Multi-resolution border segmentation for measuring spatial heterogeneity of mixed population biofilm bacteria

Multi-resolution image clustering and segmentation interactive system has been developed to analyze the interaction between clusters of heterogeneous microbial populations residing in biofilms. Biofilms are biological microorganisms attached to surfaces, which develop a complex heterogeneous three-dimensional structure. The hierarchical structural analysis concept underlying multi-resolution image segmentation is that the clusters will be more complex and noisy for higher-resolution while less complex and smoother for lower-resolution image. This hierarchical structure analysis can be used to simplify the image storage and retrieval in well-mixed populations. We are proposing an algorithm that combines Fuzzy C-Means, SOM and LVQ neural networks to segment and identify clusters. The outcome of the image segmentation is quantified by the number of cluster objects of each kind of microorganism within sections of the biofilm, and the centroid distances between the identified cluster objects. Experimental evaluations of the algorithm showed its effectiveness in enumerating cluster objects of bacteria in dual-species biofilms at the substratum and measuring the associated intercellular distances.

[1]  J. Keasling,et al.  A constructed microbial consortium for biodegradation of the organophosphorus insecticide parathion , 2003, Applied Microbiology and Biotechnology.

[2]  Helge J. Ritter,et al.  Adaptive color segmentation-a comparison of neural and statistical methods , 1997, IEEE Trans. Neural Networks.

[3]  H. O'Connell,et al.  Influences of Biofilm Structure and Antibiotic Resistance Mechanisms on Indirect Pathogenicity in a Model Polymicrobial Biofilm , 2006, Applied and Environmental Microbiology.

[4]  M. Ahmadi,et al.  New methods for contour detection and automatic thresholding , 1995, Canadian Journal of Electrical and Computer Engineering.

[5]  Sim Heng Ong,et al.  Segmentation of color images using a two-stage self-organizing network , 2002, Image Vis. Comput..

[6]  Georges Stamon,et al.  Color segmentation of biological microscopic images , 1999, Electronic Imaging.

[7]  Patrick Shen-Pei Wang,et al.  A new method of color image segmentation based on intensity and hue clustering , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[8]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Hong Yan,et al.  Color image segmentation using color space analysis and fuzzy clustering , 2000, Neural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No.00TH8501).

[10]  Jing Li Wang,et al.  Color image segmentation: advances and prospects , 2001, Pattern Recognit..

[11]  Heung-Won Park,et al.  Image segmentation of color image based on region coherency , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[12]  M. Kamel,et al.  A neural network approach for the automatic detection of microaneurysms in retinal angiograms , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[13]  Aly A. Farag,et al.  A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data , 2002, IEEE Transactions on Medical Imaging.

[14]  Saeid Belkasim,et al.  Phase-based optimal image thresholding , 2003, Digit. Signal Process..