Ultrasonographic Diagnosis of Cirrhosis Based on Preprocessing Using DCT

Akira KOBAYASHI  Shunpei WATABE  Masaaki EBARA  Jianming LU  Takashi YAHAGI  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E86-A    No.4    pp.968-971
Publication Date: 2003/04/01
Online ISSN: 
Print ISSN: 0916-8508
Type of Manuscript: LETTER
Category: Neural Networks and Bioengineering
DCT,  back-propagation,  neural network,  cirrhosis,  

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We have classified parenchymal echo patterns of cirrhotic liver into four types, according to the size of hypo echoic nodular lesions. The NN (neural network) technique has been applied to the characterization of hepatic parenchymal diseases in ultrasonic B-scan texture. We employed a multilayer feedforward NN utilizing the back-propagation algorithm. We extracted 1616 pixels in the two-dimensional regions. However, when a large area is used, input data becomes large and much time is needed for diagnosis. In this report, we used DCT (discrete cosine transform) for the feature extraction of input data, and compression. As a result, DCT was found to be suitable for compressing ultrasonographic images.