3,490 research outputs found
Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching
This paper describes a novel system for automatic classification of images
obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial
type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The
IIF protocol on HEp-2 cells has been the hallmark method to identify the
presence of ANAs, due to its high sensitivity and the large range of antigens
that can be detected. However, it suffers from numerous shortcomings, such as
being subjective as well as time and labour intensive. Computer Aided
Diagnostic (CAD) systems have been developed to address these problems, which
automatically classify a HEp-2 cell image into one of its known patterns (eg.
speckled, homogeneous). Most of the existing CAD systems use handpicked
features to represent a HEp-2 cell image, which may only work in limited
scenarios. We propose a novel automatic cell image classification method termed
Cell Pyramid Matching (CPM), which is comprised of regional histograms of
visual words coupled with the Multiple Kernel Learning framework. We present a
study of several variations of generating histograms and show the efficacy of
the system on two publicly available datasets: the ICPR HEp-2 cell
classification contest dataset and the SNPHEp-2 dataset.Comment: arXiv admin note: substantial text overlap with arXiv:1304.126
Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project
Antinuclear antibodies (ANAs) are significant biomarkers in the diagnosis of autoimmune diseases in humans, done by mean of
Indirect ImmunoFluorescence (IIF)method, and performed by analyzing patterns and fluorescence intensity. This paper introduces
the AIDA Project (autoimmunity: diagnosis assisted by computer) developed in the framework of an Italy-Tunisia cross-border
cooperation and its preliminary results. A database of interpreted IIF images is being collected through the exchange of images
and double reporting and a Gold Standard database, containing around 1000 double reported images, has been settled. The Gold
Standard database is used for optimization of aCAD(Computer AidedDetection) solution and for the assessment of its added value,
in order to be applied along with an Immunologist as a second Reader in detection of autoantibodies. This CAD system is able to
identify on IIF images the fluorescence intensity and the fluorescence pattern. Preliminary results show that CAD, used as second
Reader, appeared to perform better than Junior Immunologists and hence may significantly improve their efficacy; compared with
two Junior Immunologists, the CAD system showed higher Intensity Accuracy (85,5% versus 66,0% and 66,0%), higher Patterns
Accuracy (79,3% versus 48,0% and 66,2%), and higher Mean Class Accuracy (79,4% versus 56,7% and 64.2%)
Comparative Study of Human and Automated Screening for Antinuclear Antibodies by Immunofluorescence on HEp-2 Cells
Background: Several automated systems had been developed in order to reduce inter-observer variability in
indirect immunofluorescence (IIF) interpretation. We aimed to evaluate the performance of a processing system in
antinuclear antibodies (ANA) screening on HEp-2 cells.
Patients and Methods: This study included 64 ANA-positive sera and 107 ANA-negative sera that underwent IIF on two
commercial kits of HEp-2 cells (BioSystems® and Euroimmun®). IIF results were compared with a novel automated
interpretation system, the “CyclopusCADImmuno®” (CAD).
Results: All ANA-positive sera images were recognized as positive by CAD (sensitivity = 100%), while 17 (15.9%) of the
ANA-negative sera images were interpreted as positive (specificity = 84.1%), =0.799 (SD=0.045). Comparison of IIF
pattern determination between human and CAD system revealed on HEp-2 (BioSystems®), a complete concordance in
6 (9.37%) sera, a partial concordance (sharing of at least 1 pattern) in 42 (65.6%) cases and in 16 (25%) sera the
pattern interpretation was discordant. Similarly, on HEp-2 (Euroimmun®) the concordance in pattern interpretation was
total in 5 (7.8%) sera, partial in 39 (60.9%) and absent in 20 (31.25%). For both tested HEp-2 cells kits agreement was
enhanced for the most common patterns, homogenous, fine speckled and coarse speckled. While there was an issue in
identification of nucleolar, dots and nuclear membranous patterns by CAD.
Conclusion: Assessment of ANA by IIF on HEp-2 cells using the automated interpretation system, the
“CyclopusCADImmuno®” is a reliable method for positive/negative differentiation. Continuous integration of IIF images
would improve the pattern identification by the CAD
Preliminary results of the project A.I.D.A. (Auto Immunity: Diagnosis Assisted by computer)
In this paper, are presented the preliminary results of the A.I.D.A. (Auto Immunity: Diagnosis
Assisted by computer) project which is developed in the frame of the cross-border cooperation Italy-Tunisia.
