Browsing by Author "Abesinghe, K.A.W.P."

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    Bone Crack Detector Based on X-Ray Using Fuzzy Logic and Neural Network
    (Uva Wellassa University of Sri Lanka, 2013) Abesinghe, K.A.W.P.
    Large numbers of X-Ray images are analyze by doctors in the hospitals day today. Based on that images doctors are going to predict what the problem in that bonds is such as bone crack, damage etc. These kinds of manual inspection of X-Ray consume a lot of time and the process itself is monotonous made during the inspection. The fractures of the bones will only occur during serious incident. This study discusses the development of a system which can differentiate the fractured bone from the non-fractured bone. The images of X-Ray differ in same bone also in different angle. The grown person the shape of the image of the bone is same from person to person. Also the shapes of the bones are same in healthy persons. Shapes of the bones are same in the grown person and baby. The continuous bones image is healthy one and fractions are classified first into three group which are Simple, Wedge and Complex. This study will focus on part of bone in the skeleton in our human body fracture detection. Basic Image processing Techniques, Fuzzy Logic and Neural Network were used to implement the system. System accepts the image and detects the crack of the long bone. This system is detecting the crack of femur bone and fingers. Image processing use to extract the bone from image, fuzzy logic use to detect crack of image form the features of bone and Neural Network verify that bone contains crack or not. System was implemented using C#.net programming language with the support of emguCV(C# wrapper for OpenCV) and Aforge.net. All those libraries and Graphical user interface were integrated and form a single complete system that performed the process. Separate the rough lines from the smooth lines to detect the crack of the image. This research showed that the Fuzzy logic with Digital Image Processing techniques, able to identify the crack of the bone using X Ray image with acceptable accuracy level.
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    Bone Crack Detector based on X-Ray using Fuzzy Logic and Neural Network
    (Uva Wellassa University of Sri Lanka, 2013) Abesinghe, K.A.W.P.; Wickramarathna, S.D.H.S.
    An x-ray (radiograph) is a noninvasive medical test (Tian Tai Pengn, 2002) that helps physicians diagnose and treat medical conditions. X-Ray images are used by doctors to detect the crack and abnormal conditions of the bones. Doctors are analyzing thousands of X-Ray images at hospital day by day. That activity is monotonous and also consuming lot of time. Bones contain much calcium, which due to its relatively high atomic number absorbs X-Rays efficiently. This reduces the amount ofX-rays reaching the detector in the shadow of the bones, making them clearly visible on the radiograph. A recognition system has three parts; Image Processing and feature extraction, Fuzzy Logic based identification and Neural Network based verification. The main objective of this study is Computer-assisted decision-making system to detect the crack of the bone in X-Ray image. Image Preprocessing is applying for enhance the features of the image. Edges based filters apply for enhance the edges because edges perform the vital role for detecting the crack of the bone image. Then system detects the edges of the image using canny edge detector. Background of the image is eliminated as the next step. System is finally detecting the edges that could be a crack or not. System could detect the actual crack and also some specific features of the bone. Those specific features of the bone are smooth lines and cracks are rough line. Using that specific characteristics system separate crack lines form some features of the bone. Finally abstract the features information for Fuzzyfication. Fuzzy Classifier contains fuzzy inference engine, input output variables. Input variables are information about the edges. Output variables are the detected crack. System is using two fuzzy sets and three fuzzy functions for each fuzzy set. One fuzzy set is Fuzzy Multiplication and other is Fuzzy Ratio. The fuzzy rules calculate the output and those outputs send to Neural Network for verification. Neural Network (Davis et al., 1999) takes the input from the Fuzzyfication and specific some parameters taken from image. Eight neurons for input layers, ten neurons for hidden layer and three neurons for output layer use for Neural Network. Supervise training uses for trains the Neural Network. Output obtains as pattern. Finally Neural Network verifies the fuzzy output and correctly says crack or not a crack.