Gabor function based medical image compression

Abstract

Compression methods based on Gabor functions are implemented for simulated nuclear medicine liver images with and without lesions. The performance of the compression schemes is assessed objectively by comparing the original images to the compressed/reconstructed images through calculation of the Hotelling trace, an index that has been shown to correlate well with performance for images from this imaging modality. Gabor-based compression has not previously been implemented on medical images, nor has any rigorous task-based measure of quality been used to assess the compression. For compression based on thresholding the complex Gabor coefficients, a better than 2∶1 compression is obtained without appreciable reduction in image quality, which when combined with gains expected from bit reduction schemes, corresponds to an overall approximate 8∶1 compression. A large number of nuclear medicine liver images with and without space-occupying lesions were simulated. Then a compression scheme based on transformation of the images into the “information space” proposed by Gabor [1] was implemented. Two tasks were examined: 1) determination of the presence or absence of the lesion in a given location, and 2) determination of the presence or absence of the lesion in one of several locations. The task-based performance using the compressed/reconstructed images is compared to that using the original images according to the Hotelling trace criterion.