Image segmentation based on equivalent three-dimensional entropy method and artificial fish swarm optimization algorithm

Lei, Xiangxiao; Ouyang, Haibin; Xu, Lijuan

doi:10.1117/1.oe.57.10.103106

Cited by 13 publications

(6 citation statements)

References 9 publications

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“…To achieve the goal of suppressing the components in a certain space, the spatial filter changes the image distribution frequency, thus enhancing the image contrast ( Sa-Ing et al, 2018 ). The high-pass filter suppresses low-frequency images during local processing of image pixels to make the image clearer, which effectively avoids local over-sharpening caused by image-guided filtering processing ( Lei et al, 2018 ).…”

Section: Methodsmentioning

confidence: 99%

Fast Speckle Noise Suppression Algorithm in Breast Ultrasound Image Using Three-Dimensional Deep Learning

Wang

Zhao

et al. 2022

Front. Physiol.

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The rapid development of ultrasound medical imaging technology has greatly broadened the scope of application of ultrasound, which has been widely used in the screening, diagnosis of breast diseases and so on. However, the presence of excessive speckle noise in breast ultrasound images can greatly reduce the image resolution and affect the observation and judgment of patients’ condition. Therefore, it is particularly important to investigate image speckle noise suppression. In the paper, we propose fast speckle noise suppression algorithm in breast ultrasound image using three-dimensional (3D) deep learning. Firstly, according to the gray value of the breast ultrasound image, the input breast ultrasound image contrast is enhanced using logarithmic and exponential transforms, and guided filter algorithm was used to enhance the details of glandular ultrasound image, and spatial high-pass filtering algorithm was used to suppress the excessive sharpening of breast ultrasound image to complete the pre-processing of breast ultrasound image and improve the image clarity; Secondly, the pre-processed breast ultrasound images were input into the 3D convolutional cloud neural network image speckle noise suppression model; Finally, the edge sensitive terms were introduced into the 3D convolutional cloud neural network to suppress the speckle noise of breast ultrasound images while retaining image edge information. The experiments demonstrate that the mean square error and false recognition rate all reduced to below 1.2% at the 100th iteration of training, and the 3D convolutional cloud neural network is well trained, and the signal-to-noise ratio of ultrasound image speckle noise suppression is greater than 60 dB, the peak signal-to-noise ratio is greater than 65 dB, the edge preservation index value exceeds the experimental threshold of 0.45, the speckle noise suppression time is low, the edge information is well preserved, and the image details are clearly visible. The speckle noise suppression time is low, the edge information is well preserved, and the image details are clearly visible, which can be applied to the field of breast ultrasound diagnosis.

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Section: Methodsmentioning

confidence: 99%

Fast Speckle Noise Suppression Algorithm in Breast Ultrasound Image Using Three-Dimensional Deep Learning

Wang

Zhao

et al. 2022

Front. Physiol.

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“…According to the guiding ideology of the SOFPDCHE and the related literature [ 31 , 32 , 33 , 34 ], we eventually concluded five important indexes, i.e., the main policies, the medical resources, the natural resources, the economic support, and the personnel training and employment encouragement. Meanwhile, we adopted the entropy method [ 35 , 36 , 37 ] to measure the weight values of these five indexes. The specific calculation processes of the index weight are shown as follows:

…”

Section: The Healthcare–nursing Information Collaboration Network (Hn...mentioning

confidence: 99%

Promoting the Integration of Elderly Healthcare and Elderly Nursing: Evidence from the Chinese Government

