Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation

Authors:
Shadi AlZubi;Naveed Islam;Maysam Abbod
Affiliations:
Department of Electronic and Computer Engineering, School of Engineering and Design, Brunel University, West London, UK;Department of Electronic and Computer Engineering, School of Engineering and Design, Brunel University, West London, UK;Department of Electronic and Computer Engineering, School of Engineering and Design, Brunel University, West London, UK
Venue:
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
Year:
2011

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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 transformis 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.