Comparative Analysis of Edge Detection Techniques for Medical Images of Different Body Parts

Dhruv, Bhawna; Mittal, Neetu; Modi, Megha

doi:10.1007/978-981-10-8527-7_15

Cited by 6 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Firstorder derivative operators include Sobel, Prewitt, Roberts, and Canny. Second-order derivative operators are more efficient but still sensitive to noise [20], [21]. Two examples of second-order derivative edge detection techniques the include difference of Gaussian (DoG) and the Laplacian of Gaussian (LoG) (e.g.…”

Section: Theoretical Edge Detection Techniquesmentioning

confidence: 99%

A Comparative Study of Various Edge Detection Techniques for Underwater Images

Awalludin¹,

Arsad²,

Yussof³

et al. 2022

JTIT

View full text Add to dashboard Cite

Nowadays, underwater image identification is a challenging task for many researchers focusing on various applications, such as tracking fish species, monitoring coral reef species, and counting marine species. Because underwater images frequently suffer from distortion and light attenuation, pre-processing steps are required in order to enhance their quality. In this paper, we used multiple edge detection techniques to determine the edges of the underwater images. The pictures were pre-processed with the use of specific techniques, such as enhancement processing, Wiener filtering, median filtering and thresholding. Coral reef pictures were used as a dataset of underwater images to test the efficiency of each edge detection method used in the experiment. All coral reef image datasets were captured using an underwater GoPro camera. The performance of each edge detection technique was evaluated using mean square error (MSE) and peak signal to noise ratio (PSNR). The lowest MSE value and the highest PSNR value represent the best quality of underwater images. The results of the experiment showed that the Canny edge detection technique outperformed other approaches used in the course of the project.

show abstract

Section: Theoretical Edge Detection Techniquesmentioning

confidence: 99%

A Comparative Study of Various Edge Detection Techniques for Underwater Images

Awalludin¹,

Arsad²,

Yussof³

et al. 2022

JTIT

View full text Add to dashboard Cite

show abstract

“…ACO is one of the swarm-based algorithms used effectively for combinatorial optimization problems. ACO is a heuristic positive feedback algorithm utilising intelligence of swarms that mimics the ant's behaviour and methods of communication in search of food [17][18][19]. The major advantages of ACO includes its strong searching capability in a population simultaneously, ability to adapt to new changes and capacity to discover multiple good solutions rapidly.…”

Section: Related Workmentioning

confidence: 99%

Early and Precise Detection of Pancreatic Tumor by Hybrid Approach with Edge Detection and Artificial Intelligence Techniques

Dhruv¹,

Mittal²,

Modi³

2018

EAI Endorsed Transactions on Pervasive Health and Technology

Self Cite

View full text Add to dashboard Cite

INTRODUCTION: Pancreatic cancer is highly lethal as it grows, spreads rapidly and difficult to diagnose at its early stages. It can be identified through scan images. The tumorous images obtained from imaging techniques suffer from the drawback of cryptic data due to presence of unwanted noise and poor contrast. OBJECTIVE: To reduce the risk of pancreatic cancer, its detection and diagnosis at an early stage becomes crucial. METHODS: The proposed work encompasses the processing of CT scans of pancreatic tumor using classical and artificial intelligence based optimized edge detection techniques for optimization and detection of tumor. RESULTS: The simulation results are highly encouraging as evident from the far improved visibility of resultant images with Particle Swarm Optimization. CONCLUSION: The output image with PSO shows the quality enhanced CT images which helps in accurate detection and diagnosis of the pancreatic tumor at an early stage providing an aid in medical imaging.

show abstract

“…Artificial intelligence technique optimized edge detection may reduce the computation time with greater visibility properties. Dhruv et al [18] presented a comparative analysis of edge detection techniques.…”

Section: Introductionmentioning

confidence: 99%

Identification of Change Detection for Satellite Images using Ant Colony Optimization

Sheoran

Mittal

Gelbukh

2022

Preprint

View full text Add to dashboard Cite

Supervision of Earth surface with the help of satellite images helps in land use land cover, resource planning & management, change detection and agricultural development areas. Satellites images are great repository of information and extraction of information from these unclear images is a challenging task. In order to identify the objects and boundaries in satellite images, various digital image based processing techniques like Sobel, Canny and Prewitt have been explored in this paper. Further, Ant Colony Optimization has been used to obtain the optimized images. Hybrid techniques viz.- AC0-Sobel, ACO-Canny and ACO-Prewitt are implemented and the results are quantitatively validated with the help of entropy and PIQE values of output optimized images. The results indicates that ACO-Canny hybrid technique yields better quality satellite images as compared to ACO-Sobel, ACO-Prewitt, Sobel, Canny and Prewitt edge detection techniques.

show abstract

Comparative Analysis of Edge Detection Techniques for Medical Images of Different Body Parts

Cited by 6 publications

References 17 publications

A Comparative Study of Various Edge Detection Techniques for Underwater Images

A Comparative Study of Various Edge Detection Techniques for Underwater Images

Early and Precise Detection of Pancreatic Tumor by Hybrid Approach with Edge Detection and Artificial Intelligence Techniques

Identification of Change Detection for Satellite Images using Ant Colony Optimization

Contact Info

Product

Resources

About