Dissertation > Excellent graduate degree dissertation topics show

The Research of Medical Ultrasound Image Denosing Algorithms Based on Multiscale Geometric Analysis

Author: HeHanFang
Tutor: BaiPeiRui
School: Shandong University of Science and Technology
Course: Signal and Information Processing
Keywords: Ultrasound image denoising Contourlet Transform NSCT Bayesian threshold
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 71
Quote: 1
Read: Download Dissertation


Ultrasound imaging has been widely used in clinical diagnosis with advantages of high security, no damage, convenient, low cost and so on. However, the quality of the ultrasound image will be reduced greatly, because multiplicative speckle noise is produced during imaging.The poor ultrasound image will not only affect the doctor’s diagnosis, but also increase the difficulties of the image processing and analying subsequently. Therefore, the research of speckle noise suppression has been one of the important topics of medical ultrasound imaging technologies.Multiscale geometric analysis, which is a hot topic in image processing area currently, was successfully used in area of image denoising, fusion and so on, because it overcame the defects that wavelet could not optimally present singularity of line and surface in image. Specially, Contourlet and NSCT are widely used in imge denoising for the advantages of decomposing images in multiscales and multidrections. The Bayesian threshold was improved in this paper, which based on Contourlet and NSCT:Traditional Bayesian threshold was amended with different subband energy distributions of Log transformed ultrasound images in Contourlet and NSCT domain. In the Contourlet domain, the amended threshold got better denoising effect than Bayesian threshold denoising based on wavelet and scale adaptive threshold denoising based on Contourlet. Further more, it also gets obvious better denoising effect in the NSCT domain.In addition, Bayesian threshold was modified again with energy distribution characteristi-ics of subband images and an adjustable factor, which based on difference of noise variance on different scales in transform domain. The second modified threshold was used to denoise in NSCT domain, and better denoising effect was obtained.

Related Dissertations

  1. Research on Blind Watermarking Technique of Digital Images Based on Contourlet Transform,TP309.7
  2. Study on Fabric Defect Detection and Sutomati Grad-ing System,TP391.41
  3. Multi-source Remote Sensing Image Fusion and Its Application Research,TP751
  4. SAR image denoising and fusion based on multiscale geometric analysis,TN957.52
  5. Research on Technology of Medical Images Fusion,TP391.41
  6. Face Recognition Technology Research Based on Nonsubsampled Contourlet Transform,TP391.41
  7. Research and Application of Finish Image Based on NSCT,TP391.41
  8. Image Denoising Algorithm Based on Contourlet Transform,TP391.41
  9. Road Enhancement and Extraction in Remote Sensing Images Based on Nonsubsampled Contourlet Transform,TP751
  10. The Research on Virtual View Generation,TP391.41
  11. Localized Multi-Channel Level Set Segmentation Combined with Texture Feature,TP391.41
  12. Complex background Infrared Small Target Detection Algorithm,TP274
  13. Research on Speckle Reduction and Segmentation Algorithms for SAR Images,TP751
  14. Research on Content-Based SAR Image Retrieval Techniques,TN957.52
  15. Research and Implementation of Image Retrieval Algorithm Based on Contourlet,TP391.41
  16. Research on Image Reconstruction of Compressive Sensing,TP391.41
  17. Research on Pixel Level Fusion Algorithm for Multi-Source Remote Sensing Image Fusion,TP751
  18. The Study of Infrared and Visible Light Image Fusion Algorithm,TP391.41
  19. Change Detections in Multi-temporal Satellite Images,TP751
  20. Total Variation Models Based Algorithm of Normalization for Face Recognition,TP391.41
  21. Research on Multi-scale Image Segmentation Algorithm Based on Contourlet Transform,TP391.41

CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
© 2012 www.DissertationTopic.Net  Mobile