PDE-Based Histogram Modification With Embedded Morphological Processing Of The Level-Sets

This paper describes parallel histogram modification techniques with embedded morphological preprocessing methods within the CNN-UM framework. The procedure is formulated in terms of nonlinear partial differential equations (PDE) and approximated through finite differences in space, resulting in coupled nonlinear ordinary differential equations (ODE). The I/O mapping of the system (containing both local and global couplings) can be calculated by a complex analogic (analog and logic) algorithm executed on a stored program nonlinear array processor, called the cellular nonlinear network universal machine (CNN-UM3). We describe and illustrate how the implementation of the algorithm results in an adaptive multi-thresholding scheme when histogram modification is combined with embedded morphological processing at a finite (low) number of gray-scale levels. This has obvious advantages if the further processing steps are segmentation and/or recognition. Experimental results processing real-life and echocardiography images are measured on different hardware/software platforms, including a 64 × 64 CNN-UM chip (ACE4k6,17).

[1]  G. Sapiro,et al.  Histogram Modification via Differential Equations , 1997 .

[2]  Lin-Bao Yang,et al.  Cellular neural networks: theory , 1988 .

[3]  Ángel Rodríguez-Vázquez,et al.  A CNN UNIVERSAL CHIP IN CMOS TECHNOLOGY , 1996 .

[4]  Tamás Roska,et al.  The CNN universal machine , 1993 .

[5]  Edward R. Dougherty,et al.  An introduction to morphological image processing , 1992 .

[6]  Leon O. Chua,et al.  The CNN paradigm , 1993 .

[7]  Guillermo Sapiro,et al.  Shape preserving local histogram modification , 1999, IEEE Trans. Image Process..

[8]  A. Dawidziuk,et al.  Minimum size 0.5 micron CMOS programmable 48 by 48 CNN test chip , 1997 .

[9]  Tamás Roska,et al.  CNN-based difference-controlled adaptive non-linear image filters , 1998, Int. J. Circuit Theory Appl..

[10]  Tamás Roska,et al.  CNN‐based difference‐controlled adaptive non‐linear image filters , 1998 .

[11]  Leon O. Chua,et al.  Computing with Front Propagation: Active Contour And Skeleton Models In Continuous-Time CNN , 1999, J. VLSI Signal Process..

[12]  Ángel Rodríguez-Vázquez,et al.  ACE4k: An analog I/O 64×64 visual microprocessor chip with 7-bit analog accuracy , 2002, Int. J. Circuit Theory Appl..

[13]  A. Rodriguez-Vazquez,et al.  A 64/spl times/64 CNN universal chip with analog and digital I/O , 1998, 1998 IEEE International Conference on Electronics, Circuits and Systems. Surfing the Waves of Science and Technology (Cat. No.98EX196).

[14]  Tamás Roska,et al.  Adaptive histogram equalization with cellular neural networks , 1996, 1996 Fourth IEEE International Workshop on Cellular Neural Networks and their Applications Proceedings (CNNA-96).

[15]  Guillermo Sapiro,et al.  Contrast Enhancement via Image Evolution Flow , 1997, CVGIP Graph. Model. Image Process..

[16]  Tamás Roska,et al.  The CNN universal machine: an analogic array computer , 1993 .

[17]  Leon O. Chua,et al.  Methods for image processing and pattern formation in Cellular Neural Networks: a tutorial , 1995 .