Automatic segmentation of gallbladder using bio-inspired algorithm based on a spider web construction model

Muneeswaran, V.; Rajasekaran, M. Pallikonda

doi:10.1007/s11227-017-2230-4

Cited by 23 publications

(5 citation statements)

References 39 publications

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“…Not only that, with the acquisition of enough data, it is possible to achieve more accurate and rapid assisted diagnostic decisions for ophthalmic diseases, driven by massive data [7]. On the other hand, if we do not use computer-aided technology to segment the retinal vessels in the fundus, we can only rely on the doctors' own experience to perform subjective segmentation of the vessels, but the subjective segmentation is time-consuming and inefficient, and the accuracy is low, and the subjectivity is too strong [8]. Different doctors often give different segmentation results for the same fundus image, and even the same doctor will give different results for the same fundus image before and after two different segmentation tests.…”

Section: Introductionmentioning

confidence: 99%

Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

Zhang

Zhang²,

Feng

2021

Journal of Healthcare Engineering

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Data extraction and visualization of 3D medical images of ocular blood vessels are performed by geometric transformation algorithm, which first performs random resonance response in a global sense to achieve detection of high-contrast coarse blood vessels and then redefines the input signal as a local image shielding the global detection result to achieve enhanced detection of low-contrast microfine vessels and complete multilevel random resonance segmentation detection. Finally, a random resonance detection method for fundus vessels based on scale decomposition is proposed, in which the images are scale decomposed, the high-frequency signals containing detailed information are randomly resonantly enhanced to achieve microfine vessel segmentation detection, and the final vessel segmentation detection results are obtained after fusing the low-frequency image signals. The optimal stochastic resonance response of the nonlinear model of neurons in the global sense is obtained to detect the high-grade intensity signal; then, the input signal is defined as a local image with high-contrast blood vessels removed, and the parameters are optimized before the detection of the low-grade intensity signal. Finally, the multilevel random resonance response is fused to obtain the segmentation results of the fundus retinal vessels. The sensitivity of the multilevel segmentation method proposed in this paper is significantly improved compared with the global random resonance results, indicating that the method proposed in this paper has obvious advantages in the segmentation of vessels with low-intensity levels. The image library was tested, and the experimental results showed that the new method has a better segmentation effect on low-contrast microscopic blood vessels. The new method not only makes full use of the noise for weak signal detection and segmentation but also provides a new idea of how to achieve multilevel segmentation and recognition of medical images.

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Section: Introductionmentioning

confidence: 99%

Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

Zhang

Zhang²,

Feng

2021

Journal of Healthcare Engineering

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“…These controllers must handle the TR issue while maintaining motor speed. As a result, 5 the effectiveness of TRC is improved by substituting an appropriately designed FOPID controller for the Spider Web-based Controller (SWC). Reduced oscillation and a negligible settling time are benefits of using FOPID controllers over traditional PID controllers.…”

Section: Suggested Systemmentioning

confidence: 99%

BLDC motor torque ripple factor lowering and FOPID based motion control using DGOA algorithm

2023

Journal of Low Frequency Noise, Vibration and Active Control

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The Brushless Direct Current (BLDC) motor has many benefits due to characteristics like its small size and precise speed regulation. Uncertainty problems are presented by the system’s slow Proportional-Integral (PI) controller response time as well as some BLDC motor operating circumstances. The proposed Fractional-Order Proportional-Integral-Derivative (FOPID) controller and Difference of Orientation Offset Gaussian (DOOG) Grasshopper Optimization Algorithm (GOA) (DGOA) function are used to mitigate torque ripples in BLDC motors and overcome these drawbacks. An adaptive parameter has been introduced to enhance the performance of the conventional GOA method, and the performance of the suggested DGOA is compared to the benchmark functions. The simulation of the driver with the suggested DGOA-FOPID results in an improvement in Torque Ripple Reduction and speed control is obtained that better torque ripple reduction and speed regulation have been achieved.

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“…Precise segmentation of the gallbladder and gallstone from the ultrasound scan image is a difficult task in medical image processing. To solve this problem, Muneeswaran et al [12] presented a bio-inspired algorithm based on the spider web construction process for the image segmentation process. They focused on the detection of gallbladder shape and the segmentation of gallstones and polyps within the gallbladder using a computerized algorithm.…”

Section: Optimized Data Processing and Sharingmentioning

confidence: 99%

Machine learning on big data for future computing

2019

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IntroductionRecently, the integration of big data and machine learning technologies has emerged as an exciting new paradigm for industry and life. These technologies are fast-growing areas of research that cover the fields of Information and Communication Technologies (ICT) for future computing (FC). It becomes a preferable solution for solving complex problems in different fields, such as artificial intelligence, large-scale analysis systems, natural language processing, pattern recognition, video analysis, and telematics. For instance, AlphaGo, developed by Google DeepMind, changed the current paradigm of machine learning on big data after its steady win in the Google DeepMind Challenge. Machine learning is the art and science of designing computer algorithms from the dataset, and AlphaGo learns and improves performance by relying on massive data associated with huge games played by humans to improve artificial intelligence. Likewise, there are many issues to realize and provide effective services and applications with enormous attentions, and efforts have been focused on machine learning on big data for FC. Contributions to theoretical research presenting advanced technologies and concepts, analysis and reporting of experiences, and experiences related to the implementation and application of theories, as well as tutorials on emerging trends are required for these fields of research. As such, the purpose of this special issue is to provide an overview of the state-ofthe-art technologies and solution guidelines for these areas of research.This special issue covers pure research and applications within novel scopes related to machine learning on big data for future computing. In addition, it deals

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Automatic segmentation of gallbladder using bio-inspired algorithm based on a spider web construction model

Cited by 23 publications

References 39 publications

Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

BLDC motor torque ripple factor lowering and FOPID based motion control using DGOA algorithm

Machine learning on big data for future computing

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