scholarly journals A Hybrid Method for Image Segmentation Based on Artificial Fish Swarm Algorithm and Fuzzyc-Means Clustering

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Li Ma ◽  
Yang Li ◽  
Suohai Fan ◽  
Runzhu Fan
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Clustering Algorithms ◽  
Optimal Solution ◽  
Medical Image Segmentation ◽  
Superior Result ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Fcm Clustering ◽  
Swarm Algorithm

Image segmentation plays an important role in medical image processing. Fuzzyc-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA). The proposed algorithm combines artificial fish swarm algorithm (AFSA) with FCM whose advantages of global optimization searching and parallel computing ability of AFSA are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI) are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM).

Artificial Fish Swarm Algorithm Based on Fast Image Matching

2011 ◽  
Vol 271-273 ◽  
pp. 297-302
Author(s):  
Miao Ma ◽  
Jiao He ◽  
Min Guo
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Fitness Function ◽  
Optimal Solution ◽  
Fast Method ◽  
Artificial Fish Swarm Algorithm ◽  
Template Image ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
Exclusive Or

Due to the large amount of calculation and high time-consuming in traditional grayscale matching, this paper combines artificial fish algorithm of swarm intelligence with edge detection and the operation of bitwise exclusive or, and presents a fast method on feature matching. The method regards the problem of image matching as a process of searching the optimal solution. In order to provide artificial fish swarm algorithm with an appropriate fitness function, the operation of bitwise exclusive or and addition is employed to deal with the edge information extracted from the template image and the searching image. Then the best matching position is gradually approaching by swarming, following and other behaviors of artificial fish. Experimental results show that the proposed method not only significantly shortens the matching time and guarantees the matching accuracy, but also is robust to noise disturbance.

scholarly journals An Improved FCM Medical Image Segmentation Algorithm Based on MMTD

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Ningning Zhou ◽  
Tingting Yang ◽  
Shaobai Zhang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Spatial Information ◽  
Clustering Algorithms ◽  
Medical Image Segmentation ◽  
Segmentation Algorithm ◽  
Fuzzy Information ◽  
Spatial Features ◽  
Image Segmentation Algorithm ◽  
Truth Degree

Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) is one of the popular clustering algorithms for medical image segmentation. But FCM is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. It establishes the medium similarity measure based on the measure of medium truth degree (MMTD) and uses the correlation of the pixel and its neighbors to define the medium membership function. An improved FCM medical image segmentation algorithm based on MMTD which takes some spatial features into account is proposed in this paper. The experimental results show that the proposed algorithm is more antinoise than the standard FCM, with more certainty and less fuzziness. This will lead to its practicable and effective applications in medical image segmentation.

EFFICIENT ALGORITHMS FOR THE OPTIMAL-RATIO REGION DETECTION PROBLEMS IN DISCRETE GEOMETRY WITH APPLICATIONS

2009 ◽  
Vol 19 (02) ◽  
pp. 141-159 ◽  
Author(s):  
XIAODONG WU
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Graph Transformation ◽  
Optimal Solution ◽  
Maximum Flow ◽  
Small Region ◽  
Medical Image Segmentation ◽  
Optimal Ratio ◽  
Region Detection ◽  
Voxel Grid

In this paper, we study several interesting optimal-ratio region detection (ORD) problems in d- D (d ≥ 3) discrete geometric spaces, which arise in high dimensional medical image segmentation. Given a d- D voxel grid of n cells, two classes of geometric regions that are enclosed by a single or two coupled smooth heighfield surfaces defined on the entire grid domain are considered. The objective functions are normalized by a function of the desired regions, which avoids a bias to produce an overly large or small region resulting from data noise. The normalization functions that we employ are used in real medical image segmentation. To our best knowledge, no previous results on these problems in high dimensions are known. We develop a unified algorithmic framework based on a careful characterization of the intrinsic geometric structures and a nontrivial graph transformation scheme, yielding efficient polynomial time algorithms for solving these ORD problems. Our main ideas include the following. We observe that the optimal solution to the ORD problems can be obtained via the construction of a convex hull for a set of O(n) unknown 2-D points using the hand probing technique. The probing oracles are implemented by computing a minimum s-t cut in a weighted directed graph. The ORD problems are then solved by O(n) calls to the minimum s-t cut algorithm. For the class of regions bounded by a single heighfield surface, our further investigation shows that the O(n) calls to the minimum s-t cut algorithm are on a monotone parametric flow network, which enables to detect the optimal-ratio region in the complexity of computing a single maximum flow.

scholarly journals Digital Medical Image Segmentation Using Fuzzy C-Means Clustering

2020 ◽  
Vol 4 (1) ◽  
pp. 51 ◽  
Author(s):  
Bakhtyar Ahmed Mohammed ◽  
Muzhir Shaban Al-Ani
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Brain Mri ◽  
Accurate Method ◽  
Medical Image Segmentation ◽  
Fuzzy C Means ◽  
Fcm Clustering ◽  
The Brain ◽  
Digital Medical Image

In the modern globe, digital medical image processing is a major branch to study in the fields of medical and information technology. Every medical field relies on digital medical imaging in diagnosis for most of their cases. One of the major components of medical image analysis is medical image segmentation. Medical image segmentation participates in the diagnosis process, and it aids the processes of other medical image components to increase the accuracy. In unsupervised methods, fuzzy c-means (FCM) clustering is the most accurate method for image segmentation, and it can be smooth and bear desirable outcomes. The intention of this study is to establish a strong systematic way to segment complicate medical image cases depend on the proposed method to share in the decision-making process. This study mentions medical image modalities and illustrates the steps of the FCM clustering method mathematically with example. It segments magnetic resonance imaging (MRI) of the brain to separate tumor inside the brain MRI according to four statuses.

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