Improved skin lesion edge detection method using Ant Colony Optimization

Sengupta, Sudhriti; Mittal, Neetu; Modi, Megha

doi:10.1111/srt.12744

Cited by 19 publications

(12 citation statements)

References 22 publications

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“…As an image processing technology, edge detection is a hotspot, and it needs to be optimized in response to the complexity of medical images and the increasing amount of information contained [ 11 , 12 ]. Compared with the classic edge detection algorithm, the improved Prewitt algorithm proposed by Sengupta et al [ 13 ] greatly shortens the image processing time. On the basis of the gradient operator, Dash et al [ 14 ] used Gaussian filtering to smooth the image, and then, the Laplacian gradient edge detector was used to detect the edge of the image.…”

Section: Introductionmentioning

confidence: 99%

Edge Detection Algorithm-Based Lung Ultrasound in Evaluation of Efficacy of High-Flow Oxygen Therapy on Critical Lung Injury

Lü¹,

Xie

Ding

2022

Computational and Mathematical Methods in Medicine

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The study focused on the therapeutic effects of high-flow oxygen therapy on patients with critical lung injury using edge detection-based ultrasound images. Firstly, the traditional Canny edge detection algorithm was improved, and the optimal threshold was obtained by optimizing the median filter and combining Otsu algorithm and threshold iteration method. Then, the optimized algorithm was compared with the traditional Canny edge detection algorithm and applied to process the lung ultrasound images of 120 cases of critical lung injury, to compare the efficacy of high-flow oxygen therapy and the traditional oxygen therapy. It was found that the peak signal-to-noise ratio (PSNR) (20.34~31.3), edge intensity value (17.89~27.34), and edge detection effect of the improved Canny algorithm were better than the traditional Canny algorithm (15.2~28.61, 9.44~18.56). The failure rate of extubation (4.1%), reintubation rate (0.8%), comfort ( 2.38 ± 0.15 points), dry humidity score ( 1.07 ± 0.21 points), antibiotic use ( 7.41 ± 0.74 days), and hospital stay ( 8.66 ± 1.02 days) in the experimental group were significantly lower than the corresponding indexes in the control group (11.7%, 5%, 4.25 ± 0.26 minutes, 4.94 ± 0.78 minutes, 19.29 ± 1.7 days, and 27.49 ± 2.22 days), and the difference was statistically significant ( P < 0.05 ). In the experimental group, within 48 hours after extubation, the respiratory rate (RR), heart rate (HR), arterial partial pressure of carbon dioxide (PaCO2), and HCO3- were significantly lower than those of the control group; and the values of transcutaneous oxygen saturation (SpO2), mean arterial pressure (MAP), arterial partial pressure of oxygen (PaO2), and pH were significantly higher than the control group, and the difference was statistically significant ( P < 0.05 ). In conclusion, the algorithm in this study is superior to the traditional Canny algorithm, and the high-flow oxygen therapy can reduce the failure rate of extubation, strengthen patient comfort, improve the degree of gas humidification, stabilize the respiratory function and circulatory system, and shorten the time of antibiotic use and hospital stay.

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Section: Introductionmentioning

confidence: 99%

Edge Detection Algorithm-Based Lung Ultrasound in Evaluation of Efficacy of High-Flow Oxygen Therapy on Critical Lung Injury

Lü¹,

Xie

Ding

2022

Computational and Mathematical Methods in Medicine

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“…Various segmentation results were presented to assess the efficiency and accuracy of the proposed method. In addition, the results are compared vs. existing methods including Prewitt, Canny, Sobel, CDEDA Method [9], CSEDM Method [10] and QFEDM Method [11]. The experiments are carried out on the MATLAB software 10.…”

Section: Resultsmentioning

confidence: 99%

“…Several image segmentation techniques, such as discontinuity, divide the image based on isolated points detection and abrupt change in grayscale [10,11]. Another technique is edge detection, a fundamental model that delivers information about the boundaries of different objects within the image [11]. Hence, selecting the proper technique is essential to reach the best segmentation results [12,13].…”

Section: Introductionmentioning

confidence: 99%

Color Edge Detection Using Multidirectional Sobel Filter and Fuzzy Fusion

Chaabane¹,

Bushnag²

2023

Computers, Materials &Amp; Continua

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A new model is proposed in this paper on color edge detection that uses the second derivative operators and data fusion mechanism. The secondorder neighborhood shows the connection between the current pixel and the surroundings of this pixel. This connection is for each RGB component color of the input image. Once the image edges are detected for the three primary colors: red, green, and blue, these colors are merged using the combination rule. Then, the final decision is applied to obtain the segmentation. This process allows different data sources to be combined, which is essential to improve the image information quality and have an optimal image segmentation. Finally, the segmentation results of the proposed model are validated. Moreover, the classification accuracy of the tested data is assessed, and a comparison with other current models is conducted. The comparison results show that the proposed model outperforms the existing models in image segmentation.

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“…Feature selections are made by using a genetic algorithm and classify the lesion by using SVM. Sengupta, Mittal, and Modi () presented an improved lesion segmentation method. First, a three‐stage preprocessing approach, which comprised contrast enhancement, color space conversion, and filtering, is applied.…”

Section: Related Workmentioning

confidence: 99%

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

Rehman

Khan

Mehmood

et al. 2020

Microscopy Res & Technique

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The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel‐based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean‐based function is implemented and fed input to top‐hat and bottom‐hat filters which later fused for contrast stretching, (b) seed region growing and graph‐cut method‐based lesion segmentation and fused both segmented lesions through pixel‐based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy‐based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.

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Improved skin lesion edge detection method using Ant Colony Optimization

Cited by 19 publications

References 22 publications

Edge Detection Algorithm-Based Lung Ultrasound in Evaluation of Efficacy of High-Flow Oxygen Therapy on Critical Lung Injury

Edge Detection Algorithm-Based Lung Ultrasound in Evaluation of Efficacy of High-Flow Oxygen Therapy on Critical Lung Injury

Color Edge Detection Using Multidirectional Sobel Filter and Fuzzy Fusion

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

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