|
|
Please login using the form on menu list.
It is required to login for Full-Text PDF.
|
Differentiating Honeycombed Images from Normal HRCT Lung Images
Aamir Saeed MALIK
Tae-Sun CHOI
Publication
IEICE TRANSACTIONS on Information and Systems Vol.E92-D No.5 pp.1218-1221
Publication Date: 2009/05/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Biological Engineering
Keyword:
segmentation,
extraction,
feature selection,
wavelet energy,
classification,
K-means clustering,
Full Text: PDF(198KB)
Summary:
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.
|
|
