Automatic segmentation of CMRIs for LV contour detection
Research on detecting, recognising and interpreting cardiovascular magnetic resonance images (CMRIs) has started since the 1980s. Time consuming and the need of expert evaluation are the key problems in the manual tracing efforts of CMRIs in a routine investigation. CMRIs manual tracing is also depe...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Verlag
2017
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/14939/2/Automatic%20segmentation.pdf http://ir.unimas.my/id/eprint/14939/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992724603&doi=10.1007%2f978-981-10-1721-6_34&partnerID=40&md5=02af1bc3ac27506ed80138904d694742 |
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| Summary: | Research on detecting, recognising and interpreting cardiovascular magnetic resonance images (CMRIs) has started since the 1980s. Time consuming and the need of expert evaluation are the key problems in the manual tracing efforts of CMRIs in a routine investigation. CMRIs manual tracing is also dependent on image quality, and there is no one-size-fits-all MRI setting for an optimum image result. In this paper, we present an approach using 2-Standard Division (2-SD) correlation along with the Sum of Absolute Difference technique and Otsu Watershed to automatically detect the left ventricle (LV) wall and blood pool in the effort to automatically assist the assessment of cardiac function. We test the approach using the Sunnybrook Cardiac Data, a standard benchmark dataset. The results shown that the proposed method had improved the automatic detection of the epicardium and endocardium |
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