scholarly journals Hybrid Artificial Root Foraging Optimizer Based Multilevel Threshold for Image Segmentation

2016 ◽  
Vol 2016 ◽  
pp. 1-16
Author(s):  
Yang Liu ◽  
Junfei Liu ◽  
Liwei Tian ◽  
Lianbo Ma
Keyword(s):  
Image Segmentation ◽  
Iterative Root ◽  
Continuous Space ◽  
Root Foraging ◽  
Computation Efficiency ◽  
Spatial Population ◽  
Complex Image ◽  
Exchange Information ◽  
Comparative Results ◽  
Segmentation Problem

This paper proposes a new plant-inspired optimization algorithm for multilevel threshold image segmentation, namely, hybrid artificial root foraging optimizer (HARFO), which essentially mimics the iterative root foraging behaviors. In this algorithm the new growth operators of branching, regrowing, and shrinkage are initially designed to optimize continuous space search by combining root-to-root communication and coevolution mechanism. With the auxin-regulated scheme, various root growth operators are guided systematically. With root-to-root communication, individuals exchange information in different efficient topologies, which essentially improve the exploration ability. With coevolution mechanism, the hierarchical spatial population driven by evolutionary pressure of multiple subpopulations is structured, which ensure that the diversity of root population is well maintained. The comparative results on a suit of benchmarks show the superiority of the proposed algorithm. Finally, the proposed HARFO algorithm is applied to handle the complex image segmentation problem based on multilevel threshold. Computational results of this approach on a set of tested images show the outperformance of the proposed algorithm in terms of optimization accuracy computation efficiency.

scholarly journals A Novel Plant Root Foraging Algorithm for Image Segmentation Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Lianbo Ma ◽  
Kunyuan Hu ◽  
Yunlong Zhu ◽  
Hanning Chen ◽  
Maowei He
Keyword(s):  
Image Segmentation ◽  
Foraging Behavior ◽  
Plant Root ◽  
Biologically Inspired ◽  
Foraging Patterns ◽  
Root Foraging ◽  
New Type ◽  
Optimization Methodology ◽  
Multimodal Problems ◽  
Segmentation Problem

This paper presents a new type of biologically-inspired global optimization methodology for image segmentation based on plant root foraging behavior, namely, artificial root foraging algorithm (ARFO). The essential motive of ARFO is to imitate the significant characteristics of plant root foraging behavior including branching, regrowing, and tropisms for constructing a heuristic algorithm for multidimensional and multimodal problems. A mathematical model is firstly designed to abstract various plant root foraging patterns. Then, the basic process of ARFO algorithm derived in the model is described in details. When tested against ten benchmark functions, ARFO shows the superiority to other state-of-the-art algorithms on several benchmark functions. Further, we employed the ARFO algorithm to deal with multilevel threshold image segmentation problem. Experimental results of the new algorithm on a variety of images demonstrated the suitability of the proposed method for solving such problem.

scholarly journals Modified JSEG algorithm for reducing over-segmentation problems in underwater coral reef images

2019 ◽  
Vol 9 (6) ◽  
pp. 5244
Author(s):  
Mohammad Sameer Aloun ◽  
Muhammad Suzuri Hitam ◽  
Wan NuralJawahir Hj Wan Yussof ◽  
Abdul Aziz K Abdul Hamid ◽  
Zainuddin Bachok
Keyword(s):  
Image Segmentation ◽  
Coral Reef ◽  
Texture Features ◽  
Color Histogram ◽  
Similarity Measurement ◽  
Segmentation Problem ◽  
Better Than ◽  
Observation Results ◽  
Color And Texture Features

<p>The original JSEG algorithm has proved to be very useful and robust in variety of image segmentation case studies.However, when it is applied into the underwater coral reef images, the original JSEG algorithm produces over-segementation problem, thus making this algorithm futile in such a situation. In this paper, an approach to reduce the over-segmentation problem occurred in the underwater coral reef image segmentation is presented. The approach works by replacing the color histogram computation in region merge stage of the original JSEG algorithm with the new computation of color and texture features in the similarity measurement. Based on the perceptual observation results of the test images, the proposed modified JSEG algorithm could automatically segment the regions better than the original JSEG algorithm.</p>

The Brain Tumor Segmentation Using Fuzzy C-Means Technique

2018 ◽  
pp. 2402-2419
Author(s):  
Jyotsna Rani ◽  
Ram Kumar ◽  
Fazal A. Talukdar ◽  
Nilanjan Dey
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Fuzzy C Means ◽  
Mri Brain ◽  
Area Of Interest ◽  
Fuzzy C Means Clustering ◽  
Comparative Results ◽  
The Brain

Image segmentation is a technique which divides an image into its constituent regions or objects. Segmentation continues till we reach our area of interest or the specified object of target. This field offers vast future scope and challenges for the researchers. This proposal uses the fuzzy c mean technique to segment the different MRI brain tumor images. This proposal also shows the comparative results of Thresholding, K-means clustering and Fuzzy c- means clustering. Dice coefficient and Jaccards measure is used for accuracy of the segmentation in this proposal. Experimental results demonstrate the performance of the designed method.

scholarly journals Artificial Bee Colony Optimizer with Bee-to-Bee Communication and Multipopulation Coevolution for Multilevel Threshold Image Segmentation

2015 ◽  
Vol 2015 ◽  
pp. 1-23 ◽  
Author(s):  
Jun-yi Li ◽  
Yi-ding Zhao ◽  
Jian-hua Li ◽  
Xiao-jun Liu
Keyword(s):  
Image Segmentation ◽  
Information Exchange ◽  
Artificial Bee Colony ◽  
Experimental Results ◽  
New Method ◽  
Communication Pattern ◽  
Communication Model ◽  
Von Neumann ◽  
Bee Colony ◽  
Segmentation Problem

This paper proposes a modified artificial bee colony optimizer (MABC) by combining bee-to-bee communication pattern and multipopulation cooperative mechanism. In the bee-to-bee communication model, with the enhanced information exchange strategy, individuals can share more information from the elites through the Von Neumann topology. With the multipopulation cooperative mechanism, the hierarchical colony with different topologies can be structured, which can maintain diversity of the whole community. The experimental results on comparing the MABC to several successful EA and SI algorithms on a set of benchmarks demonstrated the advantage of the MABC algorithm. Furthermore, we employed the MABC algorithm to resolve the multilevel image segmentation problem. Experimental results of the new method on a variety of images demonstrated the performance superiority of the proposed algorithm.

