An empirical technique to improve MRA imagin
In the Region Growing Algorithm (RGA) results of segmentation are totally dependent on the selection of seed point, as an inappropriate seed point may lead to poor segmentation. However, the majority of MRA (Magnetic Resonance Angiography) datasets do not contain required region (vessels) in startin...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Emerald Publishing
2016-07-01
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| Series: | Applied Computing and Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2210832715000186 |
| Summary: | In the Region Growing Algorithm (RGA) results of segmentation are totally dependent on the selection of seed point, as an inappropriate seed point may lead to poor segmentation. However, the majority of MRA (Magnetic Resonance Angiography) datasets do not contain required region (vessels) in starting slices. An Enhanced Region Growing Algorithm (ERGA) is proposed for blood vessel segmentation. The ERGA automatically calculates the threshold value on the basis of maximum intensity values of all the slices and selects an appropriate starting slice of the image which has a appropriate seed point. We applied our proposed technique on different patients of MRA datasets of different resolutions and have got improved segmented images with reduction of noise as compared to tradition RGA. |
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| ISSN: | 2210-8327 |