Multiresolution Analysis Using Wavelet, Ridgelet, and Curvelet Transforms for Medical Image Segmentation
The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying region of interest (ROI) in medical images which are obtained from different medical scanners such as PET, CT, or MRI. Multiresolution analysis (MRA) using wavelet, ri...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2011-01-01
|
| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2011/136034 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832568395845861376 |
|---|---|
| author | Shadi AlZubi Naveed Islam Maysam Abbod |
| author_facet | Shadi AlZubi Naveed Islam Maysam Abbod |
| author_sort | Shadi AlZubi |
| collection | DOAJ |
| description | The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying region of interest (ROI) in medical images which are obtained from different medical scanners such as PET, CT, or MRI. Multiresolution analysis (MRA) using wavelet, ridgelet, and curvelet transforms has been used in the proposed segmentation system. It is particularly a challenging task to classify cancers in human organs in scanners output using shape or gray-level information; organs shape changes throw different slices in medical stack and the gray-level intensity overlap in soft tissues. Curvelet transform is a new extension of wavelet and ridgelet transforms which aims to deal with interesting phenomena occurring along curves. Curvelet transforms has been tested on medical data sets, and results are compared with those obtained from the other transforms. Tests indicate that using curvelet significantly improves the classification of abnormal tissues in the scans and reduce the surrounding noise. |
| format | Article |
| id | doaj-art-ee0a77cf430349af93d3603137656a19 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-ee0a77cf430349af93d3603137656a192025-02-03T00:59:09ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962011-01-01201110.1155/2011/136034136034Multiresolution Analysis Using Wavelet, Ridgelet, and Curvelet Transforms for Medical Image SegmentationShadi AlZubi0Naveed Islam1Maysam Abbod2Department of Electronic and Computer Engineering, School of Engineering and Design, Brunel University, West London UB8 3PH, UKDepartment of Electronic and Computer Engineering, School of Engineering and Design, Brunel University, West London UB8 3PH, UKDepartment of Electronic and Computer Engineering, School of Engineering and Design, Brunel University, West London UB8 3PH, UKThe experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying region of interest (ROI) in medical images which are obtained from different medical scanners such as PET, CT, or MRI. Multiresolution analysis (MRA) using wavelet, ridgelet, and curvelet transforms has been used in the proposed segmentation system. It is particularly a challenging task to classify cancers in human organs in scanners output using shape or gray-level information; organs shape changes throw different slices in medical stack and the gray-level intensity overlap in soft tissues. Curvelet transform is a new extension of wavelet and ridgelet transforms which aims to deal with interesting phenomena occurring along curves. Curvelet transforms has been tested on medical data sets, and results are compared with those obtained from the other transforms. Tests indicate that using curvelet significantly improves the classification of abnormal tissues in the scans and reduce the surrounding noise.http://dx.doi.org/10.1155/2011/136034 |
| spellingShingle | Shadi AlZubi Naveed Islam Maysam Abbod Multiresolution Analysis Using Wavelet, Ridgelet, and Curvelet Transforms for Medical Image Segmentation International Journal of Biomedical Imaging |
| title | Multiresolution Analysis Using Wavelet, Ridgelet, and Curvelet Transforms for Medical Image Segmentation |
| title_full | Multiresolution Analysis Using Wavelet, Ridgelet, and Curvelet Transforms for Medical Image Segmentation |
| title_fullStr | Multiresolution Analysis Using Wavelet, Ridgelet, and Curvelet Transforms for Medical Image Segmentation |
| title_full_unstemmed | Multiresolution Analysis Using Wavelet, Ridgelet, and Curvelet Transforms for Medical Image Segmentation |
| title_short | Multiresolution Analysis Using Wavelet, Ridgelet, and Curvelet Transforms for Medical Image Segmentation |
| title_sort | multiresolution analysis using wavelet ridgelet and curvelet transforms for medical image segmentation |
| url | http://dx.doi.org/10.1155/2011/136034 |
| work_keys_str_mv | AT shadialzubi multiresolutionanalysisusingwaveletridgeletandcurvelettransformsformedicalimagesegmentation AT naveedislam multiresolutionanalysisusingwaveletridgeletandcurvelettransformsformedicalimagesegmentation AT maysamabbod multiresolutionanalysisusingwaveletridgeletandcurvelettransformsformedicalimagesegmentation |