Medical image segmentation using deep learning with feature enhancement

Huang, Shaoqiong; Huang, Mengxing; Chen, Jing; Bhatti, Uzair Aslam

doi:10.1049/iet-ipr.2019.0772

Cited by 28 publications

(13 citation statements)

References 27 publications

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“…Likewise, the discrete form can be approximated using Only the discretized differential operator can be used in digital image processing [18].…”

Section: Introducing Fuzzy Entropy To Enhance Medical Image Detailsmentioning

confidence: 99%

Design of Medical Image Detail Enhancement Algorithm for Ankle Joint Talar Osteochondral Injury

Yun-dong

2021

Journal of Healthcare Engineering

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Medical imaging modalities, such as magnetic resonance imaging (MRI) and computerized tomography (CT), have allowed medical researchers and clinicians to examine the structural and functional features of the human body, thereby assisting the clinical diagnosis. However, due to the highly controlled imaging environment, the imaging process often creates noise, which seriously affects the analysis of the medical images. In this study, a medical imaging enhancement algorithm is presented for ankle joint talar osteochondral injury. The gradient operator is used to transform the image into the gradient domain, and fuzzy entropy is employed to replace the gradient to determine the diffusion coefficient of the gradient field. The differential operator is used to discretize the image, and a partial differential enhancement model is constructed to achieve image detail enhancement. Three objective evaluation indexes, namely, signal-to-noise ratio (SNR), information entropy (IE), and edge protection index (EPI), were employed to evaluate the image enhancement capability of the proposed algorithm. Experimental results show that the algorithm can better suppress noise while enhancing image details. Compared with the original image, the histogram of the transformed image is more uniform and flat and the gray level is clearer.

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“…Likewise, the discrete form can be approximated using Only the discretized differential operator can be used in digital image processing [18].…”

Section: Introducing Fuzzy Entropy To Enhance Medical Image Detailsmentioning

confidence: 99%

Design of Medical Image Detail Enhancement Algorithm for Ankle Joint Talar Osteochondral Injury

Yun-dong

2021

Journal of Healthcare Engineering

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“…Model-based methods recover image directly by modelling the degradation of image process. Recently, convolutional neural networks (CNN) and generative adversarial networks (GAN) based models have been widely concerned for image enhancement tasks [25][26][27]. Due to the excellent ability of feature extraction, CNN-based models have achieved impressive progress in image fusion [28,29], detection [30], and classification [31] tasks.…”

Section: Motivation and Preliminarymentioning

confidence: 99%

Adaptive colour restoration and detail retention for image enhancement

Tao

2021

IET Image Processing

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Computer vision-based crowd understanding and analysis technology has been widely used in public safety due to the rapid growth of population and the frequent occurrence of various accidents. Improving imaging quality is the key to improve the performance of crowd analysis, density estimation, target recognition, segmentation, and detection in computer vision tasks. Due to the complex imaging environment such as fog and low illumination, some images taken in outdoor environment often have the problems of colour distortion, lack of details, and the poor imaging quality, which affect the subsequent visual tasks. To improve the imaging quality and visual effect, an adaptive colour restoration and detail retention-based method is proposed for image enhancement. First, to overcome the problem of colour distortion caused by low illumination and fog, a multi-channel fusion based adaptive image colour restoration method is proposed. To make the enhancement result more consistent with human observation, the detail retention-based method is applied to enhance the details. Experimental results demonstrate that the authors' results are effective and outperform the compared methods both in visual and objective evaluations. INTRODUCTIONPublic safety problems have widely attracted attentions due to the frequent occurrence of various safety accidents in the world. Crowd density analysis, detection and intelligent transportation are popular research topics in the field of public safety. Particularly, with the rapid growth of population, crowd counting and analysis have been widely used in video surveillance, traffic control and sports events. In recent years, the development of artificial intelligence technology enables computer vision-based crowd understanding and analysis to develop rapidly. The performance of computer vision-based technology depends on the imaging quality. Therefore, image enhancement is an important technology in computer vision tasks, such as person counting, crowd analysis, re-identification, target segmentation and recognition [1][2][3][4], which is widely used in public safety, military and other visual applications.With the rapid development of deep learning and computer vision technology, many image vision-based intelligent tasks are realized, such as intelligent security and intelligent transportation. Based on the techniques of pattern recognition and machine learning, the person re-identification, vehicle detectionThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Reference [26] Applying deep learning and feature enhancement technique to segmenting the affected region from medical images. This work aims to reduce the difficulties in edge and texture related information selection process.…”

Section: Related Workmentioning

confidence: 99%

“…( 6), the medical image's pixels are examined effectively. The ODL technique obtained results are compared with the existing researchers works such as a fully convolutional adversarial network (FCAN) [21], group normalized convolution network (GnCNNr) [23], Gabor network-based deep learning (AGnet) [26], dual-tree complex wavelet transforms with convolution neural networks (DWT-CNN) [29]. These methods are applied to the Medical Segmentation Decathlon dataset images, and the obtained PA values are illustrated in Table 5.…”

Section: Performance Metrics and Analysismentioning

confidence: 99%

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Adam Optimized Deep Learning Model for Segmenting ROI Region in Medical Imaging

Jaber

Khalil

Ali

2021

Proceedings of International Conference on Emerging Technologies and Intelligent Systems

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Medical image processing played a crucial role in healthcare sectors because it produces visible images of inner body structure and internal tissues. With medical imaging, the organs' functions are analyzed, and anomalies are detected effectively. The imaging process utilizes enhancement, analysis, and recognition procedures that maximize disease detection accuracy. However, the gathered medical images have noise while performing the medical research that affects the ROI region segmentation process. The improper identification of the ROI region causes to reduce disease detection accuracy and create complexity. Therefore, this paper uses the Adam optimization technique with deep learning approaches for examining the medical images. The deep learning (DL) approaches utilizing the multiple layers, expert-tuned parameters, and learning function to deriving the affected ROT region. The DL method automatically extracts the medical imaging features that are optimized according to the RMS prop and momentum-based learning parameters. The optimized parameter minimizes the complexity while deriving the disease affected region. This process ensures high region segmentation accuracy with minimum time. The process has detected the affected region with 99.27% of accuracy.

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Medical image segmentation using deep learning with feature enhancement

Cited by 28 publications

References 27 publications

Design of Medical Image Detail Enhancement Algorithm for Ankle Joint Talar Osteochondral Injury

Design of Medical Image Detail Enhancement Algorithm for Ankle Joint Talar Osteochondral Injury

Adaptive colour restoration and detail retention for image enhancement

Adam Optimized Deep Learning Model for Segmenting ROI Region in Medical Imaging

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