Extension of Tamura Texture Features for 3D Fluorescence Microscopy

The image descriptors are a very useful tool in the task of classification. In biomedical image analysis, they may characterize either the shape or the internal structure of studied objects. Both characteristics are very important. When analysing cells, their shape is usually determined first. In the second step, their mask may be used for the selection of the area where the texture descriptor should be applied. In this paper, we are going to focus on the texture-based image descriptors called Tamura features. For their basic properties, they seem to be a very promising tool applicable to the biomedical image data. We will apply them to selected types of cell lines and test how they perform. We will also introduce their extension to higher dimensions and show that they give even better results than in the 2D case.

[1]  Reinhard Klein,et al.  Shape retrieval using 3D Zernike descriptors , 2004, Comput. Aided Des..

[2]  Stefan M. Rüger,et al.  Evaluation of Texture Features for Content-Based Image Retrieval , 2004, CIVR.

[3]  Chuen-Horng Lin,et al.  Fast segmentation of porcelain images based on texture features , 2010, J. Vis. Commun. Image Represent..

[4]  Pedro Martínez-Jiménez,et al.  A comparative study of texture coarseness measures , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[5]  Robert F. Murphy,et al.  Location proteomics: building subcellular location trees from high-resolution 3D fluorescence microscope images of randomly tagged proteins , 2003, SPIE BiOS.

[6]  José M. Soto-Hidalgo,et al.  Perceptually-Based Functions for Coarseness Textural Feature Representation , 2007, IbPRIA.

[7]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[8]  Michal Kozubek,et al.  Generation of digital phantoms of cell nuclei and simulation of image formation in 3D image cytometry , 2009, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[9]  Md. Monirul Islam,et al.  A geometric method to compute directionality features for texture images , 2008, 2008 IEEE International Conference on Multimedia and Expo.

[10]  Nicolai Petkov,et al.  Comparison of texture features based on Gabor filters , 2002, IEEE Trans. Image Process..

[11]  Matti Pietikäinen,et al.  Performance evaluation of texture measures with classification based on Kullback discrimination of distributions , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[12]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[13]  Akinobu Shimizu,et al.  3D extension of Haralick texture features for medical image analysis , 2007 .

[14]  Li WangDong-Chen He,et al.  Texture classification using texture spectrum , 1990, Pattern Recognit..

[15]  Alireza Khotanzad,et al.  Invariant Image Recognition by Zernike Moments , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[17]  Jan Flusser,et al.  Implicit Moment Invariants , 2009, International Journal of Computer Vision.

[18]  Nikolaos Canterakis,et al.  3D Zernike Moments and Zernike Affine Invariants for 3D Image Analysis and Recognition , 1999 .

[19]  von F. Zernike Beugungstheorie des schneidenver-fahrens und seiner verbesserten form, der phasenkontrastmethode , 1934 .

[20]  M. R. Turner,et al.  Texture discrimination by Gabor functions , 1986, Biological Cybernetics.

[21]  I. Andreadis,et al.  Image analysis using moments , 2005 .

[22]  Nimrod Megiddo,et al.  Range queries in OLAP data cubes , 1997, SIGMOD '97.

[23]  Scott Grandison,et al.  The Application of 3D Zernike Moments for the Description of "Model-Free" Molecular Structure, Functional Motion, and Structural Reliability , 2009, J. Comput. Biol..

[24]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[27]  Akinobu Shimizu,et al.  Medical image analysis of 3D CT images based on extension of Haralick texture features , 2008, Comput. Medical Imaging Graph..

[28]  Saeid Belkasim,et al.  Content-based Image Retrieval Using Gabor-Zernike Features , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[29]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[30]  John Daugman,et al.  Neural networks for image transformation, analysis, and compression , 1988, Neural Networks.

[31]  M. Teague Image analysis via the general theory of moments , 1980 .

[32]  Richard C. Olsen,et al.  Haralick texture features expanded into the spectral domain , 2006, SPIE Defense + Commercial Sensing.

[33]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[34]  D. Sagi,et al.  Gabor filters as texture discriminator , 1989, Biological Cybernetics.

[35]  Wei-Ying Ma,et al.  Benchmarking of image features for content-based retrieval , 1998, Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284).

[36]  Akinobu Shimizu,et al.  Medical image segmentation using cooccurrence matrix based texture features calculated on weighted region , 2007 .

[37]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[38]  Eizan Miyamoto,et al.  FAST CALCULATION OF HARALICK TEXTURE FEATURES , 2005 .

[39]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[40]  Marcin Novotni,et al.  3D zernike descriptors for content based shape retrieval , 2003, SM '03.

[41]  Larry S. Davis,et al.  Texture Analysis Using Generalized Co-Occurrence Matrices , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[42]  B. S. Manjunath,et al.  Color and texture descriptors , 2001, IEEE Trans. Circuits Syst. Video Technol..