Fundamentals of digital image processing
Fundamentals of digital image processing
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Euclidean paths: a new representation of boundary of discrete regions
Graphical Models and Image Processing
Partitioning 3D Surface Meshes Using Watershed Segmentation
IEEE Transactions on Visualization and Computer Graphics
ROI Boundary Detection Based on Geometric Active Contour Model in X-ray Skeletal Image
BMEI '08 Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 02
Epiphyses Extraction Method Using Shape Information for Left Hand Radiography
ICHIT '08 Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology
A Method Based on Discrete Tangent for Curvature Estimation of Digital Curve
GCIS '09 Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 04
A fast sequential rainfalling watershed segmentation algorithm
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Hybrid image segmentation using watersheds and fast region merging
IEEE Transactions on Image Processing
Automatic Image Segmentation by Dynamic Region Merging
IEEE Transactions on Image Processing
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Bone segmentation in radiographic imaging is an intermediate level processing stage for an automated vision system for the skeletal assessment of children. It is one of the challenging problems in medical image analysis due to high noise levels and low contrast with non-uniform and complex intensity distribution of radiographic image. In this paper, we present a local merging algorithm for automatically segmenting bones from the hand radiograph. With an initial over-segmented image, in which the many primitive (homogeneous) regions are generated by watershed transform and image pre-processing, the hand bone X-ray image segmentation is performed by the local merging process on regions of interest (ROIs). Firstly, the hand is separated from the background to get hand boundary. In this phase, aiming the hand separation coincide with the region reduction, an merging algorithm based on the region adjacent graph (RAG) and nearest neighbour graph (NNG) is proposed. Next, the curvature information of the hand boundary is analyzed for determining the desired ROIs on the hand image. Finally, the sub-RAGs which are sub-graph of the RAG associated with the ROI are extracted, and the local merging process on each sub-RAG is individually executed. Experiments are carried out on 30 hand X-ray images of the young children where the carpal bones have distinct, non-overlapping boundaries. The experimental results show that with the proposed method, an accurate and robust segmentation can be achieved.