Home   >   CSC-OpenAccess Library   >    Manuscript Information
Qualitative and Quantitative Evaluation of Two New Histogram Limiting Binarization Algorithms
Jan Brocher
Pages - 30 - 48     |    Revised - 24-02-2014     |    Published - 19-03-2014
Volume - 8   Issue - 2    |    Publication Date - March 2014  Table of Contents
Automatic Segmentation, Intensity Thresholds, Binarization Quality Assessment, Quantitative Segmentation Evaluation, ImageJ.
Image segmentation and thus feature extraction by binarization is a crucial aspect during image processing. The "most" critical criteria to improve further analysis on binary images is a least- biased comparison of different algorithms to identify the one performing best. Therefore, fast and easy-to-use evaluation methods are needed to compare different automatic intensity segmentation algorithms among each other. This is a difficult task due to variable image contents, different histogram shapes as well as specific user requirements regarding the extracted image features. Here, a new color-coding-based method is presented which facilitates semi-automatic qualitative as well as quantitative assessment of binarization methods relative to an intensity reference point. The proposed method represents a quick and reliable, quantitative measure for relative binarization quality assessment for individual images. Moreover, two new binarization algorithms based on statistical histogram values and initial histogram limitation are presented. This mode-limited mean (MoLiM) as well as the differential-limited mean (DiLiM) algorithms were implemented in ImageJ and compared to 22 existing global as well as local automatic binarization algorithms using the evaluation method described here. Results suggested that MoLiM quantitatively outperformed 11 and DiLiM 8 of the existing algorithms.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
A. Kefali, T. Sari, and M. Sellami, “Evaluation of several binarization techniques for old Arabic documents images,” in The First International Symposium on Modeling and Implementing Complex Systems MISC, 2010, no. 1, pp. 88–99.
A. Najjar and E. Zagrouba, “An Unsupervised Evaluation Measure of Image Segmentation?: Application to Flower Image,” pp. 448–457, 2012.
C. A. Glasbey, “An Analysis of Histogram-Based Thresholding Algorithms,” CVGIP:Graphical Models and Image Processing, vol. 55, no. 6. pp. 532–537, 1993.
C. A. Schneider, W. S. Rasband, and K. W. Eliceiri, “NIH Image to ImageJ: 25 years of image analysis,” Nat. Methods, vol. 9, no. 7, pp. 671–675, Jun. 2012.
F. Ge, S. Wang, and T. Liu, “Image-Segmentation Evaluation From the Perspective of Salient Object Extraction,” 2006 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit.- Vol. 1, vol. 1, pp. 1146–1153, 2006.
G. Landini, “Auto Local Thresholds,” 2013. [Online]. Available:http://fiji.sc/Auto_Local_Threshold. [Accessed: 29-Nov-2013].
G. Landini, “Auto Thresholds,” 2013. [Online]. Available:http://fiji.sc/wiki/index.php/Auto_Threshold. [Accessed: 29-Nov-2013].
H. Zhang, J. E. Fritts, and S. a. Goldman, “An entropy-based objective evaluation method for image segmentation,” no. 1, pp. 38–49, Dec. 2003.
H. Zhang, J. E. Fritts, and S. a. Goldman, “Image segmentation evaluation: A survey of unsupervised methods,” Comput. Vis. Image Underst., vol. 110, no. 2, pp. 260–280, May 2008.
H. Zhang, S. Cholleti, S. A. Goldman, and J. E. Fritts, “Meta-Evaluation of Image Segmentation Using Machine Learning,” in EEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR’06), vol. 1, pp. 1138–1145.
J. A. Shufelt, “Performance evaluation and analysis of monocular building extraction from aerial imagery,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 21, no. 4, pp. 311–326, Apr.1999.
J. Brocher, “BioVoxxel Fiji update site,” 2014. [Online]. Available:http://sites.imagej.net/BioVoxxel/. [Accessed: 05-Mar-2014].
J. Brocher, “BioVoxxel Toolbox,” 2014. [Online]. Available: http://fiji.sc/BioVoxxel_Toolbox.[Accessed: 05-Mar-2014].
J. Brocher, “Theshold Check (BioVoxxel Toolbox),” 2014. [Online]. Available:http://fiji.sc/BioVoxxel_Toolbox#Threshold_Check. [Accessed: 05-Mar-2014].
J. Freixenet, X. Mu, D. Raba, J. Mart, and X. Cuf, “Yet Another Survey on Image Segmentation?: Region and Boundary Information Integration,” ECCV, pp. 408–422, 2002.
J. K. Udupa, V. R. LeBlanc, Y. Zhuge, C. Imielinska, H. Schmidt, L. M. Currie, B. E.Hirsch, and J. Woodburn, “A framework for evaluating image segmentation algorithms,”Comput. Med. Imaging Graph., vol. 30, no. 2, pp. 75–87, Mar. 2006.
J. Kittler and J. Illingworth, “Minimum error thresholding,” Pattern Recognit., vol. 19, no. 1,pp. 41–47, Jan. 1986.
J. M. S. Prewitt and M. L. Mendelsohn, “THE ANALYSIS OF CELL IMAGES*,” Ann. N. Y.Acad. Sci., vol. 128, no. 3, pp. 1035–1053, Dec. 2006.
J. S. Weszka and A. Rosenfeld, “Threshold Evaluation Techniques,” IEEE Trans. Syst.Man. Cybern., vol. 8, no. 8, pp. 622–629, Aug. 1978.
J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S.Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J.-Y. Tinevez, D. J. White, V. Hartenstein,K. Eliceiri, P. Tomancak, and A. Cardona, “Fiji: an open-source platform for biologicalimage analysis.,” Nat. Methods, vol. 9, no. 7, pp. 676–82, Jul. 2012.
K. Ntirogiannis, B. Gatos, and I. Pratikakis, “An Objective Evaluation Methodology for Document Image Binarization Techniques,” in 2008 The Eighth IAPR International Workshop on Document Analysis Systems, 2008, pp. 217–224.
M. Borsotti, P. Campadelli, and R. Schettini, “Quantitative evaluation of color image segmentation results,” Pattern Recognit. Lett., vol. 19, no. 8, pp. 741–747, 1998.
O. D. Trier and T. Taxt, “Evaluation of binarization methods for document images,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 3, pp. 312–315, Mar. 1995.
P. . Sahoo, S. Soltani, and a. K. . Wong, “A survey of thresholding techniques,” Comput.Vision, Graph. Image Process., vol. 41, no. 2, pp. 233–260, Feb. 1988.
P. Stathis and N. Papamarkos, “An Evaluation Technique for Binarization Algorithms,” vol.14, no. 18, pp. 3011–3030, 2008.
R. Unnikrishnan, C. Pantofaru, and M. Hebert, “A Measure for Objective Evaluation of Image Segmentation Algorithms,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) - Workshops, vol. 3, pp. 34–34.
R. Unnikrishnan, C. Pantofaru, and M. Hebert, “Toward objective evaluation of image segmentation algorithms.,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 6, pp. 929–44, Jun. 2007.
W. Doyle, “Operations Useful for Similarity-Invariant Pattern Recognition,” J. ACM, vol. 9,no. 2, pp. 259–267, Apr. 1962.
“Edwin Smith Papyrus.” [Online]. Available:http://upload.wikimedia.org/wikipedia/commons/b/b4/Edwin_Smith_Papyrus_v2.jpg.[Accessed: 24-Jan-2014].
Dr. Jan Brocher
BioVoxxel 67112, Mutterstadt Germany - Germany