Histogram of Oriented Gradient Based Plantar Pressure Image Feature Extraction and Classification Employing Fuzzy Support Vector Machine

Wang, Cunlei; Li, Zairan; Dey, Nilanjan; Li, Zhaochun; Ashour, Amira S.; Fong, Simon; Sherratt, R. Simon; Wu, Lijun; Shi, Fuqian

doi:10.1166/jmihi.2018.2310

Cited by 28 publications

(8 citation statements)

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“…e vertebrae are complex structures consisting of vertebral bodies, vertebral roots, and various load-bearing structures such as transverse processes, spinous processes, articular processes, collaterals, and papillae [2]. e vertebral body is broadly cylindrical with a slightly flattened dorsal surface, a convex head, and a concave tail.…”

Section: Introductionmentioning

confidence: 99%

The Clinical Value of CT Scans for the Early Diagnosis and Treatment of Spinal Fractures and Paraplegia

Jia

Zhu

Guo

2021

Journal of Healthcare Engineering

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The early diagnosis and treatment of spinal fractures and paraplegia by CT scan is investigated in depth and its clinical value is discussed in this paper. In this paper, a novel circulatory generation adversarial network, Spine-GAN, is proposed for the diagnosis of various spinal diseases. The algorithmic model can fully automate the segmentation and classification of multiple spinal structures, such as intervertebral discs, vertebrae, and neuroforamina, simultaneously to intelligently generate a complete clinical diagnosis. The innovation of this method is that Spine-GAN not only overcomes the high variability and complexity of spinal structures in MRI images but also preserves the subtle differences between normal and abnormal spinal structures and dynamically learns obscure but important spatial pathological relationships between adjacent structures of the spine, thus overcoming the limitations of small datasets. Spine-GAN enables accurate segmentation, radiological classification, and pathological correlation representation of the three spinal diseases. Specifically, Spine-GAN achieves a pixel accuracy of 96.2% with a specificity and sensitivity distribution of 89.1% and 86%, respectively. The DMML-Net and Spine-GAN algorithm models have important applications and research values in the clinical diagnosis of spinal diseases and MRI image processing, as well as in the intelligent generation of medical image diagnostic reports, which are of great importance for the study of fine-grained image classification of pathological images. It also has a positive impact on the development of the software.

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Section: Introductionmentioning

confidence: 99%

The Clinical Value of CT Scans for the Early Diagnosis and Treatment of Spinal Fractures and Paraplegia

Jia

Zhu

Guo

2021

Journal of Healthcare Engineering

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“…By collecting the dynamic pressure distribution of the sole, the biomechanical properties of shoes can be evaluated, such as cushioning performance, support strength, stability and so on [4] [5]. By previous works, the results indicated that foot zoning, pressure, press, risk assessment of diabetic foot ulcer, load change rate, foot length and width, foot contact area, foot angle, foot axis, and time track are used for stability analysis [6] [7]. It is an important direction of this research to use sole pressure imaging data set and segmentation results to control the bottom surface modeling, and then improve the wearing comfort of shoes [8].…”

Section: Introductionmentioning

confidence: 99%

An efficient local binary pattern based plantar pressure optical sensor image classification using convolutional neural networks

Wang

et al. 2019

Optik

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The objective of this study was to design and produce highly comfortable shoe products guided by a plantar pressure imaging data-set. Previous studies have focused on the geometric measurement on the size of the plantar, while in this research a plantar pressure optical imaging data-set based classification technology has been developed. In this paper, an improved local binary pattern (LBP) algorithm is used to extract texture-based features and recognize patterns from the data-set. A calculating model of plantar pressure imaging feature area is established subsequently. The data-set is classified by a neural network to guide the generation of various shoe-last surfaces. Firstly, the local binary mode is improved to adapt to the pressure imaging data-set, and the texture-based feature calculation is fully used to accurately generate the feature point set; hereafter, the plantar pressure imaging feature point set is then used to guide the design of last free surface forming. In the presented experiments of plantar imaging, multi-dimensional texture-based features and improved LBP features have been found by a convolution neural network (CNN), and compared with a 21-input-3-output two-layer perceptual neural network. Three feet types are investigated in the experiment, being flatfoot (F) referring to the lack of a normal arch, or arch collapse, Talipes Equinovarus (TE), being the front part of the foot is adduction, calcaneus varus, plantar flexion, or Achilles tendon contracture and Normal (N). This research has achieved an 82% accuracy rate with 10 hidden-layers CNN of rotation invariance LBP (RI-LBP) algorithm using 21 texture-based features by comparing other deep learning methods presented in the literature.

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“…is method is mainly based on the principle of two-dimensional Harris corner point detection with the addition of temporal information of video sequences so that spatial-temporal interest points can be detected [8]. Wang et al proposed a high-efficiency Dollar detector by using the Gaussian function and Gabor wavelet function to filter the video directly [9]. Although spatial-temporal interest points are a classical excellent local feature description algorithm, the complex and variable light and background, as well as camera motion, make the interesting point detection method less effective and difficult to be applied to real-life situations.…”

Section: Related Studiesmentioning

confidence: 99%

Automatic Construction and Extraction of Sports Moment Feature Variables Using Artificial Intelligence

Zhang

Zhang³

2021

Complexity

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In this paper, we study the automatic construction and extraction of feature variables of sports moments and construct the extraction of the specific variables by artificial intelligence. In this paper, support vector machines, which have better performance in the case of small samples, are selected as classifiers, and multiclass classifiers are constructed in a one-to-one manner to achieve the classification and recognition of human sports postures. The classifier for a single decomposed action is constructed to transform the automatic description problem of free gymnastic movements into a multilabel classification problem. With the increase in the depth of the feature extraction network, the experimental effect is enhanced; however, the two-dimensional convolutional neural network loses temporal information when extracting features, so the three-dimensional convolutional network is used in this paper for spatial-temporal feature extraction of the video. The extracted features are binary classified several times to achieve the goal of multilabel classification. To form a comparison experiment, the results of the classification are randomly combined into a sentence and compared with the results of the automatic description method to verify the effectiveness of the method. The multiclass classifier constructed in this paper is used for human motion pose classification and recognition tests, and the experimental results show that the human motion pose recognition algorithm based on multifeature fusion can effectively improve the recognition accuracy and perform well in practical applications.

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Histogram of Oriented Gradient Based Plantar Pressure Image Feature Extraction and Classification Employing Fuzzy Support Vector Machine

Cited by 28 publications

References 0 publications

The Clinical Value of CT Scans for the Early Diagnosis and Treatment of Spinal Fractures and Paraplegia

The Clinical Value of CT Scans for the Early Diagnosis and Treatment of Spinal Fractures and Paraplegia

An efficient local binary pattern based plantar pressure optical sensor image classification using convolutional neural networks

Automatic Construction and Extraction of Sports Moment Feature Variables Using Artificial Intelligence

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