According to the main objectives of this project, a database of interpreted Indirect ImmunoFluorescence (IIF)
images on HEp 2 cells is being collected thanks to the contribution of Italian and Tunisian experts involved in
routine diagnosis of autoimmune diseases. Through exchanging images and double reporting; a Gold Standard
database, containing around 1000 double reported IIF images with different patterns including negative tests,
has been settled. This Gold Standard database has been used for optimization of a computing solution (CADComputer
Aided Detection) and for assessment of its added value in order to be used along with an
immunologist as a second reader in detection of auto antibodies for autoimmune disease diagnosis. From the
preliminary results obtained, the CAD appeared more powerful than junior immunologists used as second
readers and may significantly improve their efficacy
HEp-2 Cell Classification with heterogeneous classes-processes based on K-Nearest Neighbours
We present a scheme for the feature extraction and classification of the fluorescence staining patterns of HEp-2 cells in IIF images. We propose a set of
complementary processes specific to each class of patterns to search. Our set of processes consists of preprocessing,features extraction and classification. The choice of methods, features and parameters was performed
automatically, using the Mean Class Accuracy (MCA) as a figure of merit. We extract a large number (108) of features able to fully characterize the staining pattern of HEp-2 cells. We propose a classification approach based
on two steps: the first step follows the one-against-all(OAA) scheme, while the second step follows the one-against-one (OAO) scheme. To do this, we needed to implement 21 KNN classifiers: 6 OAA and 15 OAO.
Leave-one-out image cross validation method was used for the evaluation of the results
Classification of Human Epithelial Type 2 Cell Indirect Immunofluoresence Images via Codebook Based Descriptors
The Anti-Nuclear Antibody (ANA) clinical pathology test is commonly used to
identify the existence of various diseases. A hallmark method for identifying
the presence of ANAs is the Indirect Immunofluorescence method on Human
Epithelial (HEp-2) cells, due to its high sensitivity and the large range of
antigens that can be detected. However, the method suffers from numerous
shortcomings, such as being subjective as well as time and labour intensive.
Computer Aided Diagnostic (CAD) systems have been developed to address these
problems, which automatically classify a HEp-2 cell image into one of its known
patterns (eg., speckled, homogeneous). Most of the existing CAD systems use
handpicked features to represent a HEp-2 cell image, which may only work in
limited scenarios. In this paper, we propose a cell classification system
comprised of a dual-region codebook-based descriptor, combined with the Nearest
Convex Hull Classifier. We evaluate the performance of several variants of the
descriptor on two publicly available datasets: ICPR HEp-2 cell classification
contest dataset and the new SNPHEp-2 dataset. To our knowledge, this is the
first time codebook-based descriptors are applied and studied in this domain.
Experiments show that the proposed system has consistent high performance and
is more robust than two recent CAD systems
An automated pattern recognition system for classifying indirect immunofluorescence images for HEp-2 cells and specimens
AbstractImmunofluorescence antinuclear antibody tests are important for diagnosis and management of autoimmune conditions; a key step that would benefit from reliable automation is the recognition of subcellular patterns suggestive of different diseases. We present a system to recognize such patterns, at cellular and specimen levels, in images of HEp-2 cells. Ensembles of SVMs were trained to classify cells into six classes based on sparse encoding of texture features with cell pyramids, capturing spatial, multi-scale structure. A similar approach was used to classify specimens into seven classes. Software implementations were submitted to an international contest hosted by ICPR 2014 (Performance Evaluation of Indirect Immunofluorescence Image Analysis Systems). Mean class accuracies obtained on heldout test data sets were 87.1% and 88.5% for cell and specimen classification respectively. These were the highest achieved in the competition, suggesting that our methods are state-of-the-art. We provide detailed descriptions and extensive experiments with various features and encoding methods
PROFESSIONAL CONDUCT AND KNOWLEDGE GAPS CONCERNING CHAGAS DISEASE IN INTERRUPTED VECTOR-BORNE TRANSMISSION AREA
Aiming to verify gaps in the conduct and knowledge of health professionals concerning Chagas disease in Maring and Paiandu, Paran State, Brazil, from September/2004 to July/2005. The participants were chosen by systematic sampling. A total of 487 professionals, consisting of 75 physicians, 75 nurses, 150 nursing assistants and 187 community health agents (CHA), were interviewed using two semi-structured questionnaires, one created for the physicians and another for the nurses, nursing assistants and health agents. A considerable percentage of professionals from all categories demonstrated doubts about treatment, mechanisms of transmission, recognition of the triatomines and the sending of official notification of the presence of insects, tests for diagnosis confirmation, etiologic treatment, and the prognosis of the disease. Doubts arose more frequently among the CHA, who are the main link between patients and basic health units. In order to maintain the current state of disease control and provide appropriate treatment for those already infected by Trypanosoma cruzi, it is necessary to invest in epidemiological surveillance, education and to have duly capable and qualified health professionals
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