Predictive tree-structured vector quantization for medical image compression and its evaluation with computerized image analysis

Jianhua Xuan; Tulay Adali; Yue Joseph Wang; Richard M. Steinman

doi:10.1117/12.207620

27 April 1995 Predictive tree-structured vector quantization for medical image compression and its evaluation with computerized image analysis

Jianhua Xuan, Tulay Adali, Yue Joseph Wang, Richard M. Steinman

Author Affiliations +

Proceedings Volume 2431, Medical Imaging 1995: Image Display; (1995) https://doi.org/10.1117/12.207620
Event: Medical Imaging 1995, 1995, San Diego, CA, United States

Abstract

We present a predictive learning tree-structured vector quantization technique for medical image compression. A multi-layer perceptron (MLP) based vector predictor is employed to remove first as well as higher order correlations that exist among neighboring pixels. We use a learning tree-structured vector quantization (LTSVQ) scheme, which is based on competitive learning (CL) algorithm, to encode the residual vector. LTSVQ algorithm is computationally very efficient, easy to implement and provides performance comparable to that of LBG (Linde, Buzo and Gray) algorithm. We use computerized image analysis (image segmentation) as well as mean square error (MSE) and signal-to-noise ratio (SNR) to evaluate the quality of the compressed images. We apply the neural network based predictive LTSVQ to mammographic and magnetic resonance (MR) images, and evaluate the quality of images with different compression ratios.

Citation Download Citation

Jianhua Xuan, Tulay Adali, Yue Joseph Wang, and Richard M. Steinman "Predictive tree-structured vector quantization for medical image compression and its evaluation with computerized image analysis", Proc. SPIE 2431, Medical Imaging 1995: Image Display, (27 April 1995); https://doi.org/10.1117/12.207620

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available