基于曲波变换的医学图像增强算法

Medical image enhancement based on curvelet transform

为满足临床诊断的需要,对医学图像进行良好的分辨,提出一种基于曲波变换的医学图像增强算法。对医学数据进行曲波正变换,获取各个尺度、方向上的曲波系数,利用广义交叉验证准则和遗传算法自适应选取最优阈值进行阈值去噪,对处理后的数据进行频带拓宽处理,进行曲波反变换来重构数据,得到增强图像。分别选取10组医学脑部CT和胸部图像进行研究,实验结果表明,医学数据在经过该算法处理后,信噪比由原始的3dB-4dB提高到5dB-6dB,处理后图像的分辨率有明显提升,病灶点的层次划分更加清晰,有效增强了边缘细节信息,为医疗工作者诊断病症提供了更加清晰准确的依据。

To meet the needs of clinical diagnosis and distinguish, a design method of medical image enhancement based on curvelet transform was put forward. The curvelet transform was used to deal with medical data, by which the curvelet coefficient in each scale and direction was obtained. Generalized cross validation criterion and genetic algorithm was used to select the threshold which applied to threshold denoising adaptively. The method of band broadening was used to process the processed data. This method reconstructed the data using curvelet transform, by which the improved image was gained. 10 groups of medical brain CT and chest image were studied. Experimental results show that the signal to noise ratio of medical data is improved to 5 dB-6 dB after processing and the resolution of the processed image is significantly improved. The hierarchical division of the focus points is more clear, and the edge details are enhanced, providing a more clear and accurate basis for medical workers to diagnose the disease.