Parallel entropic auto-thresholding

In this paper, we examine a multi-level thresholding algorithm based on a number of phases including peak-search, fuzzy logic and entropy of the fuzzy membership function. Analysis of the algorithm is presented to show its properties and behaviours at the various cascaded stages. The fuzzy entropy function of the image histogram is computed using S-function membership and Shannon's entropy function. To establish a suitable fuzzy region bandwidth, we have used a peak-search method based on successive clipping of the image histogram. Location of the valleys in the entropy function correspond to the certainties within the fuzzy region of the image. These certainties are used to indicate an optimal segmentation pattern for multi-level image thresholding. We compare and contrast this method of thresholding with a maximum entropy method. We have implemented the technique in parallel on a transputer-based machine as well as on a cluster of SUN4 workstations, availing ourselves of the PVM communication kernel. A parallel algorithm for the maximum entropy method is given, which significantly reduces computation times. An objective method is used to evaluate the resulting images.

[1]  Sankar K. Pal,et al.  Automatic grey level thresholding through index of fuzziness and entropy , 1983, Pattern Recognit. Lett..

[2]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..

[3]  Azriel Rosenfeld,et al.  Some experiments on variable thresholding , 1979, Pattern Recognit..

[4]  Graham W. Horgan,et al.  Image analysis for the biological sciences , 1997 .

[5]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[6]  Hua Li,et al.  Fast and Reliable Image Enhancement Using Fuzzy Relaxation Technique , 1989, Pattern Recognition.

[7]  Sang Uk Lee,et al.  A comparative performance study of several global thresholding techniques for segmentation , 1990, Comput. Vis. Graph. Image Process..

[8]  H. R. Keshavan,et al.  An optimal multiple threshold scheme for image segmentation , 1984, IEEE Transactions on Systems, Man, and Cybernetics.

[9]  Shyang Chang,et al.  A new criterion for automatic multilevel thresholding , 1995, IEEE Trans. Image Process..

[10]  T. Pavlidis,et al.  Fuzzy sets and their applications to cognitive and decision processes , 1977 .

[11]  M. Ibrahim Sezan,et al.  A Peak Detection Algorithm and its Application to Histogram-Based Image Data Reduction , 1990, Comput. Vis. Graph. Image Process..

[12]  S. Pal,et al.  Fuzzy geometry in image analysis , 1992 .

[13]  Azriel Rosenfeld,et al.  Image enhancement and thresholding by optimization of fuzzy compactness , 1988, Pattern Recognit. Lett..

[14]  Martin D. Levine,et al.  Dynamic Measurement of Computer Generated Image Segmentations , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[16]  Thomas S. Huang,et al.  Image processing , 1971 .

[17]  L. Zadeh.  Calculus of fuzzy restrictions , 1996 .

[18]  Thierry Pun,et al.  Entropic thresholding, a new approach , 1981 .

[19]  Thierry Pun,et al.  A new method for grey-level picture thresholding using the entropy of the histogram , 1980 .