Image analysis aided by Segmentation algorithms for techniques ranging from Scanning Probe Microscopy to Optical Microscopy

Microscope image analysis is a common term that covers the use of digital image processing techniques to process and analyse images obtained with all the range of available microscopes, ranging from the Scanning Probe Microscopy (SPM) family, operating down to atomic scale, to Optical Microscopy (OM). Such processing techniques are now commonplace in a number of different fields such as medicine, biology, physics, chemistry, engineering. As a consequence, a number of manufacturers of microscopes now specifically design in features that allow the microscopes to interface to an image processing tool. Nevertheless, in many applications where a rough image analysis is requested, image processing systems are not enough for corrections of specific instrumental distortions or for the correct recognition of unknown sample features. In this chapter, we describe the fundamental aid offered by segmentation techniques for image analysis of microscopes operating on different scales. Several segmentation algorithms are described and commented looking how they work when applied to various scale lengths.

[1]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Serena Danti,et al.  A micro/nanoscale surface mechanical study on morpho-functional changes in multilineage-differentiated human mesenchymal stem cells. , 2007, Macromolecular bioscience.

[3]  Trygve Randen,et al.  Filtering for Texture Classification: A Comparative Study , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Thomas S. Huang,et al.  Image processing , 1971 .

[5]  I. Tummon,et al.  Diagnosis of polycystic ovaries by three-dimensional transvaginal ultrasound. , 2006, Fertility and sterility.

[6]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[7]  D. Donoho,et al.  Translation-Invariant De-Noising , 1995 .

[8]  S. Arumuga Perumal,et al.  Image De-noising using Discrete Wavelet transform , 2008 .

[9]  Brian S. Salmons,et al.  Correction of distortion due to thermal drift in scanning probe microscopy. , 2010, Ultramicroscopy.

[10]  Roberto de Alencar Lotufo,et al.  Watershed from propagated markers: An interactive method to morphological object segmentation in image sequences , 2010, Image Vis. Comput..

[11]  Junaed Sattar Snakes , Shapes and Gradient Vector Flow , 2022 .

[12]  S. Ziganshina,et al.  Computer program for the grain analysis of AFM images of nanoparticles placed on a rough surface , 2006 .

[13]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[14]  Jinglu Tan,et al.  Object density-based image segmentation and its applications in biomedical image analysis , 2009, Comput. Methods Programs Biomed..

[15]  José M. Bioucas-Dias,et al.  Denoising of medical images corrupted by Poisson noise , 2008, 2008 15th IEEE International Conference on Image Processing.

[16]  B. Mandelbrot How Long Is the Coast of Britain ? , 2002 .

[17]  Rostislav V. Lapshin,et al.  Automatic drift elimination in probe microscope images based on techniques of counter-scanning and topography feature recognition , 2007 .

[18]  Jinglu Tan,et al.  Image Processing of Hematoxylin and Eosin-Stained Tissues for Pathological Evaluation , 2004, Toxicology mechanisms and methods.

[19]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[20]  Nuggehally Sampath Jayant,et al.  An adaptive clustering algorithm for image segmentation , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[21]  Xiaolin Wu,et al.  Adaptive Split-and-Merge Segmentation Based on Piecewise Least-Square Approximation , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Leonid P. Yaroslavsky,et al.  Digital Picture Processing , 1985 .

[23]  I. B. Gurevich,et al.  A two-step approach for automatic microscopic image segmentation using fuzzy clustering and neural discrimination , 2007, Pattern Recognition and Image Analysis.

[24]  S. Zucker,et al.  Finding structure in Co-occurrence matrices for texture analysis , 1980 .

[25]  Alexander A. Sawchuk,et al.  Supervised Textured Image Segmentation Using Feature Smoothing and Probabilistic Relaxation Techniques , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Nobuyuki Otsu,et al.  ATlreshold Selection Method fromGray-Level Histograms , 1979 .

[27]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[28]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Alex Pentland,et al.  Fractal-Based Description of Natural Scenes , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Didier Dewailly,et al.  Revisiting the ovarian volume as a diagnostic criterion for polycystic ovaries. , 2005, Human reproduction.

[31]  Karen O. Egiazarian,et al.  Video denoising by sparse 3D transform-domain collaborative filtering , 2007, 2007 15th European Signal Processing Conference.

[32]  Guillermo Sapiro,et al.  Fast image and video denoising via nonlocal means of similar neighborhoods , 2005, IEEE Signal Processing Letters.

[33]  Stanley Osher,et al.  Image Decomposition and Restoration Using Total Variation Minimization and the H1 , 2003, Multiscale Model. Simul..

[34]  B. Mandelbrot How Long Is the Coast of Britain? Statistical Self-Similarity and Fractional Dimension , 1967, Science.

[35]  Leonid P. Yaroslavsky,et al.  Digital Picture Processing: An Introduction , 1985 .

[36]  Alfred M. Bruckstein,et al.  On Gabor's contribution to image enhancement , 1994, Pattern Recognit..

[37]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[38]  Yoonsuck Choe,et al.  Cell tracking and segmentation in electron microscopy images using graph cuts , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[39]  Michael A. Sutton,et al.  Advances in light microscope stereo vision , 2004 .

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

[41]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[42]  Muriel Mari,et al.  Microscopy: Science, Technology, Applications and Education , 2010 .

[43]  Magdy A. Bayoumi,et al.  Image segmentation on a 2D array by a directed split and merge procedure , 1992, IEEE Trans. Signal Process..

[44]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.