A novel color image multilevel thresholding based segmentation using nature inspired optimization algorithms

Bhandari, Ashish Kumar; Kumar, Arun; Chaudhary, Suneeta; Singh, Girish Kumar

doi:10.1016/j.eswa.2016.06.044

Cited by 106 publications

(41 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Traditional methods [1–3] need to manually design wound features for the classification algorithms used in the wound image processing, such as wound segmentation, tissue detection, and wound diagnosis. Deep learning [4–7] is popular with recent years.…”

Section: Introductionmentioning

confidence: 99%

A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

Wang

Liu

et al. 2018

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Wound segmentation plays an important supporting role in the wound observation and wound healing. Current methods of image segmentation include those based on traditional process of image and those based on deep neural networks. The traditional methods use the artificial image features to complete the task without large amounts of labeled data. Meanwhile, the methods based on deep neural networks can extract the image features effectively without the artificial design, but lots of training data are required. Combined with the advantages of them, this paper presents a composite model of wound segmentation. The model uses the skin with wound detection algorithm we designed in the paper to highlight image features. Then, the preprocessed images are segmented by deep neural networks. And semantic corrections are applied to the segmentation results at last. The model shows a good performance in our experiment.

show abstract

Section: Introductionmentioning

confidence: 99%

A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

Wang

Liu

et al. 2018

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Many multilevel thresholding methods have been developed from these kinds of algorithms, and almost every new heuristic is used in multilevel thresholding not long after it is proposed as Human Mental Search (HMS) [13][14][15]. These optimization methods combined with Otsu's and Kapur's segmentation index are widely used in multithresholding segmentation [16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

A Multilevel Image Thresholding Method Based on Subspace Elimination Optimization

Qin

Wang

Qin

2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Multilevel thresholding is to find the thresholds to segment the image with grey levels. Usually, the thresholds are so determined that some indicator functions of the segmented image are optimized. To improve the computational efficiency, we presented an optimization method for multilevel thresholding. First, the solution space is divided into subspaces. Second, the subspaces are searched to obtain their current local optimal value. Third, the subspaces that are of worse current optimal value are eliminated. Then, the next round of elimination is exerted in the remainder. The elimination is repeated until only one subspace is left and its optimal value is taken as the global optimum. In principle, any random search algorithm can be used to find the local optimum in a subspace block because it is a strategy to enhance the searching efficiency through eliminating hopeless regions as early as possible, rather than to improve the searching algorithm itself. To verify its performance, taking PSO (Particle swarm optimization) as the basic searching algorithm of subspaces, the presented method is applied to Otsu’s and Kapur’s multilevel thresholding of four different kinds of digital images. The presented method is compared with PSO, and it behaves better in efficiency.

show abstract

“…Retinal vasculature changes give important information in identification of these diseases. Image can be segmented in variety of ways, among these thresholding based methods [5]- [8] gained importance because of its effectiveness.…”

Section: Introductionmentioning

confidence: 99%

Swarm Intelligence Techniques in Segmenting Human Retinal Vasculature

Anitha¹,

Sridevi²

2019

IJRTE

View full text Add to dashboard Cite

Vasculature of human retina furnishes information concerning various eye related ailments and also assists in lesions detection. Severity of the eye diseases can be discerned from pathological conditions related to changes in the retinal vasculature. In this work, for pre-processing, Contrast Limited Adaptive Histogram Equalization (CLAHE) and average filter is used to enhance the input image. Further, swarm intelligence techniques, Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO), Fractional OrderDarwinian Particle Swarm Optimization (FO-DPSO) are used in segmenting the blood vessels of the human retina. Additionally, similarity index metrics are employed in evaluating the accuracy of the retinal vasculature segmentation with ground truth. The results obtained clearly reveals that FO-DPSO outperforms in segmenting accurately than PSO and DPSO. Results of the segmentation are further reinforced using box and dendogram plot

show abstract

A novel color image multilevel thresholding based segmentation using nature inspired optimization algorithms

Cited by 106 publications

References 57 publications

A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

A Multilevel Image Thresholding Method Based on Subspace Elimination Optimization

Swarm Intelligence Techniques in Segmenting Human Retinal Vasculature

Contact Info

Product

Resources

About