<title>Comparison of different automatic threshold algorithms for image segmentation in microscope images</title>

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 ofthe 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 ofthe background and ofthe specimen. We have studied three different methods ofthreshold determination:1 . a statistical procedure, which uses the interclass entropy maximisation of the gray level histogram. The iterative algorithm can be used for multi-threshold segmentation. The contribution of iteration step i is 2'' 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.

Efficient and Effective Extraction of Vocal Fold Vibratory Patterns from High-Speed Digital Imaging

“…For the histogram method to be effective, there should be a distinct gap between the object and background intensities. 6,10,24 During the stage of glottal closure, there is no glottal gap to detect and thus no low-intensity peak in the histogram, making it difficult to determine an accurate threshold value. These factors dramatically increase the computation error in finding the glottal area from the histogram.…”

“…The active contour method includes numerous iterations in computation. [12][13][14][24][25][26][27][28][29] Thus, the active contour method is much more computationally expensive than the histogram method or our method. This makes the active contour method difficult to apply to the realtime batch processing of thousands of high-speed images.…”

“…As the vocal fold image contains both high-intensity (surrounding tissue) and low-intensity (glottal gap) areas, then the vale in between the two can be treated as the threshold level. The threshold is determined by finding the first local minimum in the histogram, 24 similarly to the method used by Wittenberg et al 10 Based on the threshold derived from the histogram algorithm, we may separate the glottal gap from the surrounding tissues. Due to its simplicity, the histogram algorithm has high efficiency in batch processing large amounts of image data.…”

Automated comet assay analysis

“…Precise segmentation becomes more difficult when the tail of the comet structure shows no contrast or only low contrast with respect to the background. Common statistical techniques such as histogram analysis, entropy maximization (24), and kmeans clustering, yield unsatisfactory results because of the large background noise of the comet images. Contourbased procedures also fail because of the low contrast of the comets.…”