Image segmentation with improved watershed algorithm using radial bases function neural networks

This paper proposes an improved watershed segmentation algorithm that uses RBF Neural Networks for the segmentation of image target objects. Instead of using catchment basin minima in order to define object regions, the technique developed throughout this work deploys RBF neural networks to predict the end boundaries of the segmentation clusters which are formed from the watersheds created in the image histogram topography. The RBF initial parameters, such as centers, and widths, are automatically set upon the histogram peaks and minima respectively. Experimental results of this leaning algorithm make it viable for different applications of gray scale image classifications.

[1]  C. F. Sin,et al.  Image segmentation by changing template block by block , 2001, Proceedings of IEEE Region 10 International Conference on Electrical and Electronic Technology. TENCON 2001 (Cat. No.01CH37239).

[2]  Aggelos K. Katsaggelos,et al.  Hybrid image segmentation using watersheds and fast region merging , 1998, IEEE Trans. Image Process..

[3]  Yili Fu,et al.  A fast two-step marker-controlled watershed image segmentation method , 2012, 2012 IEEE International Conference on Mechatronics and Automation.

[4]  Min Hu,et al.  A Novel Model of Image Segmentation Based on Watershed Algorithm , 2013, Adv. Multim..

[5]  Ron Kikinis,et al.  Improved watershed transform for medical image segmentation using prior information , 2004, IEEE Transactions on Medical Imaging.

[6]  Marcel Worring,et al.  Watersnakes: Energy-Driven Watershed Segmentation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Alfredo Petrosino,et al.  Image Analysis and Processing , 2016, Springer US.

[8]  Mohamed Ali Mahjoub,et al.  Automatic liver segmentation method in CT images , 2012, ArXiv.

[9]  R. P. Tewari,et al.  A combined watershed segmentation approach using k-means clustering for mammograms , 2015, 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN).

[10]  Nam Mai-Duy,et al.  Solving biharmonic problems with scattered-point discretization using indirect radial-basis-function networks , 2006 .

[11]  Ghassan Hamarneh,et al.  Watershed segmentation using prior shape and appearance knowledge , 2009, Image Vis. Comput..

[12]  Robert M. Sanner,et al.  Gaussian Networks for Direct Adaptive Control , 1991, 1991 American Control Conference.

[13]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Ian Middleton,et al.  Segmentation of magnetic resonance images using a combination of neural networks and active contour models. , 2004, Medical engineering & physics.

[15]  Cedric Nishan Canagarajah,et al.  Image segmentation using a texture gradient based watershed transform , 2003, IEEE Trans. Image Process..

[16]  S. Pizer,et al.  The Image Processing Handbook , 1994 .

[17]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[18]  Alireza Behrad,et al.  Automatic liver segmentation in MRI images using an iterative watershed algorithm and artificial neural network , 2012, Biomed. Signal Process. Control..

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

[20]  Antonio J. Plaza,et al.  Cluster-Based Implementation of a Morphological Watershed Algorithm for Parallel Classification of Multichannel Images , 2007, Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2007).

[21]  Jon Atli Benediktsson,et al.  A new approach for the morphological segmentation of high-resolution satellite imagery , 2001, IEEE Trans. Geosci. Remote. Sens..

[22]  Aijuan Dong,et al.  Detection of breast tumor candidates using marker-controlled watershed segmentation and morphological analysis , 2012, 2012 IEEE Southwest Symposium on Image Analysis and Interpretation.