Reclassification of segmentation boundary base on neighboring function

Motivated by the goal of improving the performance of segmentation, a new technique based on Neighboring Function (NF) is presented to reclassify the rough segmentation boundary pixels. The NF is a novel measurement of neighboring relationship, it takes into consideration of both spatial and intensity information and their distribution pattern. With the rough boundary provided by other segmentation algorithms, the value of the NF at each boundary pixel is calculated in a specific neighborhood, then the reclassification will be implemented by comparing these values. In order to obtain the expected boundary, this step is iterated for several times. In our study, the proposed method is applied to synthetic and real medical images, a great improvement of the quality of segmentation boundary has been achieved. The accuracy and reproducibility of this reclassification method has been proven by experimental results. Experiments also show that this method is insensitive to noise.

[1]  Verónica Médina-Bañuelos,et al.  Data-driven brain MRI segmentation supported on edge confidence and a priori tissue information , 2006, IEEE Transactions on Medical Imaging.

[2]  Marie-Pierre Jolly,et al.  Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images , 2001, ICCV.

[3]  Hui Wang,et al.  Nearest neighbors by neighborhood counting , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Sahibsingh A. Dudani.  The Distance-Weighted k-Nearest-Neighbor Rule , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  David G. Stork,et al.  Pattern Classification , 1973 .

[6]  L. S. Hibbard.  Maximum a posteriori segmentation for medical visualization , 1998, Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162).

[7]  Koenraad Van Leemput,et al.  Automated model-based tissue classification of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.

[8]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[9]  Tomi Heinonen,et al.  Applications of magnetic resonance image segmentation in neurology , 1999, Medical Imaging.

[10]  Baba C. Vemuri,et al.  An Accurate and Efficient Bayesian Method for Automatic Segmentation of Brain MRI , 2002, ECCV.

[11]  R P Velthuizen,et al.  MRI segmentation: methods and applications. , 1995, Magnetic resonance imaging.

[12]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[13]  M. Stella Atkins,et al.  Fully automatic segmentation of the brain in MRI , 1998, IEEE Transactions on Medical Imaging.

[14]  M. Stella Atkins,et al.  Difficulties of T1 brain MRI segmentation techniques , 2002, SPIE Medical Imaging.