Hao

Yin

2022

IJERPH

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The increase of the aging population in China and the rise of the concept of healthy aging have accelerated the transformation and upgrading of the traditional elderly nursing pattern. Nevertheless, there is a critical limitation existing in the current situation of China’s elderly care, i.e., the medical institutions do not support elderly nursing and the elderly nursing institutions do not facilitate access to medical care. To eliminate the adverse impact of this issue, twelve ministries and commissions of the Chinese government have jointly issued a document, i.e., the Several Opinions on Further Promoting the Development of Combining the Healthcare with the Elderly care (SOFPDCHE), to provide guidance from the government level for further promoting the integration of elderly healthcare and elderly nursing. Under this background, this paper constructs a healthcare–nursing information collaboration network (HnICN) based on the SOFPDCHE, proposing three novel strategies to explore the different roles and collaboration relationships of relevant government departments and public organizations in this integration process, i.e., the node identification strategy (NIS), the local adjacency subgroup strategy (LASS), and the information collaboration effect measurement strategy (ICEMS). Furthermore, this paper retrieves 484 valid policy documents related to “the integration of elderly healthcare and elderly nursing” as data samples on the official websites of 12 sponsored ministries and commissions, and finally confirms 22 government departments and public organizations as the network nodes based on these obtained documents, such as the National Health Commission of the People’s Republic of China (NHC), the Ministry of Industry and Information Technology of the People’s Republic of China (MIIT), and the National Working Commission on Aging (NWCA). In terms of the collaboration effect, the results of all node-pairs in the HnICN are significantly different, where the collaboration effect between the NHC and MIIT is best and that between the NATCM and MIIT is second best, which are 84.572% and 20.275%, respectively. This study provides the quantifiable results of the information collaboration degree between different government agencies and forms the optimization scheme for the current collaboration status based on these results, which play a positive role in integrating elderly healthcare and elderly nursing and eventually achieving healthy aging.

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“… Chen, Sun & Shang (2017) proposed an automatic fish classification system based on deep learning, Akgül, Çalik & Töreyın (2020) proposed fish detection in turbid underwater, Li, Tang & Gao (2017) proposed a deep and lightweight network for detecting fish, Wang et al (2020) proposed a detection of abnormal behaviors of underwater fish using artificial intelligence techniques, Alshdaifat, Talib & Osman (2020) proposed a deep learning framework for segmentation of fish with underwater videos, Knausgård et al (2022) proposed a method for detecting and classificating temperate fish, Parida (2019) proposed a hybrid transition region color image segmentation method based on dual transition region extraction for fish image segmentation applications. Lei, Ouyang & Xu (2018) proposed an image segmentation method based on equivalent 3D entropy and artificial fish population optimization algorithm, which is more efficient than the traditional 3D entropy method and the equivalent 3D entropy method. Raza & Song (2020) proposed a quick and accurate method for fish detection based on the improved YOLO-v3 model and migration learning.…”

Section: Introductionmentioning

confidence: 99%

A multitask model for realtime fish detection and segmentation based on YOLOv5

Liu¹,

Gong²,

Li³

et al. 2023

PeerJ Computer Science

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The accuracy of fish farming and real-time monitoring are essential to the development of “intelligent” fish farming. Although the existing instance segmentation networks (such as Maskrcnn) can detect and segment the fish, most of them are not effective in real-time monitoring. In order to improve the accuracy of fish image segmentation and promote the accurate and intelligent development of fish farming industry, this article uses YOLOv5 as the backbone network and object detection branch, combined with semantic segmentation head for real-time fish detection and segmentation. The experiments show that the object detection precision can reach 95.4% and the semantic segmentation accuracy can reach 98.5% with the algorithm structure proposed in this article, based on the golden crucian carp dataset, and 116.6 FPS can be achieved on RTX3060. On the publicly available dataset PASCAL VOC 2007, the object detection precision is 73.8%, the semantic segmentation accuracy is 84.3%, and the speed is up to 120 FPS on RTX3060.

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Image segmentation based on equivalent three-dimensional entropy method and artificial fish swarm optimization algorithm

Cited by 13 publications

References 9 publications

Fast Speckle Noise Suppression Algorithm in Breast Ultrasound Image Using Three-Dimensional Deep Learning

Fast Speckle Noise Suppression Algorithm in Breast Ultrasound Image Using Three-Dimensional Deep Learning

Promoting the Integration of Elderly Healthcare and Elderly Nursing: Evidence from the Chinese Government

A multitask model for realtime fish detection and segmentation based on YOLOv5

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