Comparative Analysis of Eight Direction Sobel Edge Detection Algorithm for Brain Tumor MRI Images

S, Remya Ajai A; Gopalan, Sundararaman

doi:10.1016/j.procs.2022.03.063

Cited by 29 publications

(10 citation statements)

References 13 publications

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“…[22] showed that the Canny algorithm can determine smoother and thinner edges than Sobel. [39] in their studies, they stated that the Canny edge detection method is better in terms of performance than other edge detection methods, but it contains complex processing steps. In the threshold values determined in the study, the edge detection algorithms and the morphological processing applied to MSE and PSNR regression models were used to compare the image edge detection quality of the images obtained.…”

Section: Resultsmentioning

confidence: 99%

Machine Whell Edge Detection Morphological Operations

KARAKUŞ

2024

KONJES

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One of the critical issues of image processing, defined as obtaining useful information from the image and improving the quality of the image, is edge detection. How edge detection performance will be affected by adding morphological operators to edge detection algorithms is among the issues that have not been fully resolved. In the study, Canny and Sobel edge detection algorithms were applied to different milling cutters used in machinability. Morphological operators were applied to the determined edges, and their effects on the edges were examined. Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR) values were used to compare the performances of edge detection algorithms. According to MSE and PSNR results, it was seen that the Canny algorithm gave better results than the Sobel algorithm. In addition, it was concluded that the images obtained as a result of the applied morphological operations provided better performance than the images that were not applied for both Canny and Sobel algorithms.

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Section: Resultsmentioning

confidence: 99%

Machine Whell Edge Detection Morphological Operations

KARAKUŞ

2024

KONJES

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“…The non-subsampled contourlet transform (NSCT), Tenengrad algorithm [ 29 ], Roberts algorithm [ 30 , 31 ], discrete cosine transform (DCT) [ 32 , 33 ], energy of gradient (EOG) [ 34 ] algorithm, Canny algorithm [ 35 , 36 ], and Laplacian algorithm [ 37 , 38 ] are selected for this comparative experiment. The Tenengrad algorithm uses a Sobel [ 39 , 40 ] operator to extract gradient values in horizontal and vertical directions, and then calculates the gradient square sum of all pixels. In addition, since the datasets involved in the experiment are small in size, the deep learning method cannot be used for comparative experiments.…”

Section: Resultsmentioning

confidence: 99%

A Method for Medical Microscopic Images’ Sharpness Evaluation Based on NSST and Variance by Combining Time and Frequency Domains

Zhou

et al. 2022

Sensors

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An algorithm for a sharpness evaluation of microscopic images based on non-subsampled shearlet wave transform (NSST) and variance is proposed in the present study for the purpose of improving the noise immunity and accuracy of a microscope’s image autofocus. First, images are decomposed with the NSST algorithm; then, the decomposed sub-band images are subjected to variance to obtain the energy of the sub-band coefficients; and finally, the evaluation value is obtained from the ratio of the energy of the high- and low-frequency sub-band coefficients. The experimental results show that the proposed algorithm delivers better noise immunity performance than other methods reviewed by this study while maintaining high sensitivity.

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“…Remya Ajai AS et al 19 provide analysis of the eight‐direction Sobel edge algorithm that detects edges in images for the detection of tumour in the brain. First, raw images were used for preprocessing to remove noise; further segmentation was applied to detect boundary or edges of tumour, after the processed images were passed to a deep neural network model for tumour grading, mainly tumour classified in four grade, 1–4.…”

Section: Related Workmentioning

confidence: 99%

Performance analysis ofstate‐of‐the‐art CNNarchitectures for brain tumour detection

Khushi,

Masood,

Jaffar

et al. 2023

Int J Imaging Syst Tech

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Deep learning models, such as convolutional neural network (CNN), are popular now a day to solve various complex problems in medical and other fields, such as image classification, object detection, recommendation of images, processing of natural languages and video and image analysis. So, the idea of studying the architecture of CNNs has gotten a lot of attention and become popular. This study analysed and contrasted the performance of many different CNN models trained on the publicly accessible Br35h dataset for the detection of brain tumours. These models included the LeNet, AlexNet, VGG16, VGG19 and ResNet50. Several optimisers were used in this research to fine‐tune the performance of the CNN model. These included Adam (adaptive moment estimation), SGD (stochastic gradient descent) and RMSprop (root‐mean‐square propagation). Accuracy, miss‐classification rate, sensitivity, specificity, NPV (negative predictive value), PPV (positive predictive value), F1‐score and false omission rate (FOR) were used to assess the efficacy of five different CNN architectures trained using three different optimisers. The experimental results showed that AlexNet architecture with SGD optimiser performed better than other CNN architecture with different optimisers and achieved the highest accuracy of 98.79% with a miss classification rate of 1.20%. It also achieved 98.98% sensitivity, 98.58% specificity, 98.93% NPV, 98.65% PPV, 98.82% F1‐score and 1.06% FOR.

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Comparative Analysis of Eight Direction Sobel Edge Detection Algorithm for Brain Tumor MRI Images

Cited by 29 publications

References 13 publications

Machine Whell Edge Detection Morphological Operations

Machine Whell Edge Detection Morphological Operations

A Method for Medical Microscopic Images’ Sharpness Evaluation Based on NSST and Variance by Combining Time and Frequency Domains

Performance analysis ofstate‐of‐the‐art CNNarchitectures for brain tumour detection

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