Paper Info
Title
Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation.
Abstract
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.
Year
DOI
Venue
2011
10.1155/2011/136034
Int. J. Biomedical Imaging
Keywords
Field
DocType
multiresolution analysis,scanners output,medical image segmentation,different slice,proposed segmentation system,automatic image segmentation system,medical image,different medical scanner,gray-level information,medical data set,gray-level intensity,bioinformatics,biomedical research
Computer vision,Data set,Computer science,Segmentation,Multiresolution analysis,Image segmentation,Artificial intelligence,Region of interest,Wavelet,Curvelet
Journal
Volume
ISSN
Citations 
2011
1687-4196
16
PageRank 
References 
Authors
0.69
10
3
Name
Order
Citations
PageRank
Shadi Alzubi1815.12
Naveed Islam2201.91
Maysam Abbod3387.15