Research on image segmentation method using a structure-preserving region model-based MRF

Cheng-hua, Fan; Wang, Qunjing

doi:10.1007/s10586-018-2592-2

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…These methods are based on the observation that the global representation of the medical image can be estimated from its local physical structures based on conditional probability distribution function. Such popular principle is called Markov random field (MRF) [ 40 , 41 ].…”

Section: Recent Workmentioning

confidence: 99%

Towards to Optimal Wavelet Denoising Scheme—A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing

Stanke

Kubíček

Vilímek

et al. 2020

Sensors

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Wavelet transformation is one of the most frequent procedures for data denoising, smoothing, decomposition, features extraction, and further related tasks. In order to perform such tasks, we need to select appropriate wavelet settings, including particular wavelet, decomposition level and other parameters, which form the wavelet transformation outputs. Selection of such parameters is a challenging area due to absence of versatile recommendation tools for suitable wavelet settings. In this paper, we propose a versatile recommendation system for prediction of suitable wavelet selection for data smoothing. The proposed system is aimed to generate spatial response matrix for selected wavelets and the decomposition levels. Such response enables the mapping of selected evaluation parameters, determining the efficacy of wavelet settings. The proposed system also enables tracking the dynamical noise influence in the context of Wavelet efficacy by using volumetric response. We provide testing on computed tomography (CT) and magnetic resonance (MR) image data and EMG signals mostly of musculoskeletal system to objectivise system usability for clinical data processing. The experimental testing is done by using evaluation parameters such is MSE (Mean Squared Error), ED (Euclidean distance) and Corr (Correlation index). We also provide the statistical analysis of the results based on Mann-Whitney test, which points out on statistically significant differences for individual Wavelets for the data corrupted with Salt and Pepper and Gaussian noise.

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Section: Recent Workmentioning

confidence: 99%

Towards to Optimal Wavelet Denoising Scheme—A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing

Stanke

Kubíček

Vilímek

et al. 2020

Sensors

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“…Hence, image-based detection methods have become research hotspots of insect pest control in the past few years [11]. Generally speaking, there are a lot of traditional image detection methods suitable for insect pest detection, such as the detection methods based on the threshold [12], edge [13], region test [14] and graph theory [15]. However, these traditional detection methods are appropriate for a small number of samples or small scope of detection, and the detection accuracies are not very stable [16].…”

Section: Introductionmentioning

confidence: 99%

Recommending Advanced Deep Learning Models for Efficient Insect Pest Detection

Zhu

et al. 2022

Agriculture

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Insect pest management is one of the main ways to improve the crop yield and quality in agriculture and it can accurately and timely detect insect pests, which is of great significance to agricultural production. In the past, most insect pest detection tasks relied on the experience of agricutural experts, which is time-consuming, laborious and subjective. In rencent years, various intelligent methods have emerged for detection. This paper employs three frontier Deep Convolutional Neural Network (DCNN) models—Faster-RCNN, Mask-RCNN and Yolov5, for efficient insect pest detection. In addition, we made two coco datasets by ourselves on the basis of Baidu AI insect detection dataset and IP102 dataset, and compared these three frontier deep learning models on the two coco datasets. In terms of Baidu AI insect detection dataset whose background is simple, the experimental results strongly recommend Yolov5 for the insect pest detection, because its accuracy reaches above 99% while Faster-RCNN’s and Mask-RCNN’s reach above 98%. Meanwhile, Yolov5 has the faster computational speed than Faster-RCNN and Mask-RCNN. Comparatively speaking, with regard to the IP102 dataset whose background is complex and categories are abundant, Faster-RCNN and Mask-RCNN have the higher accuracy, reaching 99%, than Yolov5 whose accuracy is about 97%.

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Research on blood cell image segmentation technology based on neural network method

Tian

2021

2021 International Conference on Networking, Communications and Information Technology (NetCIT)

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Research on image segmentation method using a structure-preserving region model-based MRF

Cited by 3 publications

References 9 publications

Towards to Optimal Wavelet Denoising Scheme—A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing

Towards to Optimal Wavelet Denoising Scheme—A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing

Recommending Advanced Deep Learning Models for Efficient Insect Pest Detection

Research on blood cell image segmentation technology based on neural network method

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