Project PPTProject PPTProject PPTProject PPT.pptx

thanks

1. “Automatic Fracture Detection in Bone Using MATLAB and Image Processing “ DILIP 1AR21EC400 ESHWAR 1AR21EC401 KOTRESH 1AR21EC402 MANOJ 1AR21EC403 NAVYA CR 1AR21EC404 PHILIP C 1AR21EC406 Mrs. REKHA S ASST. PROFESSOR ECE, AIEMS DEPT OF ELECTRONICS AND COMMUNICATIONS ENGINEERING PROJECT PHASE -2(18ECP83) 8 TH SEM FINAL REVIEW TEAM MEMBERS UNDER THE GUIDANCE OF

2. CONTENT ABSTRACT INTRODUCTION LITERATURE SURVEY METHODOLOGY BLOCK DIAGRAM EXISTING SYSTEM PROPOSED METHOD SOFTWARE ENVIORNMENT WORK DONE RESULT AND ANAYLSIS OUTPUT CONCLUSION REFERENCES

3. ABSTRACT The bone fracture is a common problem in human beings that occurs when high pressure is applied on bone. the accurate diagnosis of bone fracture is an important aspect in medical field. In this work SVM is implemented on the X-ray images for bone fracture analysis. The aim of this project is to develop an image processing based efficient for a quick and accurate classification of bone fractures based on the information gained from the X-ray images.

4. INTRODUCTION Bones are the solid organs in the human body protecting many important organs such as brain, heart, lungs and other internal organs. Bone fracture is a common problem in human beings. A fracture is break in a bone. Bone fractures can occur due to accident or any other case in which high pressure is applied on the bones. There are different types of bone fracture such as oblique, compound, spiral, greenstick and transverse. If the broken bone punctures the skin, it is called an open or compound fracture.

5. There are different types of medical imaging tools to detecting different types of abnormalities such as X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), ultrasound etc. X-rays and CT are most frequently used in fracture diagnosis because it is the fastest and easiest way for the doctors to study the injuries of bones and joints. Doctors usually uses x-ray images to determine whether a fracture exists, and the location of the fracture. The database is DICOM images. In modern hospitals, medical images are stored in the standard DICOM (Digital Imaging and Communications in Medicine) format which includes text into the images.

6. LITERATURE SURVEY SERIAL NO. NAME OF AUTHOR TITLE OF THE PAPER YEAR OF PUBLICATION CONTENT 1 Anu T C, Mallikarjunaswamy M.S , Rajesh Raman Detection of Bone Fracture using Image Processing Methods 2018 Image processing based efficient system for a quick and accurate classification of bone fractures based on the information gained from the x-ray 2 Hasan S. M. Al-Khaffaf1, Abdullah Z. Talib , Rosalina Abdul Salam Removing Salt-and-Pepper Noise from Binary Images of Engineering Drawings 2019 A noise removal algorithm that can remove noise while retaining fine graphical elements is presented in this paper. 3 ] B Harinath , Sitrarasu , T.J. Nagalakshmi Bone Fracture Detection System using Image Processing and Matlab 2019 Image segmentation algorithms, Image classification techniques, Image enhancement techniques, Edge Detection method, Fractured X-Ray Images, MATLAB.

7. SL.NO NAME OF AUTHOR TITLE OF PUBLICATION YEAR CONTENT 4 Asha Joy Jacob, Apala Chakrabarti , R Sivakumar Bone Fracture Detection Using Image Processing and Neural Networks 2020 The proposed method attempts to make use of the Speed UP Robust Feature (SURF) algorithm to extract features from computed tomography scans 5 Kang Cheol Kim, Hyun Cheol Cho , Tae Jun Jang , Jong Mun Choi b , Jin Keun Seo Automatic detection and segmentation of lumbar vertebrae from X-ray images for compression fracture evaluation 2020 In this paper The results show that the proposed method achieves accurate and robust identification of each lumbar vertebra and fine segmentation of individual vertebra. 6 Rinisha Bagaria , Sulochana Wadhwani , Arun Kumar Wadhwan A Wavelet Transform and Neural Network Based Segmentation & Classification System For Bone Fracture Detection 2021 This neural network has trained with fractured and non-fractured images then tested on various other X-ray images. An EBP-NN classification system gives the maximum accuracy. The developed system can detect fractured bone images accurately.

8. METHODOLOGY The bone fracture is a common problem in human beings occurs when high pressure is applied on bone or due to simple accident and bone cancer. Therefore the accurate diagnosis of bone fracture is important aspects in medical field. In this work X-ray images are used for bone fracture analysis. Images of the fractured bone are obtained from hospital and processing techniques like pre-processing, edge detection, segmentation, and feature extraction methods are adopted. The processed images will be further classified into fractured and non-fractured bone and find the accuracy of this methods. This project is fully employed MATLAB 2018 version as the programming tool for loading image, image processing and user interface development .

9. BLOCK DAIGRAM

10. PRE-PROCESSING In computer-aided diagnosis of the medical images, image processing tools are used for noise removal. In the first step, applying preprocessing techniques is to remove the noise from the image by using the median filter. It can be written as: f (x, y) = g (x, y) + n (x, y)Where f (x, y) is the noisy image, g (x, y) is the original image and n (x, y) is the noise present in the image. There are different types of noise present in the image are Gaussian noise, Salt and pepper noise etc. Salt and pepper is one of the common types of noise present in x-ray images.

11. This is generally caused by a failure in capture or transmission that is appearing in the image as light and black dots. It can be removed by applying different filter on the images. It preserves the edges while removing noise. The median filter is a nonlinear digital filtering technique, used to remove noise such as salt and pepper noise.

