A practice of medical image quality evaluation

This paper is devoted to subjective/objective evaluation on medical image quality. A model based on neural network is proposed to mimic radiology doctors´ perception. Medical images were compressed by different algorithms such as Dct and wavelet with different compression rate. After decompression, subjective and objective methods are used to evaluate image quality. Subjective rating is given by common person, clinic and radiology doctors. Objective properties are obtained from three categories: image difference from traditional parameters such as MSE, SNR and so on; image feature based on HVS model and image structure distortion from a universal image quality index. Then all data are used to train a BP neural network, whose inputs are objective scores and outputs are yielded to subjective rating by professionals.