Hybrid framework based on evidence theory for blood cell image segmentation

2014 ◽  
Author(s):  
Ismahan Baghli ◽  
Amir Nakib ◽  
Elie Sellam ◽  
Mourtada Benazzouz ◽  
Amine Chikh ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Blood Cell ◽  
Evidence Theory ◽  
Cell Image ◽  
Hybrid Framework

Complex local phase based subjective surfaces (CLAPSS) and its application to DIC red blood cell image segmentation

2013 ◽  
Vol 99 ◽  
pp. 98-110 ◽  
Author(s):  
Taoyi Chen ◽  
Yong Zhang ◽  
Changhong Wang ◽  
Zhenshen Qu ◽  
Fei Wang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Blood Cell ◽  
Red Blood Cell ◽  
Local Phase ◽  
Cell Image ◽  
Subjective Surfaces

Interest-Based Ordering for Fuzzy Morphology on White Blood Cell Image Segmentation

2012 ◽  
Vol 16 (1) ◽  
pp. 76-86 ◽  
Author(s):  
Chastine Fatichah ◽  
◽  
Martin Leonard Tangel ◽  
Muhammad Rahmat Widyanto ◽  
Fangyan Dong ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Blood Cell ◽  
White Blood Cell ◽  
Cancer Diagnosis ◽  
Fuzzy Clustering ◽  
High Accuracy ◽  
Low Density ◽  
Fuzzy Morphology ◽  
Cell Image ◽  
Improve Accuracy

An Interest-based Ordering Scheme (IOS) for fuzzy morphology on White-Blood-Cell (WBC) image segmentation is proposed to improve accuracy of segmentation. The proposed method shows a high accuracy in segmenting both high- and low-density nuclei. Further, its running time is low, so it can be used for real applications. To evaluate the performance of the proposed method, 100 WBC images and 10 leukemia images are used, and the experimental results show that the proposed IOS segments a nucleus in WBC images 3.99% more accurately on average than the Lexicographical Ordering Scheme (LOS) does and 5.29% more accurately on average than the combined Fuzzy Clustering and Binary Morphology (FCBM) method does. The proposal method segments a cytoplasm 20.72% more accurately on average than the FCBM method. The WBC image segmentation is a part of WBC classification in an automatic cancer-diagnosis application that is being developed. In addition, the proposed method can be used to segment any images that focus on the important color of an object of interest.

scholarly journals Adaptive Hybrid Blood Cell Image Segmentation

2019 ◽  
Vol 255 ◽  
pp. 01001
Author(s):  
T. Muda T Zalizam ◽  
Abdul Salam Rosalina ◽  
Ismail Suzilah
Keyword(s):  
Image Segmentation ◽  
Blood Cell ◽  
Mean Shift ◽  
Recognition System ◽  
Medical Diagnostics ◽  
Cell Analysis ◽  
Hybrid Analysis ◽  
Crucial Step ◽  
Cell Image ◽  
Segmentation Algorithms

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.

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