Contrast enhancement of medical images using multiscale edge representation

Abstract

Experience suggests the existence of a connection between the contrast of a grayscale image and the gradient magnitude of intensity edges in the neighborhood where the contrast is measured. This observation motivates the development of edge-based contrast enhancement techniques. In this paper, we present a simple and e ective method for image contrast enhancement based on the multiscale edge representation of images. The contrast of an image can be enhanced simply by stretching or upscaling the multiscale gradient maxima of the image. This method o ers exibility to selectively enhance features of di erent sizes and ability to control noise magni cation. We present some experimental results from enhancing medical images and discuss the advantages of this wavelet approach over other edge-based techniques.

Natural color image enhancement and evaluation algorithm based on human visual system

“…The algorithms developed by Lain and others [31] have typically included the use of non-mapping functions to process the wavelet coefficients. A variety of such functions have been reported in the literature [19,27,32,33]. But the ringing artifacts or halo artifacts still often occur for these multiscale enhancement algorithms because of gain functions F i jþ1 [28,29] 3 .…”

“…Unfortunately, there is not a general rule to produce the optimal size of these varying windows. This problem has led to multiscale methods based on wavelet transforms [27]. These techniques modify the wavelet coefficients of the image decomposition at different scales and obtain a contrast enhanced image.…”

Combined edge detection using wavelet transform and signal registration

“…Because of this ability the wavelets have also been used for edge detection in different applications [13,22,24,26,27].…”

“…The authors of [24,26,27] have shown that analysing a signal at different scales increases the accuracy and reliability of edge detection. Focusing on localized signal structures, e.g., edges, with a zooming procedure enables simultaneous analysis from a rough to a fine shape.…”

Medical Image Enhancement Algorithm Using Edge-Based Denoising and Adaptive Histogram Stretching

“…Wavelet transforms have shown promising results for localization in both time and frequency, and hence have been used for image processing applications including noise removal [4][5]. In the literature, many medical image enhancement methods based on wavelet transform have been proposed [6][7][8][9][10][11]. They have been widely used for the contrast enhancement algorithms by multi-scale edge representation and wavelet analysis together with image sharpening, soft-threshold filtering, histogram specification, nonlinear histogram stretching, etc.…”