A marker-based watershed method for X-ray image segmentation.
Digital X-ray images are the most frequent modality for both screening and diagnosis in hospitals. To facilitate subsequent analysis such as quantification and computer aided diagnosis (CAD), it is desirable to exclude image background. A marker-based watershed segmentation method was proposed to segment background of X-ray images. The method consisted of six modules: image preprocessing, gradient computation, marker extraction, watershed segmentation from markers, region merging and background extraction. One hundred clinical direct radiograph X-ray images were used to validate the method. Manual thresholding and multiscale gradient based watershed method were implemented for comparison. The proposed method yielded a dice coefficient of 0.964±0.069, which was better than that of the manual thresholding (0.937±0.119) and that of multiscale gradient based watershed method (0.942±0.098). Special means were adopted to decrease the computational cost, including getting rid of few pixels with highest grayscale via percentile, calculation of gradient magnitude through simple operations, decreasing the number of markers by appropriate thresholding, and merging regions based on simple grayscale statistics. As a result, the processing time was at most 6s even for a 3072×3072 image on a Pentium 4 PC with 2.4GHz CPU (4 cores) and 2G RAM, which was more than one time faster than that of the multiscale gradient based watershed method. The proposed method could be a potential tool for diagnosis and quantification of X-ray images.
Research Lab for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, 1068 Xueyuan Boulevard, University Town of Shenzhen, Shenzhen 518055, PR China.
Xiaodong
X
Zhang
Research Lab for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, 1068 Xueyuan Boulevard, University Town of Shenzhen, Shenzhen 518055, PR China.
Fucang
F
Jia
School of Design Communication and IT, The University of Newcastle, Callaghan, NSW 2308, Australia.
Suhuai
S
Luo
Research Lab for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, 1068 Xueyuan Boulevard, University Town of Shenzhen, Shenzhen 518055, PR China; Nanfang Medical University, 1838 Guangzhou Avenue, Guangzhou 510515, PR China.
Guiying
G
Liu
Research Lab for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, 1068 Xueyuan Boulevard, University Town of Shenzhen, Shenzhen 518055, PR China. Electronic address: qm.hu@siat.ac.cn.
Qingmao
Q
Hu
D000465:Algorithms; D019295:Computational Biology; D006225:Hand / Q000000981:diagnostic imaging; D006801:Humans; D007866:Leg / Q000000981:diagnostic imaging; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000706:statistics & numerical data; D012985:Software Design
Computer-aided diagnosis; Direct radiography; Watershed segmentation; X-ray image