The Combination of Local Fuzzy-Entropy-Based Transition Region Extraction with Otsu Thresholding Method for Image Segmentation

2009 ◽  
Author(s):  
Aiying Lin ◽  
Lili Wu ◽  
Baozhou Zheng ◽  
Hongying Zan
Keyword(s):  
Image Segmentation ◽  
Transition Region ◽  
Fuzzy Entropy ◽  
Thresholding Method ◽  
Otsu Thresholding ◽  
Region Extraction

The Combination of Local Fuzzy Entropy and Gray Level Difference-Based Thresholding Segmentation

2012 ◽  
Vol 220-223 ◽  
pp. 1288-1291
Author(s):  
Tong Tong ◽  
Yan Cai ◽  
Da Wei Sun ◽  
Wei Huang
Keyword(s):  
Transition Region ◽  
Extraction Methods ◽  
Fuzzy Entropy ◽  
Experimental Results ◽  
Segmentation Result ◽  
Gray Level ◽  
Thresholding Method ◽  
Level Difference ◽  
Region Extraction

A novel transition region extraction and thresholding method based on both frequency and degree of gray level changes is proposed by analyzing properties of transition region. Frequent gray level based transition region extraction methods are greatly affected by noise. To eliminate the algorithm limitation, a modified descriptor taking both degree and frequency of gray level changes into account is developed. The proposed algorithm can accurately extract transition region of an image and get ideal segmentation result. The experimental results show its superiority and feasibility.

scholarly journals Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 328 ◽  
Author(s):  
Husein S Naji Alwerfali ◽  
Mohammed A. A. Al-qaness ◽  
Mohamed Abd Elaziz ◽  
Ahmed A. Ewees ◽  
Diego Oliva ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Fitness Function ◽  
Fuzzy Entropy ◽  
Threshold Values ◽  
Traditional Methods ◽  
Thresholding Method ◽  
Segmentation Methods ◽  
Sine Cosine Algorithm ◽  
Multi Level

Multi-level thresholding is one of the effective segmentation methods that have been applied in many applications. Traditional methods face challenges in determining the suitable threshold values; therefore, metaheuristic (MH) methods have been adopted to solve these challenges. In general, MH methods had been proposed by simulating natural behaviors of swarm ecosystems, such as birds, animals, and others. The current study proposes an alternative multi-level thresholding method based on a new MH method, a modified spherical search optimizer (SSO). This was performed by using the operators of the sine cosine algorithm (SCA) to enhance the exploitation ability of the SSO. Moreover, Fuzzy entropy is applied as the main fitness function to evaluate the quality of each solution inside the population of the proposed SSOSCA since Fuzzy entropy has established its performance in literature. Several images from the well-known Berkeley dataset were used to test and evaluate the proposed method. The evaluation outcomes approved that SSOSCA showed better performance than several existing methods according to different image segmentation measures.

Sign in / Sign up

Export Citation Format

Share Document