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.

Prediction Models for Solitary Pulmonary Nodules Based on Curvelet Textural Features and Clinical Parameters

“…While some demographic parameters and morphological features taken into the data sets had similar results with the Mayo Clinic model and other research (Khan et al, 1991;Gurney, 1993;Erasmus et al, 2000;Alzubi et al, 2011), smoking history (i.e. whether the patient smoked or not) did not reflect a difference between malignant and benign nodules.…”

Change Detection in Synthetic Aperture Radar Images Using a Multiscale-Driven Approach

“…The following alternative methods were used in the comparison: (a) Method A: our proposed approach is used without fast non-local means filtering; (b) Method B: our proposed approach is used without 2D-SWT and measurement level fusion-the approach utilized mathematical morphology and the EM algorithm for classification; (c) Method C: our proposed approach is used without morphological filtering; (d) Method D: our proposed approach is used without the EM algorithm-the approach employed the thresholding algorithm proposed by [32] and majority voting rule fusion for classification; (e) Method E: semi-supervised change detection, based on using a kernel-based abnormal detection into the wavelet decomposition of the SAR image [33]; (f) Method F: image denoising using fast discrete curvelet transform via wrapping with the EM algorithm to produce the change detection map [34]; (g) Method G: using UDWT to obtain a multiresolution representation of the log-ratio image, then identifying the number of reliable scales, and producing the final change detection map using fusion at feature level (FFL_ARS) on all reliable scales [15]; (h) Method H: implementing probabilistic Bayesian inferencing with the EM algorithm to perform unsupervised thresholding over the images generated by the dual-tree complex wavelet transform (DT-CWT) at various scales, and moreover, using intra-and inter-scale data fusion to produce the final change detection map [12]; (i) Method I: obtaining a multiresolution representation of the log-ratio image using UDWT, then applying the Chan-Vese (region-based) active contour model to the multiresolution representation to give the final change detection map [18]. Based on Table 2 and Figure 6, one can observe that the change detection result from Method G showed the lowest performance of all tested methods with an overall accuracy of 68.412% and a kappa coefficient of 0.162.…”

Multi-resolution MRI Brain Image Segmentation Based on Morphological Pyramid and Fuzzy C-mean Clustering

“…Multi-resolution analysis has been successfully used in image processing specially with image segmentation [31]. In this stage, we take the skull striped image as an input to 2D Haar discrete wavelet transform to apply multi-resolution to get the wavelet decomposition.…”