Automated lesion detection methods for 2D and 3D chest X-ray images

Author

Hara, Takeshi ; Fujita, Hiroshi ; Lee, Yongbum ; Yoshimura, Hitoshi ; Kido, Shoji

Author_Institution

Dept. of Inf. Sci., Gifu Univ., Japan

fYear

1999

fDate

1999

Firstpage

768

Lastpage

773

Abstract

The purpose of this work is to present some technical approaches of our computer-aided detection (CAD) system for chest radiograms and helical CT scans, and also evaluate that by using three databases. The CAD includes some methods to detect lesions and to eliminate false-positive findings. The detection methods consist of template matching and artificial neural network approaches. A genetic algorithm (GA) was employed in template matching to select a matched image from various reference patterns. Artificial neural networks (ANN) were also applied to eliminate the false-positive candidates. The sensitivity and the number of false-positives were 73% and 11 FP per image on chest radiogram CAD and 77% with 2.6 FP per image on helical CT scan CAD. These preliminary results imply that the GA and ANN-based detection methods may be effective in indicating lesions on chest radiograms and helical CT scans

Keywords

cancer; computerised tomography; genetic algorithms; image matching; lung; medical image processing; neural nets; object detection; tumours; 2D images; 3D images; ANN; GA; X-ray images; artificial neural network; automated lesion detection; chest radiograms; computer-aided detection; databases; false-positive findings; genetic algorithm; helical CT scans; lung; template matching; Artificial neural networks; Biological cells; Cancer detection; Gas detectors; Image databases; Lesions; Lungs; X-ray detection; X-ray detectors; X-ray imaging;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Analysis and Processing, 1999. Proceedings. International Conference on

Conference_Location

Venice

Print_ISBN

0-7695-0040-4

Type

conf

DOI

10.1109/ICIAP.1999.797688

Filename

797688