A nonparametric-based rib suppression method for chest radiographs

Authors:
Jiann-Shu Lee;Jing-Wein Wang;Hsing-Hsien Wu;Ming-Zheng Yuan
Affiliations:
Department of Computer Science and Information Engineering, National University of Tainan, Tainan, Taiwan;Institute of Photonics and Communications, National Kaohsiung University of Applied Science, Kaohsiung, Taiwan;Division of Thoracic Surgery, Tainan Municipal Hospital, Tainan, Taiwan;Institute of System Engineering, National University of Tainan, Tainan, Taiwan
Venue:
Computers & Mathematics with Applications
Year:
2012

Citing 6
Cited 0

Active shape models—their training and application

Computer Vision and Image Understanding
Detection of Rib Shadows in Digital Chest Radiographs

ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume II
Bilateral Filtering for Gray and Color Images

ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Fuzzy automata system with application to target recognition based on image processing

Computers & Mathematics with Applications
A method of image preprocessing based on nonlinear diffusion and information extraction

Computers & Mathematics with Applications
Ultrasonic image classification based on support vector machine with two independent component features

Computers & Mathematics with Applications

Quantified Score

Hi-index	0.09

Visualization

Abstract

This paper presents an automated and comprehensive system for eliminating rib shadows in chest radiographs, which integrates lung field identification, rib segmentation, rib intensity estimation, and suppression. We designed a region of interest (ROI)-based method to estimate a suitable initial lung boundary for active shape model (ASM) deformation by determining the translation and scaling parameters from the lung ROI. By considering the anatomical structure of the rib cage, we developed a locale sampling scheme to achieve nonparametric rib modeling. This scheme integrates knowledge-based generalized Hough transform (GHT) for accurate rib segmentation. We subsequently estimated rib intensity using the real-coded genetic algorithm (RCGA). Experimental results indicate that the relative conspicuity of the nodules increased after rib suppression, compared to the original image. Additionally, the proposed system uses only one standard chest radiograph, and the dual-energy subtraction technique is not required. Thus, this system is suitable for radiologists and computer-aided diagnosis (CAD) schemes for detecting lung nodules in chest radiographs.