40 |
| Fangqing Wen | 4 | 68 | 13.81 |
| Guanqun Sheng | 5 | 1 | 0.35 |
| Xin-Gong Tang | 6 | 1 | 0.35 |