An image analysis method for quantification of idiopathic pulmonary fibrosis

Author

Acharya, Mekhala ; Kinser, Jason ; Nathan, Steven ; Albano, Marcia C. ; Schlegel, Lori

Author_Institution

Sch. of Syst. Biol., George Mason Univ., Manassas, VA, USA

fYear

2011

Firstpage

1

Lastpage

5

Abstract

Diagnosis of IPF (idiopathic pulmonary fibrosis) is based on clinical, radiographic and histopathologic evaluations. In this paper we present an adaptive thresholding algorithm and utilize quantitative CT indexes to correlate IPF with pulmonary abnormality. Simulation results demonstrate that this algorithm performs well in identified IPF images. However the absence of gold standards makes quantification challenging for early stage images of IPF and blinded images.

Keywords

computerised tomography; diseases; feature extraction; image classification; learning (artificial intelligence); medical image processing; patient diagnosis; CT index; IPF diagnosis; IPF with pulmonary abnormality; adaptive thresholding algorithm; blinded image; clinical evaluation; computerised tomography; histopathologic evaluation; idiopathic pulmonary fibrosis quantification; image analysis method; radiographic evaluation; Computed tomography; Diseases; Feature extraction; Histograms; Indexes; Lungs; Measurement; Idopatic pulmonary fibrosis; adaptive multiple feature metshod; high resolution computed tomography;

fLanguage

English

Publisher

ieee

Conference_Titel

Applied Imagery Pattern Recognition Workshop (AIPR), 2011 IEEE

Conference_Location

Washington, DC

ISSN

1550-5219

Print_ISBN

978-1-4673-0215-9

Type

conf

DOI

10.1109/AIPR.2011.6176357

Filename

6176357