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Centromere and cytoplasmic staining pattern recognition: a local approach

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Autoimmune diseases are very serious and also invalidating illnesses. The benchmark procedure for their diagnosis is the indirect immunofluorescence (IIF) assay performed on the HEp-2 substrate. Medical doctors first determine the fluorescence intensity exhibited by HEp-2 wells and then report the staining pattern. Despite its pivotal role, IIF is affected by inter- and intra-laboratory variabilities demanding for the development of computer-aided-diagnosis tools supporting medical doctor decisions. With reference to staining pattern recognition, state-of-the-art approaches recognize five main patterns characterized by well-defined cell edges. These approaches are based on cell segmentation, a task that recent work suggests to be harder than the classification itself. In this paper, we extend the panel of detectable HEp-2 staining patterns, introducing the recognition of centromere and cytoplasmic patterns, which have a high specific match with certain autoimmune diseases, from other stainings. Since image segmentation algorithms fail on these samples, we developed a classification system integrating local descriptors and the bag of visual word approach, which represents image contents without the burden of segmentation. We tested our approach on a large dataset of HEp-2 images with high variability in both fluorescence intensity and staining patterns correctly recognizing the 97.12 % of samples. The system has also been validated in a daily routine fashion on 108 consecutive IIF analyses of hospital outpatients and inpatients, achieving an accuracy rate of 97.22 %.

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  1. André B, Vercauteren T, Perchant A, Buchner A, Wallace M, Ayache N (2009) Endomicroscopic image retrieval and classification using invariant visual features. In: Biomedical imaging: from nano to macro, 2009. ISBI’09. IEEE international symposium on, pp 346–349. IEEE

  2. Bizzaro N, Tozzoli R, Tonutti E, Piazza A, Manoni F, Ghirardello A, Bassetti D, Villalta D, Pradella M, Rizzotti P (1998) Variability between methods to determine ANA, anti-dsDNA and anti-ENA autoantibodies: a collaborative study with the biomedical industry. J Immunol Methods 219(1):99–107

    Article  PubMed  CAS  Google Scholar 

  3. Boonstra K, Beuers U, Ponsioen CY (2012) Epidemiology of primary sclerosing cholangitis and primary biliary cirrhosis: a systematic review. J Hepatol 56(5):1181–1188

    Article  PubMed  Google Scholar 

  4. Caicedo J, Cruz A, Gonzalez F (2009) Histopathology image classification using bag of features and kernel functions. Artif Intell Med 5651:126–135

    Article  Google Scholar 

  5. Center for Disease Control (1996) Quality assurance for the indirect immunofluorescence test for autoantibodies to nuclear antigen (IF-ANA): approved guideline. NCCLS I/LA2-A 16(11)

  6. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:27:1–27:27. Software available at

  7. Chifflot H, Fautrel B, Sordet C, Chatelus E, Sibilia J (2008) Incidence and prevalence of systemic sclerosis: a systematic literature review. Semin Arthr Rheum 37(4):223–235

    Article  Google Scholar 

  8. Csurka G, Dance C, Fan L, Willamowski J, Bray C (2004) Visual categorization with bags of keypoints. In: Workshop on statistical learning in computer vision, ECCV, vol 1, p 22

  9. Dumais S, Platt J, Heckerman D, Sahami M (1998) Inductive learning algorithms and representations for text categorization. In: Proceedings of the seventh international conference on Information and knowledge management, CIKM ’98, pp 148–155

  10. Egerer K, Roggenbuck D, Hiemann R, Weyer M, Büttner T, Radau B, Krause R, Lehmann B, Feist E, Burmester G (2010) Automated evaluation of autoantibodies on human epithelial-2 cells as an approach to standardize cell-based immunofluorescence tests. Arthr Res Therapy 12(2):40

    Article  Google Scholar 

  11. Ergul E, Arica N (2010) Scene classification using spatial pyramid of latent topics. In: IEEE international conference on pattern recognition, pp 3603–3606. IEEE

