Adaptive Hybrid Blood Cell Image Segmentation

• Main Authors: Tuan Muda, Tuan Zalizam, Abdul Salam, Rosalina, Ismail, Suzilah
• Format: Conference or Workshop Item
• Language: English
• Published: 2019
• Subjects: QA Mathematics
• Online Access: https://repo.uum.edu.my/id/eprint/30975/1/EAAI%20255%2001001%202018%2001-05.pdf; https://repo.uum.edu.my/id/eprint/30975/; https://www.matec-conferences.org/articles/matecconf/abs/2019/04/matecconf_eaaic2018_01001/matecconf_eaaic2018_01001.html

Image segmentation is an important phase in the image recognition system. In medical imaging such as blood cell analysis, it becomes a crucial step in quantitative cytophotometry. Currently, blood cell images become predominantly valuable in medical diagnostics tools. In this paper, we present an adaptive hybrid analysis based on selected segmentation algorithms. Three designates common approaches, that are Fuzzy c-means, K-means and Mean-shift are adapted. Blood cell images that are infected with malaria parasites at various stages were tested. The most suitable method will be selected based on the lowest number of regions. The selected approach will be enhanced by applying Median-cut algorithm to further expand the segmentation process. The proposed adaptive hybrid method has shown a significant improvement in the number of regions