Color Medical Image Vector Quantization Coding Using K-Means: Retinal Image

Abstract

Retinal images play an important role in supporting medical diagnosis. Digital retinal image usually are represented in such a large data volume that takes a considerable amount of time to be accessed and displayed. Digital medical image compression therefore become crucial in medical image transfer and storage in electronic database server. This research is concerned with the development of a color medical image coding scheme using vector quantization. K-means is clustering technique that is applied to create codebook in vector quantization (VQ) coding. This research investigates the performance of each of these clustering technique in four color models: RGB, 4:4:4 YUV, 4:2:0 YUV and HSV. The VQ coding scheme is conducted separately to image components in each channel of the color models. Reconstructed color image is obtained by combining the VQ decoding result of each image components. The VQ coding performance relies on the quality of the utilized codebook. The VQ codebook used in this research was developed from retinal image. The retinal image was processed as a set of four quarter-images. This special treatment is required to cope with the large computational load of the VQ codebook generation, while ensuring the incorporation of the color and texture diversity of the training set in the resulted codebook. The RGB 444 color mode (coding of the red, green, and blue channels by the size of 4x4) produces the best subjective and objective quality of image coding. However, the optimum color models for tele-ophthalmology and electronic medical records are the YUV 4:2:0 and RGB 848 for retinal image.KeywordsColor Medical ImageRetinal ImageVector QuantizationK-Means