Region merging in medical image segmentation and interpretation

Automated segmentation and interpretation of medical images is often a difficult task due to the complexity of the image data. Semantic region growing approaches often start with an oversegmented image and use a priori knowledge to merge regions in objects. We describe here a region merging method t...

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Published inProceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ Vol. 15; no. pt 1; pp. 158 - 159
Main Authors Tadikonda, S.K., Sonka, M., Collins, S.M.
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 1993
Subjects
Biomedical imaging
Cities and towns
Feedback loop
Genetic algorithms
Image generation
Image segmentation
Iris
Merging
Radiology
Shape
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Summary:Automated segmentation and interpretation of medical images is often a difficult task due to the complexity of the image data. Semantic region growing approaches often start with an oversegmented image and use a priori knowledge to merge regions in objects. We describe here a region merging method that is useful in our semantic genetic image segmentation and interpretation method.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISBN:0780313771
9780780313774
DOI:10.1109/IEMBS.1993.978479