Paper
11 July 2016 Dendritic cell recognition using template matching based on one-dimensional (1D) Fourier descriptors (FD)
Anis Azwani Muhd Suberi, Wan Nurshazwani Wan Zakaria, Razali Tomari, Mei Xia Lau
Author Affiliations +
Proceedings Volume 10011, First International Workshop on Pattern Recognition; 100110K (2016) https://doi.org/10.1117/12.2242814
Event: First International Workshop on Pattern Recognition, 2016, Tokyo, Japan
Abstract
Identification of Dendritic Cell (DC) particularly in the cancer microenvironment is a unique disclosure since fighting tumor from the harnessing immune system has been a novel treatment under investigation. Nowadays, the staining procedure in sorting DC can affect their viability. In this paper, a computer aided system is proposed for automatic classification of DC in peripheral blood mononuclear cell (PBMC) images. Initially, the images undergo a few steps in preprocessing to remove uneven illumination and artifacts around the cells. In segmentation, morphological operators and Canny edge are implemented to isolate the cell shapes and extract the contours. Following that, information from the contours are extracted based on Fourier descriptors, derived from one dimensional (1D) shape signatures. Eventually, cells are classified as DC by comparing template matching (TM) of established template and target images. The results show that the proposed scheme is reliable and effective to recognize DC.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Anis Azwani Muhd Suberi, Wan Nurshazwani Wan Zakaria, Razali Tomari, and Mei Xia Lau "Dendritic cell recognition using template matching based on one-dimensional (1D) Fourier descriptors (FD)", Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100110K (11 July 2016); https://doi.org/10.1117/12.2242814
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Cited by 3 scholarly publications.
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KEYWORDS
Blood

Image segmentation

Image processing

Image classification

Feature extraction

Algorithm development

Cancer

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