  12. Fawcett T. (2004) ROC graphs: notes and practical considerations for researchers. Mach Learn 31:1–38

    Google Scholar 

  13. Foggia P, Percannella G, Soda P, Vento M (2010) Early experiences in mitotic cells recognition on HEp-2 slides. In: Computer-based medical systems (CBMS), 2010 IEEE 23rd international symposium on, pp 38–43. IEEE

  14. Gabriel S, Michaud K (2009) Epidemiological studies in incidence, prevalence, mortality, and comorbidity of the rheumatic diseases. Arthr Res Therapy 11(3):229

    Article  Google Scholar 

  15. Gabrilovich E, Markovitch S (2004) Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5. In: Proceedings of the twenty-first international conference on machine learning, p 41

  16. Hiemann R, Büttner T, Krieger T, Roggenbuck D, Sack U, Conrad K (2009) Challenges of automated screening and differentiation of non-organ specific autoantibodies on hep-2 cells. Autoimmun Rev 9(1):17–22

    Article  PubMed  CAS  Google Scholar 

  17. Hiemann R, Hilger N, Michel J, Nitscke J, Böhm A, Anderer U, Weigert M, Sack U (2007) Automatic analysis of immunofluorescence patterns of HEp-2 cells. Ann N Y Acad Sci 1109(1):358–371

    Article  PubMed  Google Scholar 

  18. Hiemann R, Hilger N, Sack U, Weigert M (2006) Objective quality evaluation of fluorescence images to optimize automatic image acquisition. Cytom Part A 69(3):182–184

    Article  Google Scholar 

  19. Hsu W, Chang S (2005) Visual cue cluster construction via information bottleneck principle and kernel density estimation. Image and Video Retrieval, pp 591–591

  20. Huang YL, Chung CW, Hsieh TY, Jao YL (2008) Outline detection for the HEp-2 cells in indirect immunofluorescence images using watershed segmentation. In: Sensor networks, ubiquitous and trustworthy computing, 2008. SUTC’08. IEEE international conference on, pp 423–427

  21. Huang YL, Jao YL, Hsieh TY, Chung CW (2008) Adaptive automatic segmentation of HEp-2 cells in indirect immunofluorescence images. In: Sensor networks, ubiquitous and trustworthy computing, 2008. SUTC’08. IEEE international conference on, pp 418–422

  22. Iannello G, Onofri L, Punzo G, Soda P (2011) An efficient autofocus algorithm for indirect immunofluorescence applications. In: Computer-based medical systems (CBMS), 2011 24th international symposium on, pp 1–6. IEEE

  23. Iannello G, Onofri L, Soda P (2012) A bag of visual words approach for centromere and cytoplasmic staining pattern classification on hep-2 images. In: Computer-based medical systems (CBMS), 2012 25th international symposium on, pp 1–6. IEEE

  24. Jacobson D, Gange S, Rose N, Graham N et al (1997) Epidemiology and estimated population burden of selected autoimmune diseases in the united states. Clin Immunol Immunopathol 84(3):223

    Article  PubMed  CAS  Google Scholar 

  25. Kavanaugh A, Tomar R, Reveille J, Solomon DH, Homburger HA (2000) Guidelines for clinical use of the antinuclear antibody test and tests for specific autoantibodies to nuclear antigens. Am Coll Pathol Arch Pathol Lab Med 124(1):71–81

    CAS  Google Scholar 

  26. Keerthi SS, Lin CJ (2003) Asymptotic behaviors of support vector machines with gaussian kernel. Neural Comput 15(7):1667–1689

    Article  PubMed  Google Scholar 

  27. Liu J, Shah M (2008) Learning human actions via information maximization. In: Computer vision and pattern recognition, 2008. CVPR 2008. IEEE conference on, pp 1–8. IEEE

  28. Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  29. Meroni P, Schur P (2010) ANA screening: an old test with new recommendations. Ann Rheum Dis 69(8):1420–1422

