Analysis of idiopathic interstitial pneumonia by self organization map on high-resolution computed tomography images (数理モデル化と問題解決)

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概要

• 論文の詳細を見る

• In classifying the idiopathic interstitial pneumonias (IIPs), interpretation of features on high-resolution computed tomography (HRCT) image is effective. However, image patterns of IIPs on HRCT images have so much variety, that the classifying problem is difficult. The purpose of our study is to develop a diagnosis support system for classification of those HRCT images using a Kohonen's self-organizing map (SOM). Our system classify the input HRCT image as 4 IIP classes, that is, Consolidation, Ground-Grass, Honeycomb, and Reticular classes.

• 一般社団法人情報処理学会の論文
• 2007-06-25

著者

• Kido Shoji

  Yamaguchi University

• Shouno Hayaru

  Yamaguchi University

• Kido Shoji

  Yamaguchi Univ.

• GOTO Yoshiharu

  Yamaguchi University

関連論文

• Classification of Idiopathic Interstitial Pneumonia on High-resolution CT Images using Counter-propagation Network
• Classification of Idiopathic Interstitial Pneumonia CT Images using Convolutional-net with Sparse Feature Extractors
• Analysis of idiopathic interstitial pneumonia by self organization map on high-resolution computed tomography images (数理モデル化と問題解決)
• Discrimination of Lung Sounds using a Statistics of Waveform Intervals
• Naive Mean Field Approximation for Sourlas Error Correcting Code(Biocybernetics, Neurocomputing)

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