An adaptable active contour model for medical image segmentation based on region and edge information

Wang, Xianghai; Li, Wei; Zhang, Chong; Lou, Wanqi; Song, Ruoxi

doi:10.1007/s11042-019-08073-3

Cited by 12 publications

(7 citation statements)

References 20 publications

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“…The image segmentation by localizing region-based active contours was a model-based technique that could be used to process the viscera images (Duan, Peng, & Qi, 2020;Jayanthi & Shashikumar, 2020;Li, Cao, & Wang, 2020;Wang, Li, & Zhang, 2019). The curves could move to the target boundaries under the influence of internal forces and external forces, in which internal forces were used to hold the curves together and to keep them from bending too much.…”

Section: The Image Segmentation Methods Of Chicken Visceramentioning

confidence: 99%

Analysis of correlation between carcass and viscera for chicken eviscerating based on machine vision technology

Chen

2020

J Food Process Engineering

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In the poultry evisceration, the inconsistent position of visible carcass and invisible viscera may be one of the most neglected problems, which increase the level of visceral damage by gripping manipulator. In order to detect the position of the chicken carcass and viscera, a computer vision‐based automation system is developed to extract the region of interest from each chicken in the image acquisition system. The segmentation method of carcass is proposed by applying the color space transformation and the threshold segmentation. This method combines with several operations of morphology and reconstruction to remove the other regions such as wings and legs. After a midline abdominal incision of the chicken, the viscera is segmented using the active contour algorithm. Following data analysis, the change trend of relative position is presented between carcass and viscera. The average longitudinal deviation value of weighting 1,000–1,500 g and 1,500–2000 g chickens were 22.9 and 32.4 mm, separately. These results indicate that the relative position is significantly changed between carcass and viscera with chicken size in the longitudinal direction. In this study, machine vision technology can be satisfactorily applied to predict the position of chicken viscera, which can provide technical support for poultry processing.Practical applicationsFor a long time, avian influenza virus and various pathogenic microorganism have been threatening the breeding and slaughtering of poultry, which can easily damage the health of people and cause widespread transmission of the virus. Therefore, automated evisceration technology by intelligent robot will become the development trend of the poultry slaughtering. However, due to the invisibility of the poultry viscera, it will easily cause damage of the internal organs during the manipulator evisceration. In this study, in order to avoid repetitive and boring work and manual operation errors, machine vision technology was introduced to automatically obtain the relative position between the visible carcass and the invisible viscera of individual chicken based on image segmentation algorithms. Thus, the position of the viscera can be predicted based on the data analysis and the carcass location, which provide useful information to guide the robot for evisceration, and it is also helpful to conduct further research in the slaughtering and processing of other poultry.

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Section: The Image Segmentation Methods Of Chicken Visceramentioning

confidence: 99%

Analysis of correlation between carcass and viscera for chicken eviscerating based on machine vision technology

Chen

2020

J Food Process Engineering

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“…Yasmeen and colleagues expect to release a new hybrid watermarking method in 2021 that will enhance the durability and security of digital data. The DVT and SVD [ 5 ] use multilevel procedures for both the embedding and extraction of features, resulting in a much shorter processing time overall.…”

Section: Related Workmentioning

confidence: 99%

“…ree different wavelengths were used: 450 nm, 530 nm, and 630 nm. ree distinct wavelengths of light were used to capture the spectroscopic images: 450 nanometers, 530 nanometers, and 630 nanometers [4][5][6][7][8]. It has been suggested that using such pictures in the evaluation of the tympanic membrane (TM) may make it easier to determine the boundaries of the TM and segment it with high precision [4][5][6][7][8].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Control Techniques for the Detection of Biomedical Ear Infections

Abdulaal

Mehedi

Aljohani

et al. 2022

Computational Intelligence and Neuroscience

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The capacity to carry out one’s regular tasks is affected to varying degrees by hearing difficulties. Poorer understanding, slower learning, and an overall reduction in efficiency in academic endeavours are just a few of the negative impacts of hearing impairments on children’s performance, which may range from mild to severe. A significant factor in determining whether or not there will be a decrease in performance is the kind and source of impairment. Research has shown that the Artificial Neural Network technique is capable of modelling both linear and nonlinear solution surfaces in a trustworthy way, as demonstrated in previous studies. To improve the precision with which hearing impairment challenges are diagnosed, a neural network backpropagation approach has been developed with the purpose of fine-tuning the diagnostic process. In particular, it highlights the vital role performed by medical informatics in supporting doctors in the identification of diseases as well as the formulation of suitable choices via the use of data management and knowledge discovery. As part of the intelligent control method, it is proposed in this research to construct a Histogram Equalization (HE)-based Adaptive Center-Weighted Median (ACWM) filter, which is then used to segment/detect the OM in tympanic membrane images using different segmentation methods in order to minimise noise and improve the image quality. A tympanic membrane dataset, which is freely accessible, was used in all experiments.

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“…In order to verify the feasibility of the proposed algorithm, the improved model is tested on medical images and compared to hybrid level set model (HLS) [6], hybrid level set model based on LBF (HLS-LBF) [1] and three state-of-the-art segmentation methods: RAC [16], REI [28] and HFC [34]. The selected experimental environment is Windows10 operating system, GPU, Pentium(R) Dual-Core, 16G memory, MATLAB R2017a.…”

Section: Experiments and Analysismentioning

confidence: 99%

Active contour modal based on density-oriented BIRCH clustering method for medical image segmentation

Yin

Liu

et al. 2020

Multimed Tools Appl

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Currently, medical image segmentation has attracted more attention from researchers, which can assist in medical diagnosis. However, in the process of traditional medical image segmentation, it is sensitive to the initial contour and noise, which is difficult to deal with the weak edge image, complex iterative process. In this paper, we propose a new medical image segmentation method, which adopts density-oriented BIRCH (balanced iterative reducing and clustering using hierarchies) clustering method to modify active contour model and improve the robustness of noise. The BIRCH is a multi-stage clustering method using clustering feature tree. The improved model can effectively deal with the gray non-uniformity of real medical images. And we also introduce a new energy function in active contour model to make the contour curve approach to the edge, and finally stay at the edge of the image to complete the object segmentation. Experimental results show that this new model can overcome the influence of complex background on medical image segmentation and improve the speed and accuracy of medical segmentation results.

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An adaptable active contour model for medical image segmentation based on region and edge information

Cited by 12 publications

References 20 publications

Analysis of correlation between carcass and viscera for chicken eviscerating based on machine vision technology

Analysis of correlation between carcass and viscera for chicken eviscerating based on machine vision technology

Intelligent Control Techniques for the Detection of Biomedical Ear Infections

Active contour modal based on density-oriented BIRCH clustering method for medical image segmentation

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