Image understanding usually includes interrelated components of
image segmentation and object/scene recognition. Image segmentation
extracts the objects/regions of interest from images which are then
analyzed for recognition. Deformable contour methods (DCMs) are
commonly applied for image segmentation. To understand the
strengths and limitations of different DCMs, a comparative study to
review eight major snakes and level set methods applied to the
medical image segmentation is presented. The studied DCMs are
compared using both qualitative and quantitative measures and the
lessons learned from this medical segmentation comparison can be
translated to other image segmentation domains. DCM results can be
recognized for further image analysis and understanding, e.g. a
graph matching algorithm is presented in this book for rather
challenging segmentation applications, such as blur boundary,
complex shape, and intensity inhomogeneity. The skeleton-based
graph matching algorithm consists of major operations of skeleton
extraction, representation, and matching for recognition, and the
results are fedback into the image segmentation to increase the
accuracy of the advanced segmentation.
General
Imprint: |
Lap Lambert Academic Publishing
|
Country of origin: |
Germany |
Release date: |
October 2009 |
First published: |
October 2009 |
Authors: |
Lei He
|
Dimensions: |
229 x 152 x 10mm (L x W x T) |
Format: |
Paperback - Trade
|
Pages: |
164 |
ISBN-13: |
978-3-8383-1843-1 |
Categories: |
Books >
Computing & IT >
General theory of computing >
General
|
LSN: |
3-8383-1843-9 |
Barcode: |
9783838318431 |
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