CT Image Segmentation Method of Liver Tumor Based on Artificial Intelligence Enabled Medical Imaging

Liu, Liping; Wang, Lin; Xu, Dan; Zhang, Hongjie; Sharma, Ashutosh; Tiwari, Shailendra; Kaur, Manjit; Khurana, Manju; Shah, Mohd Asif

doi:10.1155/2021/9919507

Cited by 15 publications

(10 citation statements)

References 42 publications

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“…On the basis of ROI extraction and Canny operator's preliminary detection of cell edges, Gauss conic curve fitting was used to accurately locate cell edges. This method has a good segmentation effect on elliptic cells, but not on irregular shape cells [ 11 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Construction of Biologic Microscopic Image Segmentation Model Based on Smoothing of Fourth-Order Partial Differential Equation

2022

Scanning

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In order to solve the problem of microscopic image noise, a biological microscopic image segmentation model based on the smoothing of the fourth-order partial differential equation was proposed. Based on the functional description of image smoothness by directional curvature mode value, a fourth-order PDE image denoising model is derived, which can effectively reduce noise while preserving edges. The result of this method is piecewise linear image, and the gradient at the edge of the target has a step. Using the feature of noise reduction, a new geodesic active contour model is proposed. The experiment result shows that when the variance of Gaussian white noise is 15, the enhancement and denoising effects of the proposed method are 80.35% and 69.84 higher than those of the original vibration filtering method and L. Alvarez method. In terms of time, the proposed method is 1.3075 seconds slower than the original vibration filtering method and 17.5754 seconds faster than the L. Alvarez method. When the variance of Gaussian white noise is 25, the enhancement and denoising effects of the proposed method are 97.79% and 81.16 higher than those of the original vibration filtering method and L. Alvarez method. In terms of time, the proposed method is 1.3246 seconds slower than the original vibration filtering method and 17.5796 seconds faster than the L. Alvarez method. Conclusion. The new model is not only stable but also has strong ability of contour extraction and fast convergence.

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Section: Literature Reviewmentioning

confidence: 99%

Construction of Biologic Microscopic Image Segmentation Model Based on Smoothing of Fourth-Order Partial Differential Equation

2022

Scanning

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“…Liu et al proposed local extrema. The method first constructs the maximum and minimum envelopes on the extreme values selected in the local sliding window and then calculates a smooth mean envelope, so that the oscillations with high contrast can be removed [ 8 ]. Zhang and Tian proposed relative total variation (RTV).…”

Section: Literature Reviewmentioning

confidence: 99%

Construction of Computer Microscope Image Segmentation Model Based on Fourth-Order Partial Differential Equation Smoothing

2022

Scanning

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In order to solve the problem of image noise, the author proposes a computer microscope image segmentation model based on the smoothing of fourth-order partial differential equations. On the basis of the functional describing the smoothness of the image by the directional curvature modulus, the author deduces a fourth-order partial differential equation (PDE) image noise reduction model, while effectively reducing noise, the edges are well preserved. The processing result of this method is a piecewise linear image, and there is a step in the gradient at the edge of the target. Taking advantage of this feature of the noise reduction results, the author proposes a new geodesic active contour model. The experimental results show that the reference method directly segments the results, iterates 10 times, and takes 160.721 seconds. Using the noise reduction model in the paper to preprocess and then using the reference method to segment the result, iterating 8 times, it takes 32.347 seconds. Conclusion. The new model is not only stable but also has strong contour extraction ability and fast convergence speed.

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“…Firstly, the pretrained CNN models are compared with the proposed model in order to validate its flexibility and performance. The various pretrained CNN models used are AlexNet [29], GoogleNet [30], ResNet [31] and VggNet [32,33]. The comparison is drawn in terms of time intricacy and accuracy which are shown in Figure 8.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

An Optimized Deep Learning based Technique for Grading and Extraction of Diabetic Retinopathy Severities

Zhang

Luo²,

Cengiz

2021

IJCAI

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The prognosis of Diabetic Retinopathy (DR) requires regular eye examinations, as ophthalmologists depends on fundus segmentation to treat DR pathologies. Automated approaches for detection, segmentation and classification have developed as an imperative area of research for the effective diagnosis of DR for the treatment of serious eye conditions that prevent visual impairment. Diagnosis of various DR lesions, as well as different severities, helping the ophthalmologists to analyze variations in fundus images and take the necessary measures before the disease progresses. Deep learning techniques have evolved as a recent advent to combat the issues of conventional machine leaning based methods. An optimized deep learning framework is proposed in this article for grading and extraction of diabetic retinopathy severities. This involves various steps like background segmentation, feature set extraction, feature optimization using Cuckoo search and Convolutional Neural Network (CNN) severity grade classification. The method was validated on two standard datasets MESSIDOR and IDRiD. The proposed method yields an accuracy value of 97.55%, cross entropy loss of 0.367 and time intricacy of 20 mins and 15 secs for MESSIDOR and 98.02% cross entropy loss of 0.345 and time intricacy of 22 mins and 21 secs for IDRiD dataset; respectively. The state-of-the-art comparison depicts that the proposed CNN based method provides a maximum accuracy improvement of 10.46% comparative to the existing methodology. The proposed framework yields better accuracy by procurement of the investigative outcomes acquired exhibits proficient DR determination.Povzetek: Opisana je metoda ugotavljanja sladkorne bolezni iz slik očesnega ozadje na osnovi strojnega in globokega učenja.

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CT Image Segmentation Method of Liver Tumor Based on Artificial Intelligence Enabled Medical Imaging

Cited by 15 publications

References 42 publications

Construction of Biologic Microscopic Image Segmentation Model Based on Smoothing of Fourth-Order Partial Differential Equation

Construction of Biologic Microscopic Image Segmentation Model Based on Smoothing of Fourth-Order Partial Differential Equation

Construction of Computer Microscope Image Segmentation Model Based on Fourth-Order Partial Differential Equation Smoothing

An Optimized Deep Learning based Technique for Grading and Extraction of Diabetic Retinopathy Severities

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