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Dendritic Cells Feature Extraction using Geometric Features and 1D Fourier Descriptors

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@article{IJASEIT3136,
   author = {Anis Azwani Muhd Suberi and Wan Nurshazwani Wan Zakaria and Razali Tomari and Nurmiza Othman and Nik Farhan Nik Fuad},
   title = {Dendritic Cells Feature Extraction using Geometric Features and 1D Fourier Descriptors},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {4},
   year = {2017},
   pages = {1334--1339},
   keywords = {Dendritic cells; cancer immunotherapy; image processing; pattern recognition; phase contrast microscopy},
   abstract = {The current day technology such as Flow Cytometry is only able to classify Dendritic Cells (DCs) once they are stained. Subsequently this procedure affects the cell viability for vaccine preparation in DCs immunotherapy. Visually, the DCs classification can be distinguished through their unique morphological feature called tentacles compared to other immune cells, which have more rounded shape. Therefore, this paper proposes two pattern matching approaches based on Geometric and 1D Fourier Descriptors (FDs) to classify DCs from Phase Contrast Microscopy (PCM) image containing a mix of T-cells and debris. The performance of the developed algorithm is analysed and compared with the manual counting provided by the pathologist. The results show that the implementation of 1D FDs with Template Matching (TM) classifier have the better performance and achieve the best overall recognition accuracy of 98.3% compared to Geometric features and DCCIS system.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=3136},
   doi = {10.18517/ijaseit.7.4.3136}
}

EndNote

%A Muhd Suberi, Anis Azwani
%A Wan Zakaria, Wan Nurshazwani
%A Tomari, Razali
%A Othman, Nurmiza
%A Nik Fuad, Nik Farhan
%D 2017
%T Dendritic Cells Feature Extraction using Geometric Features and 1D Fourier Descriptors
%B 2017
%9 Dendritic cells; cancer immunotherapy; image processing; pattern recognition; phase contrast microscopy
%! Dendritic Cells Feature Extraction using Geometric Features and 1D Fourier Descriptors
%K Dendritic cells; cancer immunotherapy; image processing; pattern recognition; phase contrast microscopy
%X The current day technology such as Flow Cytometry is only able to classify Dendritic Cells (DCs) once they are stained. Subsequently this procedure affects the cell viability for vaccine preparation in DCs immunotherapy. Visually, the DCs classification can be distinguished through their unique morphological feature called tentacles compared to other immune cells, which have more rounded shape. Therefore, this paper proposes two pattern matching approaches based on Geometric and 1D Fourier Descriptors (FDs) to classify DCs from Phase Contrast Microscopy (PCM) image containing a mix of T-cells and debris. The performance of the developed algorithm is analysed and compared with the manual counting provided by the pathologist. The results show that the implementation of 1D FDs with Template Matching (TM) classifier have the better performance and achieve the best overall recognition accuracy of 98.3% compared to Geometric features and DCCIS system.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=3136
%R doi:10.18517/ijaseit.7.4.3136
%J International Journal on Advanced Science, Engineering and Information Technology
%V 7
%N 4
%@ 2088-5334

IEEE

Anis Azwani Muhd Suberi,Wan Nurshazwani Wan Zakaria,Razali Tomari,Nurmiza Othman and Nik Farhan Nik Fuad,"Dendritic Cells Feature Extraction using Geometric Features and 1D Fourier Descriptors," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 4, pp. 1334-1339, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.4.3136.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Muhd Suberi, Anis Azwani
AU  - Wan Zakaria, Wan Nurshazwani
AU  - Tomari, Razali
AU  - Othman, Nurmiza
AU  - Nik Fuad, Nik Farhan
PY  - 2017
TI  - Dendritic Cells Feature Extraction using Geometric Features and 1D Fourier Descriptors
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 4
Y2  - 2017
SP  - 1334
EP  - 1339
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Dendritic cells; cancer immunotherapy; image processing; pattern recognition; phase contrast microscopy
N2  - The current day technology such as Flow Cytometry is only able to classify Dendritic Cells (DCs) once they are stained. Subsequently this procedure affects the cell viability for vaccine preparation in DCs immunotherapy. Visually, the DCs classification can be distinguished through their unique morphological feature called tentacles compared to other immune cells, which have more rounded shape. Therefore, this paper proposes two pattern matching approaches based on Geometric and 1D Fourier Descriptors (FDs) to classify DCs from Phase Contrast Microscopy (PCM) image containing a mix of T-cells and debris. The performance of the developed algorithm is analysed and compared with the manual counting provided by the pathologist. The results show that the implementation of 1D FDs with Template Matching (TM) classifier have the better performance and achieve the best overall recognition accuracy of 98.3% compared to Geometric features and DCCIS system.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=3136
DO  - 10.18517/ijaseit.7.4.3136

RefWorks

RT Journal Article
ID 3136
A1 Muhd Suberi, Anis Azwani
A1 Wan Zakaria, Wan Nurshazwani
A1 Tomari, Razali
A1 Othman, Nurmiza
A1 Nik Fuad, Nik Farhan
T1 Dendritic Cells Feature Extraction using Geometric Features and 1D Fourier Descriptors
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 4
YR 2017
SP 1334
OP 1339
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Dendritic cells; cancer immunotherapy; image processing; pattern recognition; phase contrast microscopy
AB The current day technology such as Flow Cytometry is only able to classify Dendritic Cells (DCs) once they are stained. Subsequently this procedure affects the cell viability for vaccine preparation in DCs immunotherapy. Visually, the DCs classification can be distinguished through their unique morphological feature called tentacles compared to other immune cells, which have more rounded shape. Therefore, this paper proposes two pattern matching approaches based on Geometric and 1D Fourier Descriptors (FDs) to classify DCs from Phase Contrast Microscopy (PCM) image containing a mix of T-cells and debris. The performance of the developed algorithm is analysed and compared with the manual counting provided by the pathologist. The results show that the implementation of 1D FDs with Template Matching (TM) classifier have the better performance and achieve the best overall recognition accuracy of 98.3% compared to Geometric features and DCCIS system.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=3136
DO  - 10.18517/ijaseit.7.4.3136