Robust and automated unimodal histogram thresholding and potential applications

In this paper, three new histogram-based algorithms are presented to segment images expressing unimodal intensity histograms. These algorithms are applied to laser scanning confocal microscope images (known to often exhibit unimodal histograms) to identify fluorescent signals, and other applications are also shown. The first algorithm facilitates linear diffusion to investigate dynamic histogram features in scale-space. The second algorithm is based on a histogram comparison between a reference area and the whole image at reduced scale. The third algorithm uses the maximisation of a between-class variance criterion applied to image histograms. Results obtained from automatic thresholding of confocal microscopy images show good agreement between the algorithms. Further applications to segment other images are also shown.

[1]  Sorin Draghici,et al.  A Neural Network Based Artificial Vision System for Licence Plate Recognition , 1997, Int. J. Neural Syst..

[2]  Boudewijn P. F. Lelieveldt,et al.  A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering , 2000, IEEE Trans. Image Process..

[3]  Paulo Carvalho,et al.  An automatic optical sensor for vessels and fibbers quality inspection in pulp production , 1999 .

[4]  Hong Yan,et al.  Unified formulation of a class of image thresholding techniques , 1996, Pattern Recognit..

[5]  Laurent Wendling,et al.  Multi-scale binarization of images , 2003, Pattern Recognit. Lett..

[6]  Arun Anand.  Tracing of interference fringes using average gray value and simultaneous row and column scan , 2003 .

[7]  Donald L. Weaver,et al.  Three-dimensional reconstruction by confocal laser scanning microscopy in routine pathologic specimens of benign and malignant lesions of the human breast , 1997, Histochemistry and Cell Biology.

[8]  L. Rodney Long,et al.  Segmentation and feature extraction of cervical spine x-ray images , 1999, Medical Imaging.

[9]  S. Zeisel,et al.  Choline availability alters embryonic development of the hippocampus and septum in the rat. , 1999, Brain research. Developmental brain research.

[10]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[11]  J. Koenderink.  The structure of images , 2004, Biological Cybernetics.

[12]  Stefano Messelodi,et al.  Automatic identification and skew estimation of text lines in real scene images , 1999, Pattern Recognition.

[13]  Maria Petrou,et al.  Image processing - the fundamentals , 1999 .

[14]  C. H. Li,et al.  An iterative algorithm for minimum cross entropy thresholding , 1998, Pattern Recognit. Lett..

[15]  Paul L. Rosin,et al.  Neural fuzzy analysis of delaminated composites from shearography imaging , 2001 .

[16]  B. Parvin,et al.  Applications of quantitative digital image analysis to breast cancer research , 2002, Microscopy research and technique.

[17]  Andrew P. Witkin,et al.  Scale-Space Filtering , 1983, IJCAI.

[18]  J. Ellenberg,et al.  Four-dimensional imaging and quantitative reconstruction to analyse complex spatiotemporal processes in live cells , 2001, Nature Cell Biology.

[19]  Wilfried Philips,et al.  Multiresolution techniques in microscopical image processing. , 2002 .

[20]  C. Antoine,et al.  A robust thresholding algorithm for halftone dots , 2001 .

[21]  Du-Ming Tsai,et al.  A fast thresholding selection procedure for multimodal and unimodal histograms , 1995, Pattern Recognit. Lett..

[22]  Tony Lindeberg,et al.  An automatic assessment scheme for steel quality inspection , 2000, Machine Vision and Applications.

[23]  A. Sönnerborg,et al.  Quantitative single cell methods that identify cytokine and chemokine expression in dendritic cells. , 2001, Journal of immunological methods.

[24]  Marc-Olivier Baradez,et al.  Colocalization Analysis of Sialomucins CD34 and CD164 , 2003, Stem cells.

[25]  R. Spear,et al.  Fluorescent labels, confocal microscopy, and quantitative image analysis in study of fungal biology. , 1999, Methods in enzymology.

[26]  C K Abbey,et al.  Computerized classification of suspicious regions in chest radiographs using subregion Hotelling observers. , 2001, Medical physics.

[27]  Anand Asundi,et al.  Development of Matlab filtering techniques in digital speckle pattern interferometry , 2003 .

[28]  Josef Kittler,et al.  Minimum error thresholding , 1986, Pattern Recognit..

[29]  Luc Florack,et al.  Hierarchical pre-segmentation without prior knowledge , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[30]  András Kornai,et al.  Statistical zone finding , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[31]  B B Chaudhuri,et al.  Some efficient methods to correct confocal images for easy interpretation. , 2001, Micron.

[32]  Vadakke Matham Murukeshan.  Trends in digital speckle pattern interferometry , 2003 .

[33]  Marios A Gavrielides,et al.  Parameter optimization of a computer-aided diagnosis scheme for the segmentation of microcalcification clusters in mammograms. , 2002, Medical physics.

[34]  T. Wilson,et al.  Adaptive aberration correction in a confocal microscope , 2002, Proceedings of the National Academy of Sciences of the United States of America.