Robust regression for image binarization under heavy noise and nonuniform background

Abstract This paper presents a robust regression approach for image binarization under significant background variations and observation noise. The work is motivated by the need of identifying foreground regions in noisy microscopic images or degraded document images, where significant background variations and observation noise make image binarization challenging. The proposed method first estimates the background of an input image, subtracts the estimated background from the input image, and performs a global thresholding operation to the subtracted outcome thus achieving the binary image of the foreground. A robust regression approach is proposed to estimate the background intensity surface with minimal effects of the foreground intensities and observation noise, and a global threshold selector is proposed on the basis of a model selection criterion in a sparse regression. The proposed approach is validated using 26 test images and the corresponding ground truths, and the outcomes are compared with those of nine existing image binarization methods. The approach is also combined with three morphological segmentation methods to show how the proposed approach can improve their image segmentation outcomes.

[1]  Shijian Lu,et al.  Robust Document Image Binarization Technique for Degraded Document Images , 2013, IEEE Transactions on Image Processing.

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

[3]  Hyung Jeong Yang,et al.  An MRF model for binarization of music scores with complex background , 2016, Pattern Recognit. Lett..

[4]  Bin Yu,et al.  Minimum Description Length Model Selection Criteria for Generalized Linear Models , 2003 .

[5]  Nikos Papamarkos,et al.  An Evaluation Technique for Binarization Algorithms , 2008, J. Univers. Comput. Sci..

[6]  Oliver Schmitt,et al.  Morphological multiscale decomposition of connected regions with emphasis on cell clusters , 2009, Comput. Vis. Image Underst..

[7]  Cullen Jennings,et al.  Thresholding using an illumination model , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[8]  J. R. Parker,et al.  Gray Level Thresholding in Badly Illuminated Images , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Khairuddin Omar,et al.  Adaptive binarization method for degraded document images based on surface contrast variation , 2015, Pattern Analysis and Applications.

[10]  J J Vaquero,et al.  Applying watershed algorithms to the segmentation of clustered nuclei. , 1998, Cytometry.

[11]  Axel Munk,et al.  Dynamic threshold using polynomial surface regression with application to the binarization of fingerprints , 2005, SPIE Defense + Commercial Sensing.

[12]  Peter J. Rousseeuw,et al.  Robust regression and outlier detection , 1987 .

[13]  N. Phansalkar,et al.  Adaptive local thresholding for detection of nuclei in diversity stained cytology images , 2011, 2011 International Conference on Communications and Signal Processing.

[14]  Yu-Kun Lai,et al.  Efficient Circular Thresholding , 2014, IEEE Transactions on Image Processing.

[15]  Nilanjan Ray,et al.  Image thresholding by variational minimax optimization , 2009, Pattern Recognit..

[16]  Ioannis Pratikakis,et al.  ICDAR 2013 Document Image Binarization Contest (DIBCO 2013) , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[17]  Pheng-Ann Heng,et al.  A double-threshold image binarization method based on edge detector , 2008, Pattern Recognit..

[18]  Konstantinos Zagoris,et al.  ICFHR2016 Handwritten Document Image Binarization Contest (H-DIBCO 2016) , 2016, 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR).

[19]  Nicholas R. Howe,et al.  Document binarization with automatic parameter tuning , 2013, International Journal on Document Analysis and Recognition (IJDAR).

[20]  Derek Bradley,et al.  Adaptive Thresholding using the Integral Image , 2007, J. Graph. Tools.

[21]  Raúl Rojas,et al.  Transition pixel: A concept for binarization based on edge detection and gray-intensity histograms , 2010, Pattern Recognit..

[22]  Inderjit S. Dhillon,et al.  Coordinate-wise Power Method , 2016, NIPS.

[23]  B. Kapralos,et al.  I An Introduction to Digital Image Processing , 2022 .

[24]  Andrew G. Dempster,et al.  Blood Cell Segmentation Using Minimum Area Watershed and Circle Radon Transformations , 2005, ISMM.

[25]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[26]  Liansheng Wang,et al.  Broken and degraded document images binarization , 2017, Neurocomputing.

[27]  Mohamed Cheriet,et al.  A spatially adaptive statistical method for the binarization of historical manuscripts and degraded document images , 2011, Pattern Recognit..

[28]  Matti Pietikäinen,et al.  Adaptive document image binarization , 2000, Pattern Recognit..

[29]  Shijian Lu,et al.  Document image binarization using background estimation and stroke edges , 2010, International Journal on Document Analysis and Recognition (IJDAR).

[30]  Chunheng Wang,et al.  Degraded document image binarization using structural symmetry of strokes , 2018, Pattern Recognit..

[31]  Heikki Haario,et al.  Segmentation of Overlapping Elliptical Objects in Silhouette Images , 2015, IEEE Transactions on Image Processing.

[32]  I. Johnstone,et al.  Threshold selection for wavelet shrinkage of noisy data , 1994, Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[33]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[34]  Mohamed Cheriet,et al.  AdOtsu: An adaptive and parameterless generalization of Otsu's method for document image binarization , 2012, Pattern Recognit..

[35]  Ioannis Pratikakis,et al.  Adaptive degraded document image binarization , 2006, Pattern Recognit..

[36]  Shijian Lu,et al.  Binarization of historical document images using the local maximum and minimum , 2010, DAS '10.

[37]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[38]  Haiping Lu,et al.  Distance-reciprocal distortion measure for binary document images , 2004, IEEE Signal Processing Letters.

[39]  Yu Ding,et al.  Segmentation, Inference and Classification of Partially Overlapping Nanoparticles , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Bidyut Baran Chaudhuri,et al.  An efficient method based on watershed and rule-based merging for segmentation of 3-D histo-pathological images , 2001, Pattern Recognit..