Comparison of different automatic threshold algorithms for image segmentation in microscope images

Wilfried Boecker; W.-U. Muller; Christian Streffer

doi:10.1117/12.217405

22 August 1995 Comparison of different automatic threshold algorithms for image segmentation in microscope images

Wilfried Boecker, W.-U. Muller, Christian Streffer

Proceedings Volume 2564, Applications of Digital Image Processing XVIII; (1995) https://doi.org/10.1117/12.217405
Event: SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, 1995, San Diego, CA, United States

Abstract

Image segmentation is almost always a necessary step in image processing. The employed threshold algorithms are based on the detection of local minima in the gray level histograms of the entire image. In automatic cell recognition equipment, like chromosome analysis or micronuclei counting systems, flexible and adaptive thresholds are required to consider variation in gray level intensities of the background and of the specimen. We have studied three different methods of threshold determination: 1) a statistical procedure, which uses the interclass entropy maximization of the gray level histogram. The iterative algorithm can be used for multithreshold segmentation. The contribution of iteration step 'i' is 2+i-1) number of thresholds; 2) a numerical approach, which detects local minima in the gray level histogram. The algorithm must be tailored and optimized for specific applications like cell recognition with two different thresholds for cell nuclei and cell cytoplasm segmentation; 3) an artificial neural network, which is trained with learning sets of image histograms and the corresponding interactively determined thresholds. We have investigated feed forward networks with one and two layers, respectively. The gray level frequencies are used as inputs for the net. The number of different thresholds per image determines the output channels. We have tested and compared these different threshold algorithms for practical use in fluorescence microscopy as well as in bright field microscopy. The implementation and the results are presented and discussed.

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Wilfried Boecker, W.-U. Muller, and Christian Streffer "Comparison of different automatic threshold algorithms for image segmentation in microscope images", Proc. SPIE 2564, Applications of Digital Image Processing XVIII, (22 August 1995); https://doi.org/10.1117/12.217405

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