Brain MR images segmentation using statistical ratio: Mapping between watershed and competitive Hopfield clustering network algorithms

Conventional watershed segmentation methods invariably produce over-segmented images due to the presence of noise or local irregularities in the source images. In this paper, a robust medical image segmentation technique is proposed, which combines watershed segmentation and the competitive Hopfield clustering network (CHCN) algorithm to minimize undesirable over-segmentation. In the proposed method, a region merging method is presented, which is based on employing the region adjacency graph (RAG) to improve the quality of watershed segmentation. The relation of inter-region similarities is then investigated using image mapping in the watershed and CHCN images to determine more appropriate region merging. The performance of the proposed technique is presented through quantitative and qualitative validation experiments on benchmark images. Significant and promising segmentation results were achieved on brain phantom simulated data.

[1]  Bidyut Baran Chaudhuri,et al.  Region based techniques for segmentation of volumetric histo-pathological images , 2000, Comput. Methods Programs Biomed..

[2]  Jzau-Sheng Lin,et al.  Classification of multispectral images based on a fuzzy-possibilistic neural network , 2002 .

[3]  H. Jeremy Bockholt,et al.  A hybrid tissue segmentation approach for brain MR images , 2006, Medical and Biological Engineering and Computing.

[4]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[5]  Meritxell Bach Cuadra,et al.  Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images , 2005, IEEE Transactions on Medical Imaging.

[6]  Ulla Ruotsalainen,et al.  Automatic extraction of brain surface and mid-sagittal plane from PET images applying deformable models , 2005, Comput. Methods Programs Biomed..

[7]  Alan C. Evans,et al.  MRI Simulation Based Evaluation and Classifications Methods , 1999, IEEE Trans. Medical Imaging.

[8]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Dar-Ren Chen,et al.  Watershed segmentation for breast tumor in 2-D sonography. , 2004, Ultrasound in medicine & biology.

[10]  Jzau-Sheng Lin,et al.  The application of competitive Hopfield neural network to medical image segmentation , 1996, IEEE Trans. Medical Imaging.

[11]  Yi-Ping Hung,et al.  Comparison between immersion-based and toboggan-based watershed image segmentation , 2006, IEEE Transactions on Image Processing.

[12]  Annette Sterr,et al.  MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization , 2005, IEEE Transactions on Information Technology in Biomedicine.

[13]  Maria Frucci.  Oversegmentation reduction by flooding regions and digging watershed lines , 2006, Int. J. Pattern Recognit. Artif. Intell..

[14]  J.L. Marroquin,et al.  An accurate and efficient Bayesian method for automatic segmentation of brain MRI , 2002, IEEE Transactions on Medical Imaging.

[15]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[16]  D. Louis Collins,et al.  Design and construction of a realistic digital brain phantom , 1998, IEEE Transactions on Medical Imaging.

[17]  Pau-Choo Chung,et al.  Polygonal approximation using a competitive Hopfield neural network , 1994, Pattern Recognit..

[18]  Xiaolin Wu,et al.  Adaptive Split-and-Merge Segmentation Based on Piecewise Least-Square Approximation , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Stanley R Sternberg,et al.  Grayscale morphology , 1986 .

[20]  Richard Beare.  A locally constrained watershed transform , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Sun-Yuan Kung,et al.  Quantification and segmentation of brain tissues from MR images: a probabilistic neural network approach , 1998, IEEE Trans. Image Process..

[22]  Jzau-Sheng Lin.  Annealed Hopfield Neural Network with Moment and Entropy Constraints for Magnetic Resonance Image Classification , 2000 .

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

[24]  Vincent Barra,et al.  Segmentation of fat and muscle from MR images of the thigh by a possibilistic clustering algorithm , 2002, Comput. Methods Programs Biomed..

[25]  Dinggang Shen,et al.  COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements , 2007, IEEE Transactions on Medical Imaging.

[26]  Alina N. Moga,et al.  An efficient watershed algorithm based on connected components , 2000, Pattern Recognit..

[27]  Tianzi Jiang,et al.  Multicontext fuzzy clustering for separation of brain tissues in magnetic resonance images , 2003, NeuroImage.

[28]  Jzau-Sheng Lin,et al.  Vector quantization in DCT domain using fuzzy possibilistic c-means based on penalized and compensated constraints , 2002, Pattern Recognit..