Human epithelial type-2 cell categorization using hybrid descriptor with binary tree

Indirect immunofluorescence (IIF) method with human epithelial type-2 (HEp-2) cell as substrates is recommanded in anti nuclear antibody (ANA) test. These IIF slides are observed under microscope by pathologists to prepare the report. So, the ANA test is subjective and requires systematic automation. This paper proposes a novel algorithm for HEp-2 cellcategorization, which can be implanted in the ANA test automation system. A hybrid descriptor which represents the textural and morphological characters of the objects of interest was used along with Binary tree. The hybrid descriptors were generated by decomposing the image into binary images by using lower and upper thresholds. The performance of the proposed algorithm was evaluated on the ICPR 2016 IIF HEp-2 cell image dataset, the results concluded that Hybrid Descriptor with Binary Tree approach achieved the best performance with “97.5%” mean class accuracy.

[1]  Kamalraj Subramaniam,et al.  HEp-2 cell classification using binary decision tree approach , 2016, 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).

[2]  Alessia Saggese,et al.  Pattern recognition in stained HEp-2 cells: Where are we now? , 2014, Pattern Recognit..

[3]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[4]  Stephen J. McKenna,et al.  HEp-2 Cell Classification Using Multi-resolution Local Patterns and Ensemble SVMs , 2014 .

[5]  Sim Heng Ong,et al.  Classification of HEp-2 cells using distributed dictionary learning , 2016, 2016 24th European Signal Processing Conference (EUSIPCO).

[6]  S. Deutscher,et al.  Long-term outcome in mixed connective tissue disease: longitudinal clinical and serologic findings. , 1999, Arthritis and rheumatism.

[7]  Dimitris Kastaniotis,et al.  HEp-2 cell classification with Vector of Hierarchically Aggregated Residuals , 2017, Pattern Recognit..

[8]  M. Manns,et al.  Autoimmune hepatitis--Update 2015. , 2015, Journal of hepatology.

[9]  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).

[10]  Shijian Lu,et al.  Accurate HEp-2 cell classification based on Sparse Coding of Superpixels , 2016, Pattern Recognit. Lett..

[11]  Alessia Saggese,et al.  International Contest on Pattern Recognition techniques for indirect immunofluorescence images analysis , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

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

[13]  A. Barnett,et al.  Antinuclear antibodies in patients with scleroderma (systemic sclerosis) and in their blood relatives and spouses. , 1993, Annals of the rheumatic diseases.

[14]  Mario Vento,et al.  Benchmarking human epithelial type 2 interphase cells classification methods on a very large dataset , 2015, Artif. Intell. Medicine.

[15]  Dimitris Kastaniotis,et al.  HEp-2 Cells Classification Using Morphological Features and a Bundle of Local Gradient Descriptors , 2014 .

[16]  M. García-Carrasco,et al.  Primary Sjoögren's syndrome in men: clinical and immunological characteristics , 2000, Lupus.

[17]  T. Dörner,et al.  Increased serum soluble CD14, ICAM-1 and E-selectin correlate with disease activity and prognosis in systemic lupus erythematosus , 2000, Lupus.

[18]  A. Bhatia,et al.  Antinuclear antibodies and their detection methods in diagnosis of connective tissue diseases: a journey revisited , 2009, Diagnostic pathology.

[19]  A Kavanaugh,et al.  Guidelines for clinical use of the antinuclear antibody test and tests for specific autoantibodies to nuclear antigens. American College of Pathologists. , 2000, Archives of pathology & laboratory medicine.