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RESEARCH PRODUCT
Iteratively Learning a Liver Segmentation Using Probabilistic Atlases: Preliminary Results
Esther DuraEvgin GoceriJ. Domingosubject
Computer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentation02 engineering and technologyIterative reconstructionMathematical morphology030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicine0202 electrical engineering electronic engineering information engineeringmedicineSegmentationComputer visionComputingMethodologies_COMPUTERGRAPHICSmedicine.diagnostic_testSegmentation-based object categorizationbusiness.industryProbabilistic logicMagnetic resonance imagingPattern recognitionImage segmentationComputer Science::Computer Vision and Pattern Recognition020201 artificial intelligence & image processingArtificial intelligencebusinessPerfusiondescription
This works deals with the concept of liver segmentation by using a priori information based on probabilistic atlases and segmentation learning based of previous steps. A probabilistic atlas is here understood as a probability or membership map that tells how likely is that a point belongs to a shape drawn from the shape distribution at hand. We devise a procedure to segment Perfusion Magnetic Resonance liver images that combines both: a probabilistic atlas of the liver and a segmentation algorithm based on global information of previous simpler segmentation steps, local information from close segmented slices and finally a mathematical morphology procedure, namely viscous reconstruction, to fill the shape. Preliminary results of the algorithm are provided.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-12-01 | 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) |