12. EDGE DETECTION Edge detection is an important operation in image processing, that reduce the number of pixels and save the structure of the image by determining the boundaries of objects in the image. Edge detection is the method of identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. The points at which image brightness changes sharply are typically organized into a set of curved line segments termed edges.

13. There are two general approaches to edge detection that are commonly used are: gradient and Laplacian. In this method we are checking for different type of edge detection method comparing the output an selecting best edge detector, we are using canny edge detector for further processing.

14. SEGMENTATION Segmentation is the process of dividing the given image into regions homogenous with respect to certain features as color, intensity etc. It is an essential step in image analysis and locates object & boundaries (lines, curves etc.) The K-means clustering technique is used in this work. The purpose of this algorithm is minimizing an objective function, which is absolute difference function. In this algorithm distance is squared or absolute difference between a pixel and cluster center is calculated. The difference is typically based on pixel intensity, color, texture and location.

15. FEATURE EXTRACTION Feature extraction is the main step in various image processing applications. There are different types of feature extraction method namely Fourier Transforms, Hough Transform, Walesh Transform. We are using Hough transform in our work. The Hough transform is  a feature extraction technique used in image analysis, computer vision, and digital image processing . The Hough transform (HT) can be used to detect lines, circles or other parametric curves.

16. CLASSIFICATION Classification is a step of data analysis to study a set of data and categorize them into a number of categories. Each category has its own characteristics and the data that belong to such category have the same properties of this category. Different types of classifier are such as decision tree (DT), SVM. In proposed method, we are using Support Vector Machine(SVM) Support Vector Machine(SVM) is  a supervised machine learning algorithm used for classification.

17. EXISTING SYSTEM For Preprocessing median filter is used as a nonlinear digital filtering technique, used to remove noise such as salt and pepper noise. In this method Sobel edge detector is used for edge detection. For segmentation the K-means clustering technique is used in this work. Feature extraction is the main step in various image processing applications. Gray-Level Co-occurrence Matrix (GLCM) is used for feature extraction and selection.

18. X-RAY An X-ray is a quick, painless test that produces images of the structures inside your body particularly your bones. X-ray beams pass through your body, and they are absorbed in different amounts depending on the density of the material they pass through. A radiologist typically views and interprets the results and sends a report to your doctor, who then explains the results to you

19. PROPOSED METHOD Here we proposed an automated techniques and methods to detect fracture. X-ray images are examined manually but it is time consuming, prone to errors and small airline fractures are difficult to examine. As X-ray images are more susceptible to noise for which pre processing techniques has been used so as to over come this disadvantage. The median filter a nonlinear digital filtering technique, is used to remove noise such as salt and pepper noise. For Edge detection comparing different types of method and using Canny Edge Detection. For segmentation we are using K-means clustering method System detects fracture based on the connected component. System displays bounding circle around the fracture.

20. SOFTWARE ENVIRONMENT MATLAB SOFTWARE Matlab ( MATrix LABoratory ) is a programming language and numeric computing environment developed by MathWorks. MATLAB allows matrix multiplication, plotting of functions and data, creation of user interfaces and interfacing with program written in other languages. It combines a desktop environment tuned for iterative analysis and design processes with a programming language that expresses matrix and array mathematics directly

21. RESULT Original Image Adding salt and pepper noise

22. ACSCE, Median Filter Wiener Filter

23. 23 Canny Edge detection image Sobels Edge detection image Robert Edge detection image Log Edge detection image

24. RESULT AND ANALYSIS Bone fracture detected

25. Fig : Normal no fracture detection

26. Fig : Airline fracture detected

27. CONCLUSION A computer based analysis techniques for the detection of Bone fracture using X-ray/CT images has been presented in this work. It starts from the preprocessing to remove the noise and edge detected by using canny, sobel , prewitt , log edge detector. The method has been tested on a set of images and results have been evaluated. The limitation of this method is, in X-ray images very difficult to find the area of fracture. A system for identifying and analysing bone fracture based on Feature extraction using Hough transform will be implemented for the current project work.

28. REFERENCES Richard Green, Jim Graham, Hugh Devlin, April 2011 Multiscale rigid registration to detect damage in micro-CT images of progressively loaded bones, Chicago, IEEE International Symposium on Biomedical imaging. J. C. He, W. K. Leow , and T. S. Howe. Hierarchical classifiers for detection of fractures in x-ray images. In Computer Analysis of Images and Patterns. Richard Green, Jim Graham, Hugh Devlin, April 2011 Multiscale rigid registration to detect damage in micro-CT images of progressively loaded bones, Chicago, IEEE International Symposium on Biomedical imaging. Peruri Srinivasulu , Jollu Vamsi , Kattubadi Drutesh , Gandham Prudhvi.Bone fracture detection using Image Processing, June 2020 IJSDR | Volume 5, Issue 6 ISSN: 2455-2631 B Harinath , Sitrarasu , T.J. Nagalakshmi.Bone Fracture Detection System using Image Processing and Matlab . ISSN: 2278-3075, Volume-8 Issue-12, October, 2019 D.GowthamNaidu , R. Puviarasi , Mritha Ramalingam . Automatic Fracture Detection in Hand Bone Using MATLAB and Image Processing. IEEE Xplore Part Number: CFP20M19-ART, ICISS 2020 Asha Joy Jacob, Apala Chakrabarti , R Sivakumar . Bone Fracture Detection Using Image Processing and Neural Networks. NOVEMBER 27-28TH ,2020