A novel reformed histogram equalization based medical image contrast enhancement using krill herd optimization

Kandhway, Pankaj; Bhandari, Ashish Kumar; Singh, Anurag

doi:10.1016/j.bspc.2019.101677

Cited by 86 publications

(38 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They used Sobel filters to increase the intensity of the edges, obtaining an accuracy of 93%. Pankaj et al [29] found that Sobel operators are better at distinguishing the edge details in an image than the Laplacian and Canny edge detection operators. They compared the three operators by applying them to various medical images (mammograms, brain images, etc.,).…”

Section: B Pectoral Muscle Removalmentioning

confidence: 99%

Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN

et al. 2021

View full text Add to dashboard Cite

Breast cancer is the most diagnosed cancer in Australia with crude incidence rates increasing drastically from 62.8 at ages 35-39 to 271.4 at ages 50-54 (cases per 100,000 women). Various researchers have proposed methods and tools based on Machine Learning and Convolutional Neural Networks for assessing mammographic images, but these methods have produced detection and interpretation errors resulting in false-positive and false-negative cases when used in the real world. We believe that this problem can potentially be resolved by implementing effective image pre-processing techniques to create training data for Deep-CNN. Therefore, the main aim of this research is to propose effective image preprocessing methods to create datasets that can save computational time for the neural network and improve accuracy and classification rates. To do so, this research proposes methods for background removal, pectoral muscle removal, adding noise to the images, and image enhancements. Adding noise without affecting the quality of details in the images makes the input images for the neural network more representative, which may improve the performance of the neural network model when used in the real world. The proposed method for background removal is the "Rolling Ball Algorithm" and "Huang's Fuzzy Thresholding", which succeed in removing background from 100% of the images. For pectoral muscle removal "Canny Edge Detection" and "Hough's Line Transform" are used, which removed muscle from 99.06% of the images. "Invert", "CTI_RAS" and "ISOCONTOUR" lookup tables (LUTs) were used for image enhancements to outline the ROIs and regions within the ROIs.

show abstract

Section: B Pectoral Muscle Removalmentioning

confidence: 99%

Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The statistically verified results demonstrate that the suggested approach improves consistency and segmentation quality. Furthermore, [ 53 , 54 ] also using the optimization algorithms for medical image segmentation. The researchers in [ 55 ] proposed a multilevel thresholding method for medical image segmentation based on a partitioned and cooperative quantum-behaved PSO.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images

Houssein

Emam

Ali

2021

Neural Comput & Applic

View full text Add to dashboard Cite

Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to people’s safety and health. Many algorithms were developed to identify COVID-19. One way of identifying COVID-19 is by computed tomography (CT) images. Some segmentation methods are proposed to extract regions of interest from COVID-19 CT images to improve the classification. In this paper, an efficient version of the recent manta ray foraging optimization (MRFO) algorithm is proposed based on the oppositionbased learning called the MRFO-OBL algorithm. The original MRFO algorithm can stagnate in local optima and requires further exploration with adequate exploitation. Thus, to improve the population variety in the search space, we applied Opposition-based learning (OBL) in the MRFO’s initialization step. MRFO-OBL algorithm can solve the image segmentation problem using multilevel thresholding. The proposed MRFO-OBL is evaluated using Otsu’s method over the COVID-19 CT images and compared with six meta-heuristic algorithms: sine-cosine algorithm, moth flame optimization, equilibrium optimization, whale optimization algorithm, slap swarm algorithm, and original MRFO algorithm. MRFO-OBL obtained useful and accurate results in quality, consistency, and evaluation matrices, such as peak signal-to-noise ratio and structural similarity index. Eventually, MRFO-OBL obtained more robustness for the segmentation than all other algorithms compared. The experimental results demonstrate that the proposed method outperforms the original MRFO and the other compared algorithms under Otsu’s method for all the used metrics.

show abstract

“…The method enhances the contrast with region mapping on T2-weighted image hyperintensities with estimating PSNR and gradient parameters. Improvised optimal contrast and edge enrichment method presented by Pankaj et al, for images using Krill herd technique was introduced in [12]. Using the least and extreme average and intermediate histogram values, the KH method automatically adjusts the parameter with the fitness function.…”

Section: Related Workmentioning

confidence: 99%

Security Assured CNN-Based Model for Reconstruction of Medical Images on the Internet of Healthcare Things

Singla

Verma

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Medical Imaging is the most significant technique that constitutes information needed to diagnose and make the right decisions for treatment. These images suffer from inadequate contrast and noise that occurs during image acquisition. Thus, denoising and contrast enhancement is crucial in increasing the visual quality of the images for obtaining quantitative measures. In this research, an innovative and improvised denoising technique is implemented that applies a sparse aware with convolution neural network (SA_CNN) for investigating various medical modalities. To evaluate and validate, the convolution neural network utilizes patch creation and dictionary methods for obtaining information. The proposed framework is predominant to other current approaches by employing image assessment quantitative measures like peak signal to noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE). The study also optimizes the computational time to achieve increased efficiency and better visual quality of the image. Furthermore, the widespread use of the Internet of Healthcare Things (IoHT) helps to provide security with vault and challenge schemes between IoT devices and servers.

show abstract

A novel reformed histogram equalization based medical image contrast enhancement using krill herd optimization

Cited by 86 publications

References 38 publications

Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN

Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN

Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images

Security Assured CNN-Based Model for Reconstruction of Medical Images on the Internet of Healthcare Things

Contact Info

Product

Resources

About