基于小波变换的分数阶微分算法在肝脏肿瘤CT图像纹理增强中的应用
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摘要
图像纹理增强过程中容易丢失平滑区域纹理细节,而分数阶微分增强虽然能够非线性保留平滑区域纹理细节,但对频率分辨率敏感。针对这个问题,提出一种基于小波变换的分数阶微分纹理增强算法,应用于平扫计算机断层扫描(CT)图像的肝脏肿瘤区域的纹理增强。首先,通过小波变换将图像感兴趣区分解成多个子带分量;其次,基于分数阶微分定义构造一个带补偿参数的分数阶微分掩膜;最后,使用该掩膜与每个高频子带分量进行卷积并利用小波逆变换重组图像感兴趣区。实验结果表明,该方法在使用较大分数阶次显著增强肿瘤区域的高频轮廓信息的同时,有效地保留了低频平滑的纹理细节:增强后的肝细胞癌区域与原区域相比,信息熵平均增加36.56%,平均梯度平均增加321.56%,平均绝对差值平均为9.287;增强后的肝血管瘤区域与原区域相比,信息熵平均增加48.77%,平均梯度平均增加511.26%,平均绝对差值平均为14.097。
Smooth texture details are easily lost in the process of image texture enhancement. Although fractional-order differential enhancement can preserve the texture details of smooth regions nonlinearly, it is sensitive to frequency resolution. Focusing on this problem, a fractional differential texture enhancement algorithm based on wavelet transform was proposed and applied to texture enhancement of liver tumor regions in plain Computed Tomography(CT) images. Firstly, wavelet transform was used to decompose the image region of interest into multiple subband components. Then, a fractional differential mask with compensation parameter was constructed based on fractional-order differential definition. Finally, the mask was used to convolve with each high frequency subband component respectively, and the image region of interest was recombined by using reverse wavelet transform. The experimental results show that the algorithm effectively preserves the low-frequency smooth texture details while observably enhances the high-frequency contour information of the tumor region by a relatively large fractional order: compared with the original region, the enhanced hepatocellular carcinoma region has the information entropy increased by 36.56% averagely, the average gradient increased by 321.56% averagely, and the mean absolute difference of 9.287 averagely; compared with the original region, the enhanced hepatic hemangioma region has the information entropy increased by 48.77% averagely, the average gradient increased by 511.26% averagely, and the mean absolute difference of 14.097 averagely.
Smooth texture details are easily lost in the process of image texture enhancement. Although fractional-order differential enhancement can preserve the texture details of smooth regions nonlinearly, it is sensitive to frequency resolution. Focusing on this problem, a fractional differential texture enhancement algorithm based on wavelet transform was proposed and applied to texture enhancement of liver tumor regions in plain Computed Tomography(CT) images. Firstly, wavelet transform was used to decompose the image region of interest into multiple subband components. Then, a fractional differential mask with compensation parameter was constructed based on fractional-order differential definition. Finally, the mask was used to convolve with each high frequency subband component respectively, and the image region of interest was recombined by using reverse wavelet transform. The experimental results show that the algorithm effectively preserves the low-frequency smooth texture details while observably enhances the high-frequency contour information of the tumor region by a relatively large fractional order: compared with the original region, the enhanced hepatocellular carcinoma region has the information entropy increased by 36.56% averagely, the average gradient increased by 321.56% averagely, and the mean absolute difference of 9.287 averagely; compared with the original region, the enhanced hepatic hemangioma region has the information entropy increased by 48.77% averagely, the average gradient increased by 511.26% averagely, and the mean absolute difference of 14.097 averagely.
引文
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[3]PU Y F,ZHOU J L,YUAN X.Fractional differential mask:a fractional differential-based approach for multiscale texture enhancement[J].IEEE Transactions on Image Processing,2010,19(2):491-511.
[4]JALAB H A,IBRAHIM R W.Texture enhancement for medical images based on fractional differential masks[J].Discrete Dynamics in Nature and Society,2013,2013:618536.
[5]HALAVAARA J,BREUER J,AYUSO C,et al.Liver tumor characterization:comparison between liver-specific gadoxetic acid disodium-enhanced MRI and biphasic CT-a multicenter trial[J].Journal of Computer Assisted Tomography,2006,30(3):345-354.
[6]HASEBROOCK K M,SERKOVA N J.Toxicity of MRI and CT contrast agents[J].Expert Opinion on Drug Metabolism&Toxicology,2009,5(4):403-416.
[7]YANG Y,SU Z,SUN L.Medical image enhancement algorithm based on wavelet transform[J].Electronics Letters,2010,46(2):120-121.
[8]BHARDWAJ A,SINGH M K.A novel approach of medical image enhancement based on wavelet transform[J].International Journal of Engineering Research and Applications,2012,2(3):2356-2360.
[9]LAVANYA C,YUGANDHAR D.Medical image enhancement based on wavelet transform[J].IOSR Journal of Electronics and Communication Engineering,2016,11(1):20-28.
[10]PU Y,WANG W,ZHOU J,et al.Fractional differential approach to detecting textural features of digital image and its fractional differential filter implementation[J].Science in China Series F:Information Sciences,2008,51(9):1319-1339.
[11]张涌,蒲亦非,周激流.基于分数阶微分的图像增强模板[J].计算机应用研究,2012,29(8):3195-3197.(ZHANG Y,PUY F,ZHOU J L.Image enhancement masks based on fractional differential[J].Application Research of Computers,2012,29(8):3195-3197.)
[12]LI B,XIE W.Adaptive fractional differential approach and its application to medical image enhancement[J].Computers&Electrical Engineering,2015,45:324-335.
[13]陈向阳,谭礼健.基于自适应分数阶微分的医学图像增强算法[J].计算机应用研究,2017,34(12):3895-3898.(CHENX Y,TAN L J.Medical image enhancement algorithm based on adaptive fractional order differentiation[J].Application Research of Computers,2017,34(12):3895-3898.)
[14]MALLAT S G.A theory for multiresolution signal decomposition:the wavelet representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(7):674-693.
[15]DAUBECHIES I.The wavelet transform,time-frequency localization and signal analysis[J].IEEE Transactions on Information Theory,1990,36(5):961-1005.
[16]OLDHAM K,SPANIER J.The Fractional Calculus Theory and Applications of Differentiation and Integration to Arbitrary Order[M].Amsterdam:Elsevier,1974.
[17]GARG V,SINGH K.An improved Grunwald-Letnikov fractional differential mask for image texture enhancement[J].International Journal of Advanced Computer Science and Applications,2012,3(3):130-135.
[18]WANG J,LIU Z C,CHOROWSKI J,et al.Robust 3D action recognition wit random occupancy patters[C]//ECCV 2012:Proceedings of the 12th European Conference on Computer Vision,LNCS 7573.Berlin:Springer,2012:872-885.
[19]吴瑞芳,宣士斌,荆奇.基于局部特征的分数阶微分图像增强方法[J].计算机工程与应用,2014,50(3):160-164.(WUR F,XUAN S B,JING Q.Fractional differential image enhancement algorithm based on local feature[J].Computer Engineering and Applications,2014,50(3):160-164.)