Automatic Detection of Tram Tracks on HRCT Images

Author

Rudrapatna, M. ; Amaratunga, P. ; Prasad, M. ; Sowmya, Arcot ; Wilson, P.

Author_Institution

Sch. of Comput. Sci. & Eng., New South Wales Univ., Sydney, NSW, Australia

fYear

2006

Firstpage

885

Lastpage

888

Abstract

On high resolution computed tomography (HRCT) images, dilated airways appear as two parallel lines that resemble tram tracks, when they lie in the plane of scan. Tram tracks, when visible, are characteristic of bronchiectasis, a disease caused by the irreversible dilatation of the bronchial tree. Detection of such patterns provides valuable diagnostic information. In this work, semi-supervised learning together with image analysis techniques have been used to detect tram tracks on HRCT images. The approach was tested on 1091 HRCT images belonging to 54 patients, and the results visually validated by radiologists. Sensitivity and specificity of 80% and 91% respectively were achieved.

Keywords

computerised tomography; diagnostic radiography; diseases; feature extraction; image resolution; learning (artificial intelligence); medical image processing; pneumodynamics; HRCT images; airway abnormality; automatic tram track detection; bronchiectasis; diagnostic information; disease; feature extraction; high-resolution computed tomography; image analysis techniques; image pre-processing; irreversible bronchial tree dilatation; semisupervised learning; Australia; Biomedical measurements; Diseases; Image recognition; Length measurement; Lungs; Object recognition; Respiratory system; Shape; Testing; Biomedical image processing; Object recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2006 IEEE International Conference on

Conference_Location

Atlanta, GA

ISSN

1522-4880

Print_ISBN

1-4244-0480-0

Type

conf

DOI

10.1109/ICIP.2006.312510

Filename

4106672