Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computers in Biology and Medicine
Computer Methods and Programs in Biomedicine
Supervised range-constrained thresholding
IEEE Transactions on Image Processing
Morphological grayscale reconstruction in image analysis: applications and efficient algorithms
IEEE Transactions on Image Processing
A review of thresholding strategies applied to human chromosome segmentation
Computer Methods and Programs in Biomedicine
Lung tumor segmentation in PET images using graph cuts
Computer Methods and Programs in Biomedicine
Breast mass contour segmentation algorithm in digital mammograms
Computer Methods and Programs in Biomedicine
A novel tool for segmenting 3D medical images based on generalized cylinders and active surfaces
Computer Methods and Programs in Biomedicine
Hi-index | 0.00 |
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 3072x3072 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.