DOI : https://doi.org/10.18517/ijaseit.7.4.3136

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

Anis Azwani Muhd Suberi (1), Wan Nurshazwani Wan Zakaria (2), Razali Tomari (3), Nurmiza Othman (4), Nik Farhan Nik Fuad (5)
(1) Advance Mechatronic Research Group (ADMIRE), Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Malaysia
(2) Advance Mechatronic Research Group (ADMIRE), Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Malaysia
(3) Advance Mechatronic Research Group (ADMIRE), Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Malaysia
(4) Advance Mechatronic Research Group (ADMIRE), Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Malaysia
(5) UKM Medical Centre, Jalan Yaacob Latif, Bandar Tun Razak, 56000 Cheras, Kuala Lumpur, Malaysia
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How to cite (IJASEIT) :
Muhd Suberi, Anis Azwani, et al. “Dendritic Cells Feature Extraction Using Geometric Features and 1D Fourier Descriptors”. International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 4, Aug. 2017, pp. 1334-9, doi:10.18517/ijaseit.7.4.3136.
Citation Format :
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.

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