D.-Y. Tsai, Y. Lee, and R. Chiba (Japan)
Medical image processing, contrast enhancement, noise reduction, entropy
This paper presents an improved adaptive-neighborhood contrast-enhancement (ANCE) method for improvement of medical image quality. The ANCE method consists of computing a local contrast around each pixel using a vari able neighborhood whose size depends on the statistical properties around the given pixel. The obtained contrast image is then transformed into a new contrast image using a contrast enhancement function. Finally, a contrast enhanced image is obtained by applying inverse contrast transform to the previous step. This technique provides the advantages of enhancing or preserving image contrast while suppressing noise. However, it has a drawback. The performance of the ANCE method largely depends on how to determine the parameters used in the processing steps. The present study proposes a novel method for op timal and automatic determination of threshold-value and neighborhood-size parameters using entropy. To quantita tively compare the performance of the proposed method with that of the ANCE method, computer-simulated im ages are generated. The output-to-input SNR ratio and the mean squared error are used as comparison criteria. Re sults demonstrate the superiority of the proposed method. Moreover, we have applied our new algorithm to X-ray CT images and echocardiograms. Our results show that the proposed method has the potential to become useful for improvement of image quality of medical images.
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