Edge Detection Algorithm of MRI Medical Image Based on Artificial Neural Network

Shao, Yunhong; Shilei, Yuan; Zhou, Xiaohan; Ye, Jinhua

doi:10.1016/j.procs.2022.10.021

Cited by 6 publications

(7 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the second experiment, we aimed to determine a K p value that would result in the highest edge preservation. We calculated the metrics discussed in the last paragraph of Section 5 using a grid of 10 equally spaced values within the interval 1 10 ≤ Kp ≤ 1 2 ; subsequently, we computed the edge-oriented metrics and presented the distribution of this data in the box plot shown in Figure 4, which depicts the distribution of K p values that maximize each metric. This figure indicates that the median K p value for maximizing the metrics MSSSIM and GCMSE is 1 2 for nearly all images in the BSDS500; in contrast, the median for ESSIM is 0.4.…”

Section: Resultsmentioning

confidence: 99%

“…We calculated the metrics discussed in the last paragraph of Section 5 using a grid of 10 equally spaced values within the interval 1 10 ≤ Kp ≤ 1 2 ; subsequently, we computed the edge-oriented metrics and presented the distribution of this data in the box plot shown in Figure 4, which depicts the distribution of K p values that maximize each metric. This figure indicates that the median K p value for maximizing the metrics MSSSIM and GCMSE is 1 2 for nearly all images in the BSDS500; in contrast, the median for ESSIM is 0.4. Moreover, these results imply that finding a fixed K p value that ensures high edge preservation for the ESSIM metric is challenging in most cases.…”

Section: Resultsmentioning

confidence: 99%

“…The edges play a crucial role in computer vision tasks, such as in the medical image analysis field, where edges are expected to convey relevant information about patients' health; the inaccurate distinction of the boundaries in medical images could lead to a false diagnosis; therefore, it is paramount to identify and highlight edges effectively so that analysts can learn any information the image was intended to convey. An example of such works is [1], where authors stress the importance of carrying on effective image segmentation based on edge detection for reaching an accurate diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…However, current research is shifting towards techniques utilizing machine learning and deep-learning-based algorithms specifically tuned for detecting edges in images. For instance, Yunhong et al [1] developed an edge detection algorithm based on an artificial neural network (ANN), expecting that the edges information the ANN computes might be useful for diagnosing illness from magnetic resonance images (MRI). Their algorithm was reported to be up to three times faster than traditional edge detection schemes; however, they noted that one drawback of the ANN-based approach they proposed is that it requires data preparation and cleaning, which imply a significant additional effort compared to approaches based on the Canny or Sobel operators.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A New Class of Edge Filter Based on a Cross-correlation-like Equation Derived from Fractional Calculus Principles

Gonzalez-Lee,

Vazquez-Leal,

Garcia-Martinez

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

In this paper, we propose a new sliding window edge-oriented filter that computes the output pixels using a cross-correlation-like equation derived from the principles of fractional calculus (FC); thus, we call it the “fractional cross-correlation filter” (FCCF). We assessed the performance of this filter utilizing exclusively edge-preservation-oriented metrics such as the gradient conduction mean square error (GCMSE), the edge-based structural similarity (EBSSIM), and the multi-scale structural similarity (MS-SSIM); we conducted a statistical assessment of the performance of the filter based on those metrics by using the Berkeley segmentation dataset benchmark as a test corpus. Experimental data reveal that our approach achieves higher performance compared to conventional edge filters for all the metrics considered in this study. This is supported by the statistical analysis we carried out; specifically, the FCCF demonstrates a consistent enhancement in edge detection. We also conducted additional experiments for determining the main filter parameters, which we found to be optimal for a broad spectrum of images. The results underscore the FCCF’s potential to make significant contributions to the advancement of image processing techniques since many practical applications such as medical imaging, image enhancement, and computer vision rely heavily on edge detection filters.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Class of Edge Filter Based on a Cross-correlation-like Equation Derived from Fractional Calculus Principles

Gonzalez-Lee,

Vazquez-Leal,

Garcia-Martinez

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Edge detection is among the most significant topics in computer vision. The study of medical images [1] and object recognition [2] are two common uses of edge detection. The focus of edge detection techniques in the past has been on grayscale images.…”

Section: Introductionmentioning

confidence: 99%

Klasik ve Bulanık Kenar Algılama Yöntemlerinin Karşılaştırılması

OZDEMİR

2024

KONJES

View full text Add to dashboard Cite

Edge detection is one of the challenging problems in image processing. Four different classical edge detection methods—Sobel, Prewitt, Roberts, and Canny—and type-1 and type-2 fuzzy logic-based edge detection methods are applied to analyze two separate datasets with various properties. The datasets are STARE which contains medical images of the retina and BIPED which contains images of the street. Furthermore, two separate hybrid fuzzy logic methods are implemented. The type-1 and type-2 fuzzy inference techniques are combined to produce the hybrid-1 and hybrid-2 approaches, using the "AND" and "OR" logic operators. We compare the simulation results for each technique using three different image quality metrics. These are Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), and Structural Similarity Index (SSIM). The type-2 fuzzy technique outperformed the hybrid-1 fuzzy method in visual quality metrics comparison, demonstrating superior blood vessel recognition on the STARE retinal image dataset—a dataset that more closely resembles the human visual system. Using the BIPED street image dataset, the hybrid-1 fuzzy approach outperformed the Roberts method. The hybrid-1 fuzzy technique showed good results in the second order for both kinds of datasets. Any data and general applications can take advantage of it.

show abstract

EG-UNETR: An edge-guided liver tumor segmentation network based on cross-level interactive transformer

Cheng,

Zhou,

Zhang

2024

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

Edge Detection Algorithm of MRI Medical Image Based on Artificial Neural Network

Cited by 6 publications

References 2 publications

A New Class of Edge Filter Based on a Cross-correlation-like Equation Derived from Fractional Calculus Principles

A New Class of Edge Filter Based on a Cross-correlation-like Equation Derived from Fractional Calculus Principles

Klasik ve Bulanık Kenar Algılama Yöntemlerinin Karşılaştırılması

EG-UNETR: An edge-guided liver tumor segmentation network based on cross-level interactive transformer

Contact Info

Product

Resources

About