Linked Life Data

19963588

Source:http://linkedlifedata.com/resource/pubmed/id/19963588

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2009-12-7
pubmed:abstractText
X-ray image segmentation is an important issue in medical image analysis. Due to inconsistent X-ray absorption, the intensities are usually unevenly distributed and noisy in the processed organ, thus the object segmentation becomes difficult. In this paper we propose a new segmentation method for patella from the lateral knee X-ray images based on the active shape model (ASM). At first, a patella shape model is constructed by principal component analysis (PCA) of corresponding landmarks obtained from a set of training shape. As the knee X-ray image usually contains many anatomical structures, we design a strategy based on edge tracing to place the initial shape model as close to the patella boundary as possible. Then, the shape model is deformed and fitted to the patella boundary by using a dual-optimization approach that includes a genetic algorithm (GA) to get the global geometric transform and ASM to deform the shape model iteratively. Consequently, the proposed method can cope with different knee X-ray images and can segment the patella in an automatic procedure. In the experiment, 20 images were tested and promising results are obtained by the proposed method. This method is found useful for the clinical evaluation and biomechanical study of knee.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1557-170X
pubmed:author
pubmed:issnType
Print
pubmed:volume
2009
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
3553-6
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Automated segmentation for patella from lateral knee X-ray images.
pubmed:affiliation
Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't