A novel color image segmentation method and its application to white blood cell image analysis

2006 ◽  
Author(s):  
Jianhua Wu ◽  
Pingping Zeng ◽  
Yuan Zhou ◽  
Christian Olivier
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Blood Cell ◽  
White Blood Cell ◽  
Color Image ◽  
Color Image Segmentation ◽  
Segmentation Method ◽  
Cell Image ◽  
Cell Image Analysis

Color Image Segmentation Method Based on Parameter-Controlled MSPCNN and SLIC

2021 ◽  
Vol 58 (2) ◽  
pp. 0210023
Author(s):  
李新颖 Li Xinying ◽  
冉思园 Ran Siyuan ◽  
廉敬 Lian Jing
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Color Image Segmentation ◽  
Segmentation Method

Algorithm for Petro-Graphic Color Image Segmentation Used in Oil Exploration

2014 ◽  
pp. 187-195 ◽  
Keyword(s):  
Image Segmentation ◽  
Thin Section ◽  
Heuristic Algorithm ◽  
Color Image ◽  
Color Image Segmentation ◽  
Oil Exploration ◽  
Segmentation Method ◽  
Graphic Images ◽  
Oil Gas ◽  
Automatic Algorithm

A new heuristic algorithm for porosity segmentation for the colored petro-graphic images is proposed. The proposed algorithm automatically detects the porosities that represent the presence of oil, gas, or even water in the analyzed thin section rock segment based on the colour of the porosity area filled with dies in the analyzed sample. For the purpose of the oil exploration, the thin section fragments are died in order to emphasize the porosities that are analyzed under the microscope. The percentage of the porosity is directly proportional to the probability of the oil, gas, or even water presence in the area where the drilling is performed (i.e. the increased porosity indicates the higher probability of oil existence in the region). The proposed automatic algorithm shows better results than the existing K-means segmentation method.

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.

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