NDLI: Automated approach for indirect immunofluorescence images classification based on unsupervised clustering method

Content Provider	IET Digital Library
Author	Vivona, Letizia Cascio, Donato Taormina, Vincenzo Raso, Giuseppe
Abstract	Autoimmune diseases (ADs) are a collection of many complex disorders of unknown aetiology resulting in immune responses to self-antigens and are thought to result from interactions between genetic and environmental factors. ADs collectively are amongst the most prevalent diseases in the U.S., affecting at least 7% of the population. The diagnosis of ADs is very complex, the standard screening methods provides seeking and recognizing of Antinuclear Antibodies (ANA) by Indirect ImmunoFluorescence (IIF) based on HEp-2 cells. In this paper an automatic system able to identify and classify the Centromere pattern is presented. The method is based on the grouping of centromeres present on the cells through a clustering K-means algorithm. The performances were obtained on two public database of IIF images (A.I.D.A. and MIVIA). Our results showed a sensitivity for image of (90 ± 5)% and a Accuracy equal to (98.0 ± 0.5)%. Results demonstrate that the system is able to identify and classify Centromere pattern with accuracy better or comparable with some representative state of the art works. Moreover, it should be noted that for the classification phase the works used for the comparison used an expert-manual segmentation while, in the present work, the segmentation was obtained automatically.
Starting Page	989
Ending Page	995
Page Count	7
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 7, Oct (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5271
Journal	IET Computer Vision
Publisher Date	2018-08-06
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	ANAs Antinuclear Antibodies Autoimmune Diseases Automated Approach Automatic System Biology And Medical Computing Biomedical Imaging/measurement Biomedical Optical Imaging Blood Cellular Biophysics Centromere Pattern Centromere Pattern Classification Classification Phase Clustering K-means Algorithm Complex Disorders Computer Vision And Image Processing Technique Databases Diseases Environmental Factors Feature Extraction Fluorescence Fluorescence Intensity Genetic Factors HEp-2 Cell IIF Image Image Classification Image Recognition Image Segmentation Image Texture Immune Responses Indirect Immunofluorescence Image Classification Knowledge Engineering Technique Medical Disorders Medical Image Processing MIVIA Optical And Laser Radiation Patient Diagnostic Method And Instrumentation Prevalent Diseases Public Database Representative State-of-the-art Works Self-antigens Staining Pattern Standard Screening Method State-of-the-art Technique Unknown Aetiology Unsupervised Clustering Method
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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