Computer Aided Diagnosis for Anti-Nuclear Antibodies HEp-2 images: Progress and challenges

Hobson, Peter; Lovell, Brian C.; Percannella, Gennaro; Saggese, Alessia; Vento, Mario; Wiliem, Arnold

doi:10.1016/j.patrec.2016.06.013

Cited by 40 publications

(26 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In past years, the scientific community has shown a great interest in the analysis of IIF images, and in particular for the development of CAD systems. Various aspects of the entire analysis flow have been addressed by different research groups [8]. As a consequence of this, in the literature, it is possible to find work concerning the image acquisition [9], image preprocessing [10], and cell segmentation [11,12].…”

Section: Related Workmentioning

confidence: 99%

An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification

2019

View full text Add to dashboard Cite

The antinuclear antibody (ANA) test is widely used for screening, diagnosing, and monitoring of autoimmune diseases. The most common methods to determine ANA are indirect immunofluorescence (IIF), performed by human epithelial type 2 (HEp-2) cells, as substrate antigen. The evaluation of ANA consist an analysis of fluorescence intensity and staining patterns. This paper presents a complete and fully automatic system able to characterize IIF images. The fluorescence intensity classification was obtained by performing an image preprocessing phase and implementing a Support Vector Machines (SVM) classifier. The cells identification problem has been addressed by developing a flexible segmentation methods, based on the Hough transform for ellipses, and on an active contours model. In order to classify the HEp-2 cells, six SVM and one k-nearest neighbors (KNN)classifiers were developed. The system was tested on a public database consisting of 2080 IIF images. Unlike almost all work presented on this topic, the proposed system automatically addresses all phases of the HEp-2 image analysis process. All results have been evaluated by comparing them with some of the most representative state-of-the-art work, demonstrating the goodness of the system in the characterization of HEp-2 images.

show abstract

Section: Related Workmentioning

confidence: 99%

An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification

2019

View full text Add to dashboard Cite

show abstract

“…The reader can refer to [36] for an overview of the literature and of the open challenges in this research area. Below, we discuss some specific works in terms of features and classifiers.…”

Section: Related Workmentioning

confidence: 99%

Feature Importance for Human Epithelial (HEp-2) Cell Image Classification

Gupta

Bhavsar

2018

J. Imaging

View full text Add to dashboard Cite

Indirect Immuno-Fluorescence (IIF) microscopy imaging of human epithelial (HEp-2) cells is a popular method for diagnosing autoimmune diseases. Considering large data volumes, computer-aided diagnosis (CAD) systems, based on image-based classification, can help in terms of time, effort, and reliability of diagnosis. Such approaches are based on extracting some representative features from the images. This work explores the selection of the most distinctive features for HEp-2 cell images using various feature selection (FS) methods. Considering that there is no single universally optimal feature selection technique, we also propose hybridization of one class of FS methods (filter methods). Furthermore, the notion of variable importance for ranking features, provided by another type of approaches (embedded methods such as Random forest, Random uniform forest) is exploited to select a good subset of features from a large set, such that addition of new features does not increase classification accuracy. In this work, we have also, with great consideration, designed class-specific features to capture morphological visual traits of the cell patterns. We perform various experiments and discussions to demonstrate the effectiveness of FS methods along with proposed and a standard feature set. We achieve state-of-the-art performance even with small number of features, obtained after the feature selection.

show abstract

“…IIF is a test having high sensitivity, but only analytical and not diagnostic specificity, since the positivity for ANA does not automatically confirm the presence of autoimmune disease; indeed, the ANA may be present even in healthy subjects [1,2]. In the past years, a great deal of effort was put into research regarding Indirect Immunofluorescence techniques, with the aim of development of computer-assisted diagnosis (CAD) systems [3].…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network for HEp-2 Fluorescence Intensity Classification

2019

View full text Add to dashboard Cite

Indirect ImmunoFluorescence (IIF) assays are recommended as the gold standard method for detection of antinuclear antibodies (ANAs), which are of considerable importance in the diagnosis of autoimmune diseases. Fluorescence intensity analysis is very often complex, and depending on the capabilities of the operator, the association with incorrect classes is statistically easy. In this paper, we present a Convolutional Neural Network (CNN) system to classify positive/negative fluorescence intensity of HEp-2 IIF images, which is important for autoimmune diseases diagnosis. The method uses the best known pre-trained CNNs to extract features and a support vector machine (SVM) classifier for the final association to the positive or negative classes. This system has been developed and the classifier was trained on a database implemented by the AIDA (AutoImmunité, Diagnostic Assisté par ordinateur) project. The method proposed here has been tested on a public part of the same database, consisting of 2080 IIF images. The performance analysis showed an accuracy of fluorescent intensity around 93%. The results have been evaluated by comparing them with some of the most representative state-of-the-art works, demonstrating the quality of the system in the intensity classification of HEp-2 images.

show abstract

Computer Aided Diagnosis for Anti-Nuclear Antibodies HEp-2 images: Progress and challenges

Abstract: To create your highlights, please type the highlights against each \item command. It should be short collection of bullet points that convey the core findings of the article. It should include 3 to 5 bullet points (maximum 85 characters, including spaces, per bullet point.

Cited by 40 publications

References 42 publications

An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification

An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification

Feature Importance for Human Epithelial (HEp-2) Cell Image Classification

Deep Convolutional Neural Network for HEp-2 Fluorescence Intensity Classification

Contact Info

Product

Resources

About