Automated Classification System for HEp-2 Cell Patterns
Human Epithelial Type-2 (HEp-2) cells are essential in diagnosing autoimmune diseases. Indirect immunofluorescence (IIF) imaging is a fundamental technique for detecting antinuclear antibodies in HEp-2 cells. The four main patterns of HEp-2 cells that are being identified are nucleolar, homogeneous,...
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my-utp-utpedia.165182017-01-25T09:35:05Z http://utpedia.utp.edu.my/16518/ Automated Classification System for HEp-2 Cell Patterns Nor Shaharim, Nur Ashiqin TK Electrical engineering. Electronics Nuclear engineering Human Epithelial Type-2 (HEp-2) cells are essential in diagnosing autoimmune diseases. Indirect immunofluorescence (IIF) imaging is a fundamental technique for detecting antinuclear antibodies in HEp-2 cells. The four main patterns of HEp-2 cells that are being identified are nucleolar, homogeneous, speckled and centromere. The most commonly used method to classify the patterns is manual evaluation. This method is prone to human error. This paper will propose an automated method of classifying HEp-2 cells patterns. The first stage is image enhancement using Histogram equalization contrast adjustment and Wiener Filter. The second stage uses Sobel Filter and Mean Filter for segmentation. The third stage feature extraction based on shape properties data extraction. The last stage uses classification based on different properties data abstracted. The results obtained are more than 90% for nucleolar and centromere and about 70% for homogenous and speckled. For future work, another feature extraction method need to be introduced to increase the accuracy of the classification result. The method suggested is to analyze and obtain the data based on the texture of the image. IRC 2015-09 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/16518/1/04_Dissertation.pdf Nor Shaharim, Nur Ashiqin (2015) Automated Classification System for HEp-2 Cell Patterns. IRC, Universiti Teknologi PETRONAS. (Unpublished) |
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TK Electrical engineering. Electronics Nuclear engineering Nor Shaharim, Nur Ashiqin Automated Classification System for HEp-2 Cell Patterns |
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Human Epithelial Type-2 (HEp-2) cells are essential in diagnosing autoimmune diseases. Indirect immunofluorescence (IIF) imaging is a fundamental technique for detecting antinuclear antibodies in HEp-2 cells. The four main patterns of HEp-2 cells that are being identified are nucleolar, homogeneous, speckled and centromere. The most commonly used method to classify the patterns is manual evaluation. This method is prone to human error. This paper will propose an automated method of classifying HEp-2 cells patterns. The first stage is image enhancement using Histogram equalization contrast adjustment and Wiener Filter. The second stage uses Sobel Filter and Mean Filter for segmentation. The third stage feature extraction based on shape properties data extraction. The last stage uses classification based on different properties data abstracted. The results obtained are more than 90% for nucleolar and centromere and about 70% for homogenous and speckled. For future work, another feature extraction method need to be introduced to increase the accuracy of the classification result. The method suggested is to analyze and obtain the data based on the texture of the image. |
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Final Year Project |
author |
Nor Shaharim, Nur Ashiqin |
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Nor Shaharim, Nur Ashiqin |
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Nor Shaharim, Nur Ashiqin |
title |
Automated Classification System for HEp-2 Cell Patterns |
title_short |
Automated Classification System for HEp-2 Cell Patterns |
title_full |
Automated Classification System for HEp-2 Cell Patterns |
title_fullStr |
Automated Classification System for HEp-2 Cell Patterns |
title_full_unstemmed |
Automated Classification System for HEp-2 Cell Patterns |
title_sort |
automated classification system for hep-2 cell patterns |
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IRC |
publishDate |
2015 |
url |
http://utpedia.utp.edu.my/16518/1/04_Dissertation.pdf http://utpedia.utp.edu.my/16518/ |
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1739832270062092288 |
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13.18916 |