    Article  PubMed  CAS  Google Scholar 

  30. Percannella G, Soda P, Vento M (2012) A classification-based approach to segment hep-2 cells. In: Computer-based medical systems (CBMS), 2012 25th international symposium on, pp 1–5. IEEE

  31. Perner P, Perner H, Müller B (2002) Mining knowledge for HEp-2 cell image classification. Artif Intell Med 26(1):161–173

    Article  PubMed  Google Scholar 

  32. Rigon A, Soda P, Zennaro D, Iannello G, Afeltra A (2007) Indirect immunofluorescence in autoimmune diseases: assessment of digital images for diagnostic purpose. Cytom Part B Clin Cytom 72B(6):472–477

    Article  Google Scholar 

  33. Sack U, Knoechner S, Warschkau H, Pigla U, Emmrich F, Kamprad M et al (2003) Computer-assisted classification of HEp-2 immunofluorescence patterns in autoimmune diagnostics. Autoimmun Rev 5(2):298–304

    Article  Google Scholar 

  34. Situ N, Yuan X, Chen J, Zouridakis G (2008) Malignant melanoma detection by bag-of-features classification. In: Engineering in medicine and biology society, 2008. EMBS 2008. 30th annual international conference of the IEEE, pp 3110–3113. IEEE

  35. Slonim N, Tishby N (1999) Agglomerative information bottleneck. Adv Neural Inf Process Syst 12:617–623

    Google Scholar 

  36. Soda P (2011) A multi-objective optimisation approach for class-imbalance learning. Pattern Recogn 44:1801–1810

    Article  Google Scholar 

  37. Soda P, Iannello G (2009) Aggregation of classifiers for staining pattern recognition in antinuclear autoantibodies analysis. Inf Technol Biomed IEEE Trans 13(3):322–329

    Article  Google Scholar 

  38. Soda P, Iannello G, Vento M (2009) A multiple experts system for classifying fluorescence intensity in antinuclear autoantibodies analysis. Pattern Anal Appl 12(3):215–226

    Article  Google Scholar 

  39. Soda P, Onofri L, Iannello G (2011) A decision support system for Crithidia Luciliae image classification. Artif Intell Med 51(1):67–74

    Article  PubMed  Google Scholar 

  40. Song L, Hennink EJ, Young IT, Tanke HJ (1995) Photobleaching kinetics of fluorescein in quantitative fluorescence microscopy. Biophys J 68(6):2588–2600

    Article  PubMed  CAS  Google Scholar 

  41. Tommasi T, Orabona F, Caputo B (2008) Discriminative cue integration for medical image annotation. Pattern Recogn Lett 29(15):1996–2002. doi:10.1016/j.patrec.2008.03.009

    Article  Google Scholar 

  42. Tuytelaars T, Mikolajczyk K (2008) Local invariant feature detectors: a survey. Foundations Trends® Comput Graph Vis 3(3):177–280

    Article  Google Scholar 

  43. Winn J, Criminisi A, Minka T (2005) Object categorization by learned universal visual dictionary. In: Computer vision, 2005. ICCV 2005. Tenth IEEE international conference on, vol 2, pp 1800–1807. IEEE

  44. Zhang J, Marszałek M, Lazebnik S, Schmid C (2007) Local features and kernels for classification of texture and object categories: a comprehensive study. Int J Comput Vis 73(2):213–238

    Article  Google Scholar 

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The authors wish to thank Prof. A. Afeltra, Dr. A. Rigon and Dr. F. Buzzulini for their help in collecting and annotating the images. This work has been carried out in the framework of the ITINERIS2 project, Codice CUP F87G10000080009, under the financial support of Regione Lazio (Programme “Sviluppo dell’Innovazione Tecnologica nel Territorio Regionale”, Art. 182, comma 4, lettera c), L.R. no. 4, 28 Aprile 2006).

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Correspondence to Leonardo Onofri.

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Iannello, G., Onofri, L. & Soda, P. Centromere and cytoplasmic staining pattern recognition: a local approach. Med Biol Eng Comput 51, 1305–1314 (2013).

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