Multilevel Thresholding for Image Segmentation Based on Cellular Metaheuristics

2018 ◽  
Vol 9 (4) ◽  
pp. 1-32 ◽  
Author(s):  
Mohamed Abdou Bouteldja ◽  
Mohamed Baadeche ◽  
Mohamed Batouche
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Multilevel Thresholding ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Cellular Genetic Algorithm ◽  
Kapur’S Entropy ◽  
Computationally Expensive ◽  
Segmentation Problem ◽  
Nature Inspired Metaheuristics

This article describes how multilevel thresholding image segmentation is a process used to partition an image into well separated regions. It has various applications such as object recognition, edge detection, and particle counting, etc. However, it is computationally expensive and time consuming. To alleviate these limitations, nature inspired metaheuristics are widely used to reduce the computational complexity of such problem. In this article, three cellular metaheuristics namely cellular genetic algorithm (CGA), cellular particle swarm optimization (CPSO) and cellular differential evolution (CDE) are adapted to solve the multilevel thresholding image segmentation problem. Experiments are conducted on different test images to assess the performance of the cellular algorithms in terms of efficiency, quality and stability based on the between-class variance and Kapur's entropy as objective functions. The experimental results have shown that the proposed cellular algorithms compete with and even outperform existing methods for multilevel thresholding image segmentation.

A Kind of Watershed Image Segmentation Method Based on Combination Pre-Processing

2010 ◽  
Vol 121-122 ◽  
pp. 320-324
Author(s):  
Jin Xi Wang ◽  
Lin Xiang Liu ◽  
Xiu Zheng Li
Keyword(s):  
Image Segmentation ◽  
Segmentation Algorithm ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Simple Operation ◽  
Processing Steps ◽  
Segmentation Problem ◽  
Image Segmentation Algorithm

The watershed algorithm has been widely used in image segmentation for its characteristics of accurately positioning edge, simple operation and etc. But it also has drawbacks of easy to over-segmentation and loss important outline for the character of sensitive to noise. Aiming at the problem of over-segmentation of watershed algorithm, the paper brought out an improved image segmentation algorithm based on watershed, which can limit the number of existing regions that are allowed with combination pre-processing steps, so that the over-segmentation problem can be better solved. The result of experiment also verifies the correctness and feasibility of the proposed algorithm in the paper.

scholarly journals LCC-Net: A Lightweight Cross-Consistency Network for Semisupervised Cardiac MR Image Segmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lai Song ◽  
Jiajin Yi ◽  
Jialin Peng
Keyword(s):  
Image Segmentation ◽  
Semantic Segmentation ◽  
Representation Learning ◽  
Input Image ◽  
Unlabeled Data ◽  
Clinical Practices ◽  
Mr Image ◽  
Vast Number ◽  
Computation Efficiency ◽  
Model Training

Semantic segmentation plays a crucial role in cardiac magnetic resonance (MR) image analysis. Although supervised deep learning methods have made significant performance improvements, they highly rely on a large amount of pixel-wise annotated data, which are often unavailable in clinical practices. Besides, top-performing methods usually have a vast number of parameters, which result in high computation complexity for model training and testing. This study addresses cardiac image segmentation in scenarios where few labeled data are available with a lightweight cross-consistency network named LCC-Net. Specifically, to reduce the risk of overfitting on small labeled datasets, we substitute computationally intensive standard convolutions with a lightweight module. To leverage plenty of unlabeled data, we introduce extreme consistency learning, which enforces equivariant constraints on the predictions of different perturbed versions of the input image. Cutting and mixing different training images, as an extreme perturbation on both the labeled and unlabeled data, are utilized to enhance the robust representation learning. Extensive comparisons demonstrate that the proposed model shows promising performance with high annotation- and computation-efficiency. With only two annotated subjects for model training, the LCC-Net obtains a performance gain of 14.4% in the mean Dice over the baseline U-Net trained from scratch.

ADAPTIVE IMAGE SEGMENTATION USING ARTIFICIAL CO-EVOLVING TRIBES

2007 ◽  
Vol 21 (07) ◽  
pp. 1171-1193
Author(s):  
YUN WEN CHEN ◽  
YAN QIU CHEN
Keyword(s):  
Image Segmentation ◽  
Visual Perception ◽  
Artificial Life ◽  
Experimental Results ◽  
The Other ◽  
Evolution Process ◽  
Human Visual Perception ◽  
Segmentation Methods ◽  
Scale Control ◽  
Segmentation Problem

Deriving from the artificial life theory, this paper proposes an artificial co-evolving tribes model and applies it to solve the image segmentation problem. During the evolution process, the individuals in this model making up the tribes effect communication cooperatively from one agent to the other in order to increase the homogeneity of the ensemble of the image regions they represent. Two remarkable properties, that is, the monotone contraction and the conservation of the system are proved. Stability and scale control of the proposed method are carefully analyzed. Experimental results are presented and compared with two latest segmentation methods, both quantitatively and visually. We also discuss the results matching with human visual perception.

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