Anaysis of Idiopathic interstitial Pneumonia by Self Organization Map on High-resolution Computed Tomography Images

摘要

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.