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Your search keyword '"HEp-2 cell segmentation"' showing total 4 results
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4 results on '"HEp-2 cell segmentation"'

1. An automated approach to the segmentation of HEp-2 cells for the indirect immunofluorescence ANA test.

Author

Tonti, Simone, Di Cataldo, Santa, Bottino, Andrea, and Ficarra, Elisa

Subjects
*AUTOIMMUNE disease diagnosis, *IMMUNOFLUORESCENCE, *IMAGE segmentation, *IMAGE analysis, *BIOMARKERS, *MEDICAL imaging systems
Abstract

The automatization of the analysis of Indirect Immunofluorescence (IIF) images is of paramount importance for the diagnosis of autoimmune diseases. This paper proposes a solution to one of the most challenging steps of this process, the segmentation of HEp-2 cells, through an adaptive marker-controlled watershed approach. Our algorithm automatically conforms the marker selection pipeline to the peculiar characteristics of the input image, hence it is able to cope with different fluorescent intensities and staining patterns without any a priori knowledge. Furthermore, it shows a reduced sensitivity to over-segmentation errors and uneven illumination, that are typical issues of IIF imaging. [ABSTRACT FROM AUTHOR]

Published
2015
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2. Automatic segmentation of HEp-2 cell Fluorescence microscope images using level set method via geometric active contours

Author

Jasmine Banks, Khamael Al-Dulaimi, Vinod Chandran, and Inmaculada Tomeo-Reyes

Subjects
Level set (data structures), Level set method, business.industry, Rand index, Scale-space segmentation, Boundary (topology), Pattern recognition, 02 engineering and technology, Image segmentation, HEp-2 cell segmentation, 03 medical and health sciences, 0302 clinical medicine, 090609 Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Segmentation, Computer vision, Artificial intelligence, Geometric active contours, Variation of information, level set method, business, 030217 neurology & neurosurgery, Mathematics
Abstract

A method for segmenting HEp-2 cells in Indirect Immuno Fluorescence microscope(IIF) images is implemented and evaluated. Challenges to accurate segmentation include the complexity of the data acquired at multiple wavelengths, overlapping cells, and variations in staining. Level set methods via geometric active contours are used to solve this problem. Level set methods use morphological operations to estimate an initial cell boundary and are fully automated. Geometric active contours are able to adapt to the curve topology of the cell boundary. Segmentation performance is evaluated using six indices: boundary displacement error, global consistency error, variation of information, Jaccard distance error, rand index and F-index .

Published
2016

3. Automatic segmentation of HEp-2 cell Fluorescence microscope images using level set method via geometric active contours

Author

Davis, L, Lovell, B C, Del Bimbo, A, Al-Dulaimi, Khamael Abbas Khudhair, Banks, Jasmine, Tomeo-Reyes, Inma, Chandran, Vinod, Davis, L, Lovell, B C, Del Bimbo, A, Al-Dulaimi, Khamael Abbas Khudhair, Banks, Jasmine, Tomeo-Reyes, Inma, and Chandran, Vinod

Abstract

A method for segmenting HEp-2 cells in Indirect Immuno Fluorescence microscope(IIF) images is implemented and evaluated. Challenges to accurate segmentation include the complexity of the data acquired at multiple wavelengths, overlapping cells, and variations in staining. Level set methods via geometric active contours are used to solve this problem. Level set methods use morphological operations to estimate an initial cell boundary and are fully automated. Geometric active contours are able to adapt to the curve topology of the cell boundary. Segmentation performance is evaluated using six indices: boundary displacement error, global consistency error, variation of information, Jaccard distance error, rand index and F-index .

Published
2016

4. An automated approach to the segmentation of HEp-2 cells for the indirect immunofluorescence ANA test

Author

Santa Di Cataldo, Elisa Ficarra, Andrea Bottino, and Simone Tonti

Subjects
Computer science, Pipeline (computing), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Fluorescent Antibody Technique, HEp-2 cell segmentation, Cell pattern analysis, Indirect immunofluorescence, ANA testing, Microscope image processing, Health Informatics, Sensitivity and Specificity, Cell Line, Pattern Recognition, Automated, Image Interpretation, Computer-Assisted, Humans, Radiology, Nuclear Medicine and imaging, Segmentation, Computer vision, Sensitivity (control systems), Connective Tissue Diseases, Radiological and Ultrasound Technology, business.industry, Reproducibility of Results, Epithelial Cells, IIf, Image Enhancement, Computer Graphics and Computer-Aided Design, Microscopy, Fluorescence, Cell Tracking, Antibodies, Antinuclear, A priori and a posteriori, Computer Vision and Pattern Recognition, Artificial intelligence, business, Marker selection, Algorithms
Abstract

The automatization of the analysis of Indirect Immunofluorescence (IIF) images is of paramount importance for the diagnosis of autoimmune diseases. This paper proposes a solution to one of the most challenging steps of this process, the segmentation of HEp-2 cells, through an adaptive marker-controlled watershed approach. Our algorithm automatically conforms the marker selection pipeline to the peculiar characteristics of the input image, hence it is able to cope with different fluorescent intensities and staining patterns without any a priori knowledge. Furthermore, it shows a reduced sensitivity to over-segmentation errors and uneven illumination, that are typical issues of IIF imaging.

Published
2015

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