Medical image compression based on a morphological representation of wavelet coefficients.
Image compression is fundamental to the efficient and cost-effective use of digital medical imaging technology and applications. Wavelet transform techniques currently provide the most promising approach to high-quality image compression which is essential for diagnostic medical applications. A novel approach to image compression based on the wavelet decomposition has been developed which utilizes the shape or morphology of wavelet transform coefficients in the wavelet domain to isolate and retain significant coefficients corresponding to image structure and features. The remaining coefficients are further compressed using a combination of run-length and Huffman coding. The technique has been implemented and applied to full 16 bit medical image data for a range of compression ratios. Objective peak signal-to-noise ratio performance of the compression technique was analyzed. Results indicate that good reconstructed image quality can be achieved at compression ratios of up to 15:1 for the image types studied. This technique represents an effective approach to the compression of diagnostic medical images and is worthy of further, more thorough, evaluation of diagnostic quality and accuracy in a clinical setting.
Phelan
N C
NC
Institute of Radiological Sciences, University College Dublin, Mater Hospital, Ireland. phelann@nbsp.ie
Ennis
J T
JT
D000465:Algorithms; D055592:Biophysical Phenomena; D001703:Biophysics; D001921:Brain / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D008279:Magnetic Resonance Imaging / Q000379:methods / Q000706:statistics & numerical data; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013131:Spine / Q000033:anatomy & histology; D014057:Tomography, X-Ray Computed / Q000379:methods / Q000706:statistics & numerical data