Improved watershed transform for medical image segmentation using prior information

The watershed transform has interesting properties that make it useful for many different image segmentation applications: it is simple and intuitive, can be parallelized, and always produces a complete division of the image. However, when applied to medical image analysis, it has important drawback...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2004-04, Vol.23 (4), p.447-458
Main Authors: Grau, V., Mewes, A.U.J., Alcaniz, M., Kikinis, R., Warfield, S.K.
Format: Article
Language:English
Subjects:
Algorithms
Biomedical imaging
Brain - anatomy & histology
Cartilage - anatomy & histology
Computer Simulation
Filters
Hospitals
Image analysis
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image segmentation
Imaging, Three-Dimensional - methods
Knee Joint - anatomy & histology
Magnetic Resonance Imaging - methods
Medical signal detection
Models, Biological
Models, Statistical
Morphological operations
Pattern Recognition, Automated
Probability
Reproducibility of Results
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Signal to noise ratio
Subtraction Technique
Surgery
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Summary:The watershed transform has interesting properties that make it useful for many different image segmentation applications: it is simple and intuitive, can be parallelized, and always produces a complete division of the image. However, when applied to medical image analysis, it has important drawbacks (oversegmentation, sensitivity to noise, poor detection of thin or low signal to noise ratio structures). We present an improvement to the watershed transform that enables the introduction of prior information in its calculation. We propose to introduce this information via the use of a previous probability calculation. Furthermore, we introduce a method to combine the watershed transform and atlas registration, through the use of markers. We have applied our new algorithm to two challenging applications: knee cartilage and gray matter/white matter segmentation in MR images. Numerical validation of the results is provided, demonstrating the strength of the algorithm for medical image segmentation.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2004.824224