McCulloch’s algorithm inspired cuckoo search optimizer based mammographic image segmentation

2020 ◽  
Vol 79 (41-42) ◽  
pp. 30453-30488
Author(s):  
Kumar A. Santhos ◽  
A. Kumar ◽  
V. Bajaj ◽  
G. K. Singh
Keyword(s):  
Image Segmentation ◽  
Cuckoo Search ◽  
Mammographic Image

Cuckoo search with differential evolution mutation and Masi entropy for multi-level image segmentation

2021 ◽  
Author(s):  
Swarnajit Ray ◽  
Santanu Parai ◽  
Arunita Das ◽  
Krishna Gopal Dhal ◽  
Prabir Kumar Naskar
Keyword(s):  
Image Segmentation ◽  
Differential Evolution ◽  
Cuckoo Search ◽  
Multi Level ◽  
Masi Entropy

Elephant Herding Optimization for Multi-Level Image Thresholding

2020 ◽  
Vol 11 (4) ◽  
pp. 64-90
Author(s):  
Falguni Chakraborty ◽  
Provas Kumar Roy ◽  
Debashis Nandi
Keyword(s):  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Cuckoo Search ◽  
Threshold Value ◽  
Objective Functions ◽  
Image Thresholding ◽  
Bee Colony ◽  
Kapur’S Entropy ◽  
Feature Similarity Index

Multilevel thresholding plays a significant role in the arena of image segmentation. The main issue of multilevel image thresholding is to select the optimal combination of threshold value at different level. However, this problem has become challenging with the higher number of levels, because computational complexity is increased exponentially as the increase of number of threshold. To address this problem, this paper has proposed elephant herding optimization (EHO) based multilevel image thresholding technique for image segmentation. The EHO method has been inspired by the herding behaviour of elephant group in nature. Two well-known objective functions such as ‘Kapur's entropy' and ‘between-class variance method' have been used to determine the optimized threshold values for segmentation of different objects from an image. The performance of the proposed algorithm has been verified using a set of different test images taken from a well-known benchmark dataset named Berkeley Segmentation Dataset (BSDS). For comparative analysis, the results have been compared with three popular algorithms, e.g. cuckoo search (CS), artificial bee colony (ABC) and particle swarm optimization (PSO). It has been observed that the performance of the proposed EHO based image segmentation technique is efficient and promising with respect to the others in terms of the values of optimized thresholds, objective functions, peak signal-to-noise ratio (PSNR), structure similarity index (SSIM) and feature similarity index (FSIM). The algorithm also shows better convergence profile than the other methods discussed.

scholarly journals Multilevel Image Thresholding for Image Segmentation using Hybrid Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 4272-4279
Keyword(s):  
Image Segmentation ◽  
Search Algorithm ◽  
Similarity Index ◽  
Structural Similarity ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimization Techniques ◽  
Entropy Maximization ◽  
Image Thresholding ◽  
Segmentation Threshold

Image thresholding is an extraction method of objects from a background scene, which is used most of the time to evaluate and interpret images because of their advanced simplicity, robustness, time reduced, and precision. The main objective is to distinguish the subject from the background of the image segmentation. As the ordinary image segmentation threshold approach is computerized costly while the necessity for optimization techniques are highly recommended for multi-tier image thresholds. Level object segmentation threshold by using Shannon entropy and Fuzzy entropy maximized with hGSA-PS. An entropy maximization of hGSA-PS dependent multilevel image thresholds is developed, where the results are best demonstrated in PSNR, misclassification, structural similarity index and segmented image quality compared to the Firefly algorithm, adaptive cuckoo search algorithm and the search algorithm gravitational.

scholarly journals Hybrid Bee Colony and Cuckoo Search based centroid initialization for fuzzy c-means clustering in bio-medical image segmentation

2019 ◽  
Vol 8 (9) ◽  
pp. 1684-1688
Keyword(s):  
Image Segmentation ◽  
Brain Tissue ◽  
Medical Image ◽  
Cuckoo Search ◽  
Medical Image Segmentation ◽  
Tissue Segmentation ◽  
Bee Colony ◽  
Brain Tissue Segmentation ◽  
Fuzzy C Means Clustering ◽  
Number Of Iterations

In current years, the grouping has become well identified for numerous investigators due to several application fields like communication, wireless networking, and biomedical domain and so on. So, much different research has already been made by the investigators to progress an improved system for grouping. One of the familiar investigations is an optimization that has been efficiently applied for grouping. In this paper, propose a method of Hybrid Bee Colony and Cuckoo Search (HBCCS) based centroid initialization for fuzzy c-means clustering (FCM) in bio-medical image segmentation (HBCC-KFCM-BIM). For MRI brain tissue segmentation, KFCM is most preferable technique because of its performance. The major limitation of the conventional KFCM is random centroids initialization because it leads to rising the execution time to reach the best resolution. In order to accelerate the segmentation process, HBCCS is used to adjust the centroids of required clusters. The quantitative measures of results were compared using the metrics are the number of iterations and processing time. The number of iterations and processing of HBCC-KFCM-BIM method take minimum value while compared to conventional KFCM. The HBCC-KFCM-BIM method is very efficient and faster than conventional KFCM for brain tissue segmentation.

scholarly journals An Improved Cuckoo Search Algorithm for Multithreshold Image Segmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wentan Jiao ◽  
Wenqing Chen ◽  
Jing Zhang
Keyword(s):  
Image Segmentation ◽  
Maximum Entropy ◽  
Search Algorithm ◽  
Fitness Function ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Threshold Function ◽  
Size Factor ◽  
Step Size ◽  
Multiple Thresholds

Image segmentation is an important part of image processing. For the disadvantages of image segmentation under multiple thresholds such as long time and poor quality, an improved cuckoo search (ICS) is proposed for multithreshold image segmentation strategy. Firstly, the image segmentation model based on the maximum entropy threshold is described, and secondly, the cuckoo algorithm is improved by using chaotic initialization population to improve the diversity of solutions, optimizing the step size factor to improve the possibility of obtaining the optimal solution, and using probability to reduce the complexity of the algorithm; finally, the maximum entropy threshold function in image segmentation is used as the individual fitness function of the cuckoo search algorithm for solving. The simulation experiments show that the algorithm has a good segmentation effect under four different thresholding conditions.

PCNN-based level set method of automatic mammographic image segmentation

Optik ◽  
2016 ◽  
Vol 127 (4) ◽  
pp. 1644-1650 ◽  
Author(s):  
Weiying Xie ◽  
Yunsong Li ◽  
Yide Ma
Keyword(s):  
Image Segmentation ◽  
Level Set Method ◽  
Level Set ◽  
Mammographic Image
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