Automated classification for HEp-2 cells based on linear local distance coding framework
The occurrence of antinuclear antibodies (ANAs) in patient serum has significant relation to some specific autoimmune diseases. Indirect immunofluorescence (IIF) on human epithelial type 2 (HEp-2) cells is the recommended methodology for detecting ANAs in clinic practice. However, the currently prac...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2015
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| Online Access: | https://hdl.handle.net/10356/105269 http://hdl.handle.net/10220/25964 |
| _version_ | 1811683026500845568 |
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| author | Xu, Xiang Lin, Feng Ng, Carol Leong, Khai Pang |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Xu, Xiang Lin, Feng Ng, Carol Leong, Khai Pang |
| author_sort | Xu, Xiang |
| collection | NTU |
| description | The occurrence of antinuclear antibodies (ANAs) in patient serum has significant relation to some specific autoimmune diseases. Indirect immunofluorescence (IIF) on human epithelial type 2 (HEp-2) cells is the recommended methodology for detecting ANAs in clinic practice. However, the currently practiced manual detection system suffers from serious problems due to subjective evaluation. In this paper, we present an automated system for HEp-2 cells classification. We adopt a bag-of-words (BoW) framework which has shown impressive performance in image classification tasks because it can obtain discriminative and effective image representation. However, the information loss is inevitable in the coding process. Therefore, we propose a linear local distance coding (LLDC) method to capture more discriminative information. Our LLDC method transforms original local feature to more discriminative local distance vector by searching for local nearest few neighbors of the local feature in the class-specific manifolds. The obtained local distance vector is further encoded and pooled together to get salient image representation. The LLDC method is combined with the traditional coding methods to achieve higher classification accuracy. Incorporated with a linear support vector machine classifier, our proposed method demonstrated its effectiveness on two public datasets, namely, the International Conference on Pattern Recognition (ICPR) 2012 dataset and the International Conference on Image Processing (ICIP) 2013 training dataset. Experimental results show that the LLDC framework can achieve superior performance to the state-of-the-art coding methods for staining pattern classification of HEp-2 cells. |
| first_indexed | 2024-10-01T04:06:11Z |
| format | Journal Article |
| id | ntu-10356/105269 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:06:11Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | ntu-10356/1052692020-05-28T07:17:18Z Automated classification for HEp-2 cells based on linear local distance coding framework Xu, Xiang Lin, Feng Ng, Carol Leong, Khai Pang School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision The occurrence of antinuclear antibodies (ANAs) in patient serum has significant relation to some specific autoimmune diseases. Indirect immunofluorescence (IIF) on human epithelial type 2 (HEp-2) cells is the recommended methodology for detecting ANAs in clinic practice. However, the currently practiced manual detection system suffers from serious problems due to subjective evaluation. In this paper, we present an automated system for HEp-2 cells classification. We adopt a bag-of-words (BoW) framework which has shown impressive performance in image classification tasks because it can obtain discriminative and effective image representation. However, the information loss is inevitable in the coding process. Therefore, we propose a linear local distance coding (LLDC) method to capture more discriminative information. Our LLDC method transforms original local feature to more discriminative local distance vector by searching for local nearest few neighbors of the local feature in the class-specific manifolds. The obtained local distance vector is further encoded and pooled together to get salient image representation. The LLDC method is combined with the traditional coding methods to achieve higher classification accuracy. Incorporated with a linear support vector machine classifier, our proposed method demonstrated its effectiveness on two public datasets, namely, the International Conference on Pattern Recognition (ICPR) 2012 dataset and the International Conference on Image Processing (ICIP) 2013 training dataset. Experimental results show that the LLDC framework can achieve superior performance to the state-of-the-art coding methods for staining pattern classification of HEp-2 cells. Published version 2015-06-18T04:58:29Z 2019-12-06T21:48:27Z 2015-06-18T04:58:29Z 2019-12-06T21:48:27Z 2015 2015 Journal Article Xu, X., Lin, F., Ng, C., & Leong, K. P. (2015). Automated classification for HEp-2 cells based on linear local distance coding framework. EURASIP journal on image and video processing, 2015(13). 1687-5281 https://hdl.handle.net/10356/105269 http://hdl.handle.net/10220/25964 10.1186/s13640-015-0064-7 en EURASIP journal on image and video processing © 2015 Xu et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Xu, Xiang Lin, Feng Ng, Carol Leong, Khai Pang Automated classification for HEp-2 cells based on linear local distance coding framework |
| title | Automated classification for HEp-2 cells based on linear local distance coding framework |
| title_full | Automated classification for HEp-2 cells based on linear local distance coding framework |
| title_fullStr | Automated classification for HEp-2 cells based on linear local distance coding framework |
| title_full_unstemmed | Automated classification for HEp-2 cells based on linear local distance coding framework |
| title_short | Automated classification for HEp-2 cells based on linear local distance coding framework |
| title_sort | automated classification for hep 2 cells based on linear local distance coding framework |
| topic | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision |
| url | https://hdl.handle.net/10356/105269 http://hdl.handle.net/10220/25964 |
| work_keys_str_mv | AT xuxiang automatedclassificationforhep2cellsbasedonlinearlocaldistancecodingframework AT linfeng automatedclassificationforhep2cellsbasedonlinearlocaldistancecodingframework AT ngcarol automatedclassificationforhep2cellsbasedonlinearlocaldistancecodingframework AT leongkhaipang automatedclassificationforhep2cellsbasedonlinearlocaldistancecodingframework |