Bone Fracture Detection from X-Ray Image of Human Fingers Using Image Processing

Bharodiya, Anil K.; Gonsai, Atul M.

doi:10.1007/978-981-13-2285-3_6

Cited by 6 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A doctor manually checks an X-ray image after it has been retrieved. [ 8 ] used an X-ray image to detect several orthopedic and radiology-based mussel diseases in this study. Average, middle, and Wiener filters are employed to remove noise in the preprocessing stage; edge detection recognition is suggested for image capture.…”

Section: Related Workmentioning

confidence: 99%

Construction of a Computer-Aided Analysis System for Orthopedic Diseases Based on High-Frequency Ultrasound Images

Xiu

Rong

Zhang

2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

The area of medical diagnosis has been transformed by computer-aided diagnosis (CAD). With the advancement of technology and the widespread availability of medical data, CAD has gotten a lot of attention, and numerous methods for predicting different pathological diseases have been created. Ultrasound (US) is the safest clinical imaging method; therefore, it is widely utilized in medical and healthcare settings with computer-aided systems. However, owing to patient movement and equipment constraints, certain artefacts make identification of these US pictures challenging. To enhance the quality of pictures for classification and segmentation, certain preprocessing techniques are required. Hence, we proposed a three-stage image segmentation method using U-Net and Iterative Random Forest Classifier (IRFC) to detect orthopedic diseases in ultrasound images efficiently. Initially, the input dataset is preprocessed using Enhanced Wiener Filter for image denoising and image enhancement. Then, the proposed segmentation method is applied. Feature extraction is performed by transform-based analysis. Finally, obtained features are reduced to optimal subset using Principal Component Analysis (PCA). The classification is done using the proposed Iterative Random Forest Classifier. The proposed method is compared with the conventional performance measures like accuracy, specificity, sensitivity, and dice score. The proposed method is proved to be efficient for detecting orthopedic diseases in ultrasound images than the conventional methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Construction of a Computer-Aided Analysis System for Orthopedic Diseases Based on High-Frequency Ultrasound Images

Xiu

Rong

Zhang

2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…Hence the involvement of machine learning and artificial intelligence would have a great impact on the outcomes and the fracture can be accurately diagnosed. X-ray imaging technique is often used by orthopedic doctors for fracture detection [5] .…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for the Identification of Bone Deformities

et al. 2023

IJIRMPS

View full text Add to dashboard Cite

The success of machine learning algorithms in medical imaging has boosted the demand for models that have been artificially trained to function more rapidly and effectively in the medical profession. In this paper, a method for identifying bone fractures using machine learning algorithms is presented, which can help to lighten the workload of orthopedics. Instead of spending hours in radiology departments, the substantial application of machine learning in this era of huge medical data will make it possible to obtain information from the available X-ray images. The imaging techniques described in this study can quickly determine whether a bone fracture has occurred in a human body after an X-ray has been obtained.

show abstract

“…Digital Image Correlation (DIC) is a 3D full-field, non-contact and optical system to quantify and research about shape, distortion, vibration and strain on practically any material [8]. The system can be utilized with numerous tests including malleable, torsion, bowing and consolidated stacking for both static and dynamic applications [9][10][11][12][13]. This technique can be applied from little (miniaturized scale) to (full scale) enormous testing regions -and furthermore the outcomes are promptly practically identical with FEA results or by the strain checks.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey on Crack detection of Bone using various techniques

S¹,

Akshaya²,

Kaviya³

et al. 2020

Bull. Sci. Res.

View full text Add to dashboard Cite

Digital image processing plays a key role in manipulation of image and extracting the maximum amount of data from image with help of various algorithm. Digital image correlation algorithm determines the displacement and deformation of pattern across several images. Creating innovation are developing every day in various fields, particularly in restoration condition. Notwithstanding, still some old strategies are very famous. X-ray or CT images are one among the system for identification of bone cracks. during this article, we offer a comprehensive overview of various algorithm and techniques of displacement measurement generally and crack detection especially using digital image processing. we've been successful in highlighting each and each key feature and aspect of crack detection in bone which can take the add this domain further

show abstract

Bone Fracture Detection from X-Ray Image of Human Fingers Using Image Processing

Cited by 6 publications

References 10 publications

Construction of a Computer-Aided Analysis System for Orthopedic Diseases Based on High-Frequency Ultrasound Images

Construction of a Computer-Aided Analysis System for Orthopedic Diseases Based on High-Frequency Ultrasound Images

Machine Learning for the Identification of Bone Deformities

A Comprehensive Survey on Crack detection of Bone using various techniques

Contact Info

Product

Resources

About