scholarly journals An attention‐based cascade R‐CNN model for sternum fracture detection in X‐ray images

2022 ◽  
Author(s):  
Yang Jia ◽  
Haijuan Wang ◽  
Weiguang Chen ◽  
Yagang Wang ◽  
Bin Yang
Keyword(s):  
Fracture Detection ◽  
X Ray ◽  
Sternum Fracture

scholarly journals Detection of Fracture Bones in X-ray Images Categorization

2020 ◽  
pp. 1-11
Author(s):  
Adigun Oyeranmi ◽  
Babatunde Ronke ◽  
Rufai Mohammed ◽  
Aigbokhan Edwin
Keyword(s):  
Feature Extraction ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
False Negative ◽  
Support Vector ◽  
Kappa Statistics ◽  
Fracture Detection ◽  
True Negative ◽  
Research Attention ◽  
X Ray

Fractured bone detection and categorization is currently receiving research attention in computer aided diagnosis system because of the ease it has brought to doctors in classification and interpretation of X-ray images.  The choice of an efficient algorithm or combination of algorithms is paramount to accurately detect and categorize fractures in X-ray images, which is the first stage of diagnosis in treatment and correction of damaged bones for patients. This is what this research seeks to address. The research design involves data collection, preprocessing, segmentation, feature extraction, classification and evaluation of the proposed method. The sample dataset were x-ray images collected from the Department of Radiology, National Orthopedic Hospital, Igbobi-Lagos, Nigeria as well as Open Access Medical Image Repositories. The image preprocessing involves the conversion of images in RGB format to grayscale, sharpening and smoothing using Unsharp Masking Tool.  The segmentation of the preprocessed image was carried out by adopting the Entropy method in the first stage and Canny edge method in the second stage while feature extraction was performed using Hough Transformation. Detection and classification of fracture image employed a combination of two algorithms;  K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) for detecting fracture locations based on four classification types: (normal, comminute, oblique and transverse).Two performance assessment methods were employed to evaluate the developed system. The first evaluation was based on confusion matrix which evaluates fracture and non-fracture on the basis of TP (True Positive), TN (True negative), FP (False Positive) and FN (False Negative). The second appraisal was based on Kappa Statistics which evaluates the type of fracture by determining the accuracy of the categorized fracture bone type. The result of first assessment for fracture detection shows that 26 out of 40 preprocessed images were fractured, resulting to the following three values of performance metrics: accuracy value of 90%, sensitivity of 87% and specificity of 100%. The Kappa coefficient error assessment produced accuracy of 83% during classification. The proposed method can find suitable use in categorization of fracture types on different bone images based on the results obtained from the experiment.

scholarly journals Identification of Bone Fragmentation in X-Ray Images using Contour Detection Algorithm

2019 ◽  
Vol 8 (9) ◽  
pp. 2121-2124
Keyword(s):  
Edge Detection ◽  
Bone Fracture ◽  
Detection Algorithm ◽  
Contour Detection ◽  
Crack Identification ◽  
Fracture Detection ◽  
Canny Edge Detection ◽  
X Ray ◽  
Base Paper ◽  
Canny Edge

The crack can occur in any bone ofour body. Broken bone is a bone condition that endured a breakdown of bone trustworthiness. The Fracture can't recognize effortlessly by the bare eye, so it is found in the x-beam images. The motivation behind this task is to find the precise territory where the bone fracture happens utilizing X-Ray Bone Fracture Detection by Canny Edge Detection Method. Shrewd Edge Detection technique is an ideal edge identification calculation on deciding the finish of a line with alterable limit and less error rate. The reproduction results have indicated how canny edge detection can help decide area of breaks in x-beam images. In the base paper, the cracked bit is chosen physically to defeat this downside, the proposed technique identify the bone fracture consequently and furthermore it quantifies the parameter like length of the crack, profundity of the fracture and the situation of the crack as for even and vertical pivot. The outcome demonstrates that the proposed technique for crack identification is better. The outcomes demonstrate that calculation is 91% exact and effective

Combining classifiers for bone fracture detection in X-ray images

2005 ◽  
Author(s):  
Vineta Lai Fun Lum ◽  
Wee Kheng Leow ◽  
Ying Chen ◽  
Tet Sen Howe ◽  
Meng Ai Png
Keyword(s):  
Bone Fracture ◽  
Fracture Detection ◽  
Combining Classifiers ◽  
X Ray

Hairline fracture detection using Talbot-Lau x-ray imaging

2018 ◽  
Author(s):  
Christian Hauke ◽  
Katharina Hellbach ◽  
Martino Leghissa ◽  
Felix G. Meinel ◽  
Thomas Mertelmeier ◽  
...  
Keyword(s):  
Fracture Detection ◽  
X Ray ◽  
X Ray Imaging

scholarly journals Obturator ring fracture detection using anteroposterior view x-ray

2019 ◽  
Vol 160 ◽  
pp. 369-376
Author(s):  
Pascal Damien ◽  
Ralph Bou Nader ◽  
Charles Yaacoub ◽  
Jean-Claude Lahoud
Keyword(s):  
Fracture Detection ◽  
Ring Fracture ◽  
X Ray ◽  
Anteroposterior View

A survey of fracture detection techniques in bone X-ray images

2020 ◽  
Vol 53 (6) ◽  
pp. 4475-4517 ◽  
Author(s):  
Deepa Joshi ◽  
Thipendra P. Singh
Keyword(s):  
Fracture Detection ◽  
X Ray ◽  
Detection Techniques
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