Differentiating Honeycombed Images from Normal HRCT Lung Images

Author:

MALIK Aamir Saeed1,CHOI Tae-Sun2

Affiliation:

1. Hanyang University

2. Gwangju Institute of Science & Technology

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Reference7 articles.

1. [1] K. Nishimura, T. Izumi, M. Kitaichi, S. Nagai, and H. Itoh, “The diagnostic accuracy of high resolution computed tomography in diffuse infiltrative lung diseases, ” Chest, vol.104, pp.1149-1155, 1993.

2. [2] R. Uppaluri, E.A. Hoffman, M. Sonka, P.G. Hartley, G.W. Hunninghake, and G. MClennan, “Computer recognition of regional lung disease patterns, ” American Journal of Respiratory and Critical Care Medicine, vol.160, no.2, pp.648-654, 1999.

3. Method for segmenting chest CT image data using an anatomical model: preliminary results

4. Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images

5. [5] M.N. Prasad and A. Sowmya, “Multi-class unsupervised classification with label correction of HRCT lung images, ” IEEE International Conference on Intelligent Sensing & Information Processing, pp.51-56, 2004.

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