Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 23, Issue 4, Pages 447-458Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2004.824224
Keywords
biomedical imaging; image segmentation; morphological operations; tissue classification; watersheds
Categories
Funding
- NCI NIH HHS [R01 CA86879, P01 CA67165] Funding Source: Medline
- NCRR NIH HHS [P41 RR 13218, R01 RR1747] Funding Source: Medline
- NIMH NIH HHS [R21 MH67054] Funding Source: Medline
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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.
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