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

Anis Azwani Muhd Suberi, Wan Nurshazwani Wan Zakaria, Razali Tomari, Nurmiza Othman, Nik Farhan Nik Fuad


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.


Dendritic cells; cancer immunotherapy; image processing; pattern recognition; phase contrast microscopy

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Published by INSIGHT - Indonesian Society for Knowledge and Human Development