HEp-2 cell classification using rotation invariant co-occurrence among local binary patterns

Abstract This paper proposes a novel method for classifying six categories of patterns of fluorescence staining of a HEp-2 cell. The proposed method is constructed as a combination of the powerful rotation invariant co-occurrence among adjacent local binary pattern (RIC-LBP) image feature and a linear support vector machine (SVM). RIC-LBP provides high descriptive ability and robustness against local rotations of an input cell image. To further deal with global rotation, we synthesize many training images by rotating the original training images and constructing the SVM using both the original and synthesized images. The proposed method has the following advantages: (1) robustness against uniform changes in intensity of an input cell image, (2) invariance under local and global rotation of the image, (3) low computational cost, and (4) easy implementation. The proposed method was demonstrated to be effective through evaluation experiments using the MIVIA HEp-2 images dataset and comparison with typical state-of-the-art methods.

[1]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

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

[3]  Mario Vento,et al.  Benchmarking HEp-2 Cells Classification Methods , 2013, IEEE Transactions on Medical Imaging.

[4]  Mario Vento,et al.  A multiple expert system for classifying fluorescent intensity in antinuclear autoantibodies analysis , 2009, Pattern Analysis and Applications.

[5]  Mario Vento,et al.  Early experiences in mitotic cells recognition on HEp-2 slides , 2010, 2010 IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS).

[6]  Richard Nock,et al.  Classification of biological cells using bio-inspired descriptors , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[7]  Kazuhiro Fukui,et al.  Feature Extraction Based on Co-occurrence of Adjacent Local Binary Patterns , 2011, PSIVT.

[8]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.

[9]  PietikainenMatti,et al.  Face Description with Local Binary Patterns , 2006 .

[10]  Fredrik Kahl,et al.  HEp-2 staining pattern classification , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[11]  Paolo Soda,et al.  Color to grayscale staining pattern representation in IIF , 2011, 2011 24th International Symposium on Computer-Based Medical Systems (CBMS).

[12]  Kazuhiro Fukui,et al.  Rotation Invariant Co-occurrence among Adjacent LBPs , 2012, ACCV Workshops.

[13]  Shuicheng Yan,et al.  An HOG-LBP human detector with partial occlusion handling , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[14]  Jing Peng,et al.  HEp-2 cell classification in IIF images using Shareboost , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[15]  Matti Pietikäinen,et al.  Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Giulio Iannello,et al.  Automatic Acquisition of Immunofluorescence Images: Algorithms and Evaluation , 2006, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06).

[17]  Rico Hiemann,et al.  Challenges of automated screening and differentiation of non-organ specific autoantibodies on HEp-2 cells. , 2009, Autoimmunity reviews.

[18]  Matti Pietikäinen,et al.  Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features , 2009, SCIA.

[19]  Matti Pietikäinen,et al.  Unsupervised texture segmentation using feature distributions , 1997, Pattern Recognit..

[20]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[22]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[23]  Yongkang Wong,et al.  Classification of Human Epithelial type 2 cell indirect immunofluoresence images via codebook based descriptors , 2013, 2013 IEEE Workshop on Applications of Computer Vision (WACV).

[24]  Enrico Macii,et al.  Applying textural features to the classification of HEp-2 cell patterns in IIF images , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[25]  Mario Vento,et al.  Special issue on the analysis and recognition of indirect immuno-fluorescence images , 2014, Pattern Recognit..

[26]  Frank Emmrich,et al.  Computer-assisted classification of HEp-2 immunofluorescence patterns in autoimmune diagnostics. , 2003, Autoimmunity reviews.

[27]  Dimitris Kastaniotis,et al.  HEp-2 Cells classification via fusion of morphological and textural features , 2012, 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE).