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In the second approach, the multilevel image is conversed into a set of bilevel images and these images are processed simultaneously.

The results reveal that the performance of real coded genetic algorithm with SBX crossover based optimal multilevel thresholding for medical image is better and has consistent performance than already reported methods.

With the generated control point pairs, a new simulated live image is produced by warping of it using multilevel B-spline interpolation.

All methods are designed for the study of multilevel images, where the information is stored in the grey scale of the image.

As the third approach giving the best results, a genuine multilevel image analysis based on gradient methods is suggested.

Noise filtering of the RetCam images was done using anisotropic diffusion filtering which allowed multilevel visualization of vascularity analysis data.

Note that in these non-critical claudicant patients there is little 'venous contamination', and these MRA images are as presented immediately after acquisition with automated MIP post-processing and stitching to give an instant overview vascular map of multilevel disease.

The major motivation for a multilevel approach is that certain variations in an image, such as those due to edges are local in nature, and hence will be better captured by use of local (rather than global) information.

Distributed multilevel perception is achieved that yields a detailed image of the whole body's state.

In this paper, real coded genetic algorithm with Simulated Binary Crossover (SBX) based multilevel thresholding is used for the segmentation of medical brain images.

In this paper, image warping is performed using multilevel B-spline interpolation function [ 23].