Computer aided system for dendritic cells detection and counting
lmmunotherapy is an entirely advanced class of cancer treatment which has been highly active and exciting field in clinical therapeutics. In numerous procedures, cancer immunotherapy demands a laborious practice to recognise and count Dendritic Cells (DCs) in the harnessing of immune system. Convent...
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2017
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| Online Access: | http://eprints.uthm.edu.my/7830/2/24p%20ANIS%20AZWANI%20MUHD%20SUBERI.pdf http://eprints.uthm.edu.my/7830/1/ANIS%20AZWANI%20MUHD%20SUBERI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/7830/3/ANIS%20AZWANI%20MUHD%20SUBERI%20WATERMARK.pdf |
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| Summary: | lmmunotherapy is an entirely advanced class of cancer treatment which has been highly active and exciting field in clinical therapeutics. In numerous procedures, cancer immunotherapy demands a laborious practice to recognise and count Dendritic Cells (DCs) in the harnessing of immune system. Conventionally, the laser-based technology that provides a rapid analysis such as Flow Cytometry can affect the DCs viability as the staining procedure is involved. Another highly promising method which is Phase Contrast Microscopy (PCM) involves experienced pathologists to visually examine the respective microscopy images. In fact, PCM confronts complex issues regarding imaging artifacts which can deteriorate the recognition process. As DCs counting are crucial in most cancer treatment procedures, this research proposes a pioneering system called CasDC (Computer Aided System for Dendritic Cells identification) which implements an image processing algorithm to recognise and count DCs with a label-free method. Initially, the images undergo Grayscale Normalization, H-GLAT, and Halo Removal to remove the imaging artifacts. In segmentation, morphological operators and Canny edge detector are implemented to extract the cell contours. Following that, information from the contours are characterized through the use of One-Dimensional (ID) Fourier Descriptors (FDs) and classified using Template Matching (TM). The aim of developing this system is to establish a reliable and time saving-tool as a second reader in the clinical practice. The proposed system has an enormous potential towards helping Cancer Research Institute in improving the diagnosis of cancer. Through the experiments conducted on dataset provided by the Cancer Research Institute, performance measures of 83.8%, 94.2%, 99 .5% and 88. 7% have been recorded for precision, recall, accuracy and FI-score respectively . |
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