2009
DOI: 10.1109/titb.2008.2010855
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Abstract: Indirect immunofluorescence is currently the recommended method for the detection of antinuclear autoantibodies (ANA). The diagnosis consists of both estimating the fluorescence intensity and reporting the staining pattern for positive wells only. Since resources and adequately trained personnel are not always available for these tasks, an evident medical demand is the development of computer-aided diagnosis (CAD) tools that can support the physician decisions. In this paper, we present a system that classifie… Show more

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Cited by 92 publications
(64 citation statements)
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“…In the literature, Perner et al [23] use automatic thresholding via Otsu's algorithm to segment the individual cells, followed by extracting a set of textural features and use decision tree classifier. Soda et al [24] utilize a multiple expert system based on a set of specific features related to statistical and spectral components to assign the pattern of single cell. Cordelli and Soda [25] experimentally compare four different methods for converting a color image into a gray scale one, which are weighted conversion, green channel, intensity channel, and HelmholtzKohlrausch (HK) conversion.…”
Section: Related Workmentioning
confidence: 99%
“…In the literature, Perner et al [23] use automatic thresholding via Otsu's algorithm to segment the individual cells, followed by extracting a set of textural features and use decision tree classifier. Soda et al [24] utilize a multiple expert system based on a set of specific features related to statistical and spectral components to assign the pattern of single cell. Cordelli and Soda [25] experimentally compare four different methods for converting a color image into a gray scale one, which are weighted conversion, green channel, intensity channel, and HelmholtzKohlrausch (HK) conversion.…”
Section: Related Workmentioning
confidence: 99%
“…Perner et al [23] Textural Decision Tree Hiemann et al [15] Structural; textural LogisticModel Tree Elbischger et al [8] Image statistics; cell shape; textural Nearest Neighbour (NN) Hsieh et al [16] Image statistics; textural Learning Vector Quantisation (LVQ) Soda et al [34] Specific set of features (e.g. textural) for each class Multi Expert System Cordelli et al [7] Image statistics; textural; morphological AdaBoost Strandmark et al [35] Morphological; image statistics; textural Random Forest Ali et al [2] Biological-Inspired Descriptor Boosted k-NN Classifier Theodorakopoulos et al [36] Morphological and texture features Kernel SVM (KSVM) Thibault et al [37] Morphological and texture features Linear Regression, Random Forest Ghosh et al [13] Histograms of Oriented Gradients, SVM image statistics and textural Li et al [19] Textural and image statistics SVM Di Cataldo et al [5] GLCM and DCT features SVM Snell et al [33] Texture and shape Multistage classifier Ersoy et al [9] Local shape measures, gradient and textural ShareBoost Wiliem et al [41] Bag of visual words with dual-region structure Nearest Convex Hull Classifier (NCH) and apply an automated feature selection process [15].…”
Section: Approach Descriptors Classifiermentioning
confidence: 99%
“…textural) for each class Multi Expert System Cordelli et al [7] Image statistics; textural; morphological AdaBoost Strandmark et al [35] Morphological; image statistics; textural Random Forest Ali et al [2] Biological-Inspired Descriptor Boosted k-NN Classifier Theodorakopoulos et al [36] Morphological and texture features Kernel SVM (KSVM) Thibault et al [37] Morphological and texture features Linear Regression, Random Forest Ghosh et al [13] Histograms of Oriented Gradients, SVM image statistics and textural Li et al [19] Textural and image statistics SVM Di Cataldo et al [5] GLCM and DCT features SVM Snell et al [33] Texture and shape Multistage classifier Ersoy et al [9] Local shape measures, gradient and textural ShareBoost Wiliem et al [41] Bag of visual words with dual-region structure Nearest Convex Hull Classifier (NCH) and apply an automated feature selection process [15]. Another approach uses Multi Expert Systems to allow the use of a specifically tailored feature set and classifier for each HEp-2 cell pattern class [34]. Nevertheless, the generalisation ability of these systems is still not guaranteed since these systems were only evaluated on a dataset with a specific setup.…”
Section: Approach Descriptors Classifiermentioning
confidence: 99%
“…This necessitates automation of the interpretation of ANA HEp-2 cell images. Several computerbased methods for classification of cell images [12][13][14][15] as well as preprocessing segmentation methods [16] were proposed in the literature, but they have not been benchmarked.…”
Section: Introductionmentioning
confidence: 99%