基於定量粗糙紋理所進行之超音波影像瀰漫性肝病變的分類

肝硬化典型的超音波影像表現：結節狀肝表面、肝實質紋理粗糙紊亂、肝靜脈之不規則狹窄。此三項評估因子為成形肝硬化所獨有，並以coarse echotexture 靈敏度最高。本研究提出一種量測coarse echotexture的方法，以小波轉換(wavelet)中的二維離散小波轉換(2D-DWT)為工具，利用小波分析將由超音波儀器所取得的超音波影像進行金字塔架構(j=4)分解，並重建其不同階層高頻影像紋理能量與其每個階層的顆粒進行加權計算，量化coarse echotexture的程度，並以3種不同粗糙程度的海綿超音波影像作實驗驗證，並以此量化的方式有效的辨別肝硬化與正常肝臟。

關鍵字

肝；超音波

並列摘要

The ultrasonic image with typical cirrhosis behaves: Nodular liver surface , coarse echotexture, may be compared with the echo-texture of spleen, irregular narrowing outline of hepatic vein.Cirrhosis (established liver cirrhosis ) in order to take shape of three items of assessment factor is exclusive , and the highest in sensitivity with coarseness of echotexture. My research propose Coarseness as new feature, prevent the machine from operating and establishing the question caused, and set up the method of examining coarseness of a kind of quantity.The two-dimentional discrete wavelet (2D-DWT) as the tool to decompose the ultrasonic image made by ultrasonic instrument, then rebuild the high-frequency image , and then by rebuilding all kinds of proportion of image particle [1248 ] and calculating texture energy with four Level high-frequency image convert into making characteristic value worthwhily of Coarseness the weighting, the degree of quantization coarse echotexture, and do the experiment to verify with the ultrasonic image of sponge of 3 kinds of different coarse degree, the effective one distinguishes cirrhosis and normal liver.

並列關鍵字

Ultrasound ； Liver

參考文獻

[2]A. Mojsilovic, M. Popovic, etc, "On the Selection of an Optimal Wavelet Basis for Texture Characterization," IEEE Trans. Image Process, vol. 9, no. 12, pp. 2043-50, 2000

[3]A. Mojsilovic, M. Popovic, A. Popovic, and A. Neskovic, “Wavelet Image Extension for Analysis and Classification of Infarcted Myocardial Tissue,” IEEE Trans. on Biomedical Engineering, vol. 44, no. 9, pp.856-865, 1997.

[4]A. Mojsilovic, M. Popovic, and D. Sevic, “Classification of The Ultrasound Liver Images With The 2Nx1-D Wavelet Transform,” in Proc. 1996 IEEE Int. Conf. Image Processing, vol. 1, pp.367-370, 1996.

[5]A. Mojsilovic, M. Popovic, A. Popovic, A. Neskovic, and V. Obradovic, “Analysis and Characterization of Myocardial Tissue With The Wavelet Image Extension,” in Proc. IEEE Int. Conf. Image Processing, vol. 2, pp.504-507, 1995.

[6] P. Fish, “Physics and instrumentation of diagnostic medical ultrasound.” John Wiley & Sons, 1990.

國際替代計量

基於定量粗糙紋理所進行之超音波影像瀰漫性肝病變的分類

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