Ensemble Systems for Automatic Fracture Detection

Mahendran, Siddharth; Baboo, S. Santhosh

doi:10.7763/ijet.2012.v4.310

Cited by 15 publications

(7 citation statements)

References 14 publications

(9 reference statements)

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“…Ms.ShivaniZalte, et al [5] connected picture preparing methods to discover break in a bone.The creators look at the changed edge finders and depict the points of advantages and drawbacks of these detectors. It is that the Canny strategy create similarly great edge with the smooth continuous pixels and thin edge.…”

Section: Related Workmentioning

confidence: 99%

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

Shifani¹,

Ramkumar²,

Clemencia³

et al. 2019

IJITEE

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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

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Section: Related Workmentioning

confidence: 99%

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

Shifani¹,

Ramkumar²,

Clemencia³

et al. 2019

IJITEE

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“…The first work is to address the problem and distinguishing features are extracted after preprocessing. Different classification algorithms were used to detect the existence of fracture [4]. Mahendran.…”

Section: Introductionmentioning

confidence: 99%

Analysis on Leg Bone Fracture Detection and Classification Using X-ray Images

Myint¹

2018

MLR

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Nowadays, computer aided diagnosis (CAD) system become popular because it improves the interpretation of the medical images compared to the early diagnosis of the various diseases for the doctors and the medical expert specialists. Similarly, bone fracture is a common problem due to pressure, accident and osteoporosis. Moreover, bone is rigid portion and supports the whole body. Therefore, the bone fracture is taken account of the important problem in recent year. Bone fracture detection using computer vision is getting more and more important in CAD system because it can help to reduce workload of the doctor by screening out the easy case. In this paper, lower leg bone (Tibia) fracture types recognition is developed using various image processing techniques. The purpose of this work is to detect fracture or non-fracture and classify type of fracture of the lower leg bone (tibia) in x-ray image. The tibia bone fracture detection system is developed with three main steps. They are preprocessing, feature extraction and classification to classify types of fracture and locate fracture locations. In preprocessing, Unshrap Masking (USM), which is the sharpening technique, is applied to enhance the image and highlight the edges in the image. The sharpened image is then processed by Harris corner detection algorithm to extract corner feature points for feature extraction. And then, two classification approaches are chosen to detect fracture or non-fracture and classify fracture types. For fracture or not classification, simple Decision Tree (DT) is employed and K-Nearest Neighbour (KNN) is used for classifying fracture types. In this work, Normal, Transverse, Oblique and Comminute are defined as the four fracture types. Moreover, fracture locations are pointed out by the produced Harris corner points. Finally, the outputs of the system are evaluated by two performance assessment methods. The first one is performance evaluation for fracture or non-fracture (normal) conditions using four possible outcomes such as TP, TN, FP and FN. The second one is to analysis for accuracy of each fracture type within error conditions using the Kappa assessment method. The programming software used to implement the system is MATLAB with wide range of image processing tools environment. The system produces 82% accuracy for classification fracture types.

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“…The hierarchy of SVM's performs better than an individual SVM solving the whole problem. [1] [2] Mahendran presents ensemble system for the purpose of detecting fractures in X-ray images. Total 11 texture features used are GLCM features namely Contrast, Homogeneity, Energy, Entropy, Mean, Variance, Standard Deviation.…”

Section: Hierarchy Of Svm (H-svm)mentioning

confidence: 99%

“…Until recently, X-Ray images were maintained as hard film copy. [1] Now-a-days, X-Ray machines produce extremely high-quality images for radiologists to interpret. X-Ray image classification is an area that has attracted researchers for the past few decades.…”

Section: Introductionmentioning

confidence: 99%

“…X-Ray image classification is an area that has attracted researchers for the past few decades. [1] X-Ray is one the oldest and frequently used devices, as they are non-invasive, painless and economical. [2] A bone x-ray makes images of any bone in the body, including the hand, wrist, arm, elbow, shoulder, foot, ankle, leg (shin), knee, thigh, hip, pelvis or spine.…”

Section: Introductionmentioning

confidence: 99%

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Survey on Classifiers: Implemented for Fracture Detection

Bhalke¹,

Wyawahare²

2014

IJCA

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This paper deals with classifiers that have been used for bone fracture detection in the past few years. The authors have made attempts to survey papers from different modalities. This guided us to study different classifiers that have been applied to images obtained from X-Ray. The study presented in simple manner so that it will be easy to understand and will help reader to study classifiers. General TermsMedical imaging, machine vision, biomedical image analysis

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Ensemble Systems for Automatic Fracture Detection

Cited by 15 publications

References 14 publications

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

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

Analysis on Leg Bone Fracture Detection and Classification Using X-ray Images

Survey on Classifiers: Implemented for Fracture Detection

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