Enhanced Computer Aided Bone Fracture Detection Employing X-Ray Images by Harris Corner Technique

2020 ◽  
Author(s):  
Cmak Zeelan Basha ◽  
M Ravi Kishore Reddy ◽  
K Hemanth Sai Nikhil ◽  
P S M Venkatesh ◽  
A V Asish
Keyword(s):  
Bone Fracture ◽  
Fracture Detection ◽  
X Ray ◽  
Harris Corner ◽  
Computer Aided

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

scholarly journals Leg Bone Fracture Segmentation and Detection using Advanced Morphological Techniques

2019 ◽  
Vol 8 (2S3) ◽  
pp. 1246-1249 ◽  
Keyword(s):  
Image Processing ◽  
Bone Fracture ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Bone Area ◽  
Fracture Detection ◽  
Statistical Parameters ◽  
Accurate Identification ◽  
X Ray ◽  
Vehicle Accidents

The bone fracture is the most common problem and is likely to occur due to traumatic incidents like vehicle accidents, sporting injuries or due to conditions like osteoporosis, cancer related to bones. Fracture cannot be viewed by naked eye and so X-ray, CT, ultrasound, MRI images are used to detect it. These images cannot be diagnosed directly and henceforth image processing plays a very important role in fracture detection. This paper presents an image processing technique that uses Laplacian method of edge detection for accurate identification of fractured bone area from the X-ray/CT images. From the fractured bone area several parameters like mean, standard deviation are calculated in order to analyze the accuracy and sensitivity of the used technique. NIVISION assistant software is used and the statistical parameters are calculated.

scholarly journals An Audit of Computer Aided Fracture Detection System

2019 ◽  
Vol 9 (2) ◽  
pp. 868-872
Keyword(s):  
Medical Imaging ◽  
Detection System ◽  
Correct Diagnosis ◽  
Region Of Interest ◽  
Automatic Identification ◽  
X Rays ◽  
Fracture Detection ◽  
X Ray ◽  
Course Of Action ◽  
Computer Aided

X-Ray images are the most widely recognized methods for medical imaging accessibility for individuals during the wounds and mishaps. X-rays are most frequent and the oldest form of medical imaging. Yet, the minute fracture identification using the X-Ray image is beyond the realm of imagination,because of the complication of bone organisation and the dissimilarity in visual attributes of fracture upon their location. This is the reason why it it is hard to detect the fractures and furthermore decide the seriousness of the damage. The major challenges of X-Ray imaging are the presence of noise, intensity ambiguity, and overlapping tissues. This creates a hurdle in correct diagnosis and delays treatment. The various rates require the human services experts to analyze countless x-ray images. computerized detection of fractures in X-Ray images can be a huge commitment for helping the doctors in settling on quicker and increasingly precise diagnostic decisions and speeds up the plan for the treatment. This research compares the existing fracture detection techniques.From various fractures, programmed identification is viewed as challenging since they are unique and variable in presentation and their results are quite un predictable.The major challenges for computer-aided fracture detection can be accurate segmentation process, automatic identification of the region of interest (bone fracture), evaluation and suggestive course of action.

scholarly journals Computer Based Bone Breakage Detection using Machine Learning Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 3695-3698
Keyword(s):  
Machine Learning ◽  
Bone Fracture ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Fracture Detection ◽  
X Ray ◽  
Learning Techniques ◽  
Computer Based

X-Ray is one of the most commonly used medium to extract the images of any bone in the body.Fracture of a bone is most common in recent days due to accidents or any means.In order to detect whether there is a fracture or not the orthopaedics suggest for x-ray.In many places due to more patients there might be a delay of doctor consult which may leads to the increase in the severity of problem.In order to avoid this we have proposed an automatic bone fracture detection system where a system is trained about the fractures and further used to detect the fractures in a bone in the x-ray images.ANN,PNN.BPNN are the classifiers used for bone fracture detection where BPNN has given more prominent results compared to ANN and PNN with an accuracy of 82%.

Sign in / Sign up

Export Citation Format

Share Document