Differentiating Honeycombed Images from Normal HRCT Lung Images

スポンサーリンク

概要

• 論文の詳細を見る

• A classification method is presented for differentiating honeycombed High Resolution Computed Tomographic (HRCT) images from normal HRCT images. For successful classification of honeycombed HRCT images, a complete set of methods and algorithms is described from segmentation to extraction to feature selection to classification. Wavelet energy is selected as a feature for classification using K-means clustering. Test data of 20 patients are used to validate the method.

• （社）電子情報通信学会の論文
• 2009-05-01

著者

• Malik Aamir

  Hanyang University

• CHOI Tae-Sun

  Gwangju Institute of Science & Technology

• Choi Tae-sun

  Gwangju Institute Of Science & Technology

関連論文

• Differentiating Honeycombed Images from Normal HRCT Lung Images

スポンサーリンク