Combining vector quantization and histogram equalization

Abstract

Combined vector quantization and adaptive histogram equalization Pamela C. Cosman Eve A. Riskin Robert M. Gray tDurand Building, Department of Electrical Engineering Stanford University, Stanford, CA, 94305-4055 Department of Electrical Engineering, FT- 10 University of Washington, Seattle, WA 98195 ABSTRACT Adaptive histogram equalization is a contrast enhancement technique in which each pixel is remapped to an intensity proportional to its rank among surrounding pixels in a selected neighborhood. We present work in which adaptive histogram equalization is performed on the codebook of a tree-structured vector quantizer so that encoding with the resulting codebook performs both compression and contrast enhancement. The algorithm was tested on magnetic resonance brain scans from different subjects and the resulting images were significantly contrast enhanced. 1. INTRODUCTION Histogram equalization refers to a set of contrast enhancement techniques which attempt to spread out the intensity levels occurring in an image over the full available range.1 Histogram equalization is a competitor of interactive intensity windowing, which is the established contrast enhancement technique for medical images. In global histogram equalization, one calculates the intensity histogram for the entire image and then remaps each pixel's intensity proportional to its rank among all the pixel intensities. In adaptive histogram equalization (AHE), the histogram is calculated only for pixels in a context region, usually a square, and the remapping is done for the center pixel of the square. This can be called pointwise histogram equalization because, for each point in the image, one calculates the histogram for the square context region centered on that point. Because this is very computationally intensive, the bilinear interpolative version is an alternative that lowers the computational complexity.2 It calculates the histogram for only a set of non-overlapping context regions that cover the image and the reniapping of pixel intensity values is then exact for only the small number of pixels that are at the centers of these context regions. For all other pixels, a bilinear interpolation from the nearest context region centers determines the appropriate remapping function. With the bilinear interpolative version of AHE, the remapping function for a given pixel of intensity i at location (, y) is determined from the nearest 4 context regions as shown in figure 1. Ifm+_ denotes the mapping at the grid pixel (x+, y.) to the upper right of (x, y), and similar subscripts are used for the other surrounding context regions, then the interpolated AHE result is given by2: in(i) = a[bm(i) + (1 — b)m_(i)J + [1 — u]{bm_(i) + (1 — b)m__(i)], b= here y+—y- O-8194-0805-O/92/$4.QO SPIE Vol. 1653 Image Capture, Formatting, and Display (1992) / 213 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 05/20/2014 Terms of Use: http://spiedl.org/terms