Biomedical Image Processing for Human Elbow
|
|
- Primrose Davis
- 6 years ago
- Views:
Transcription
1 Biomedical Image Processing for Human Elbow Akshay Vishnoi, Sharad Mehta, Arpan Gupta Department of Mechanical Engineering Graphic Era University Dehradun, India Abstract Biomedical image processing is an emerging field for analyzing medical images to develop models for clinical analysis and medical intervention. Recently with the development in biomechanics, the models developed can be used for mechanical stress and vibrational analysis of human body parts. In this work, computer aided design (CAD) models are developed for a patient specific human elbow using Magnetic Resonance Imaging (MRI) data using open source software ITK-snap. The 3-D CAD model is developed using segmentation of MRI data using manual method and automatic methods. Different algorithms and parameter variation have been implemented for development of the human elbow model. Keywords Biomedical imaging; CT scan MRI images; CAD model development; segmentation. The elbow joint is an important joint in human body and it is composed of bones, ligaments, tendons and cartilages. The elbow is a very complex joint in the human body. To obtain the 3D reconstructed CAD model of human elbow, we have used 2D medical grey scale image from MRI data. We have used open source software ITK-snap with its different methods and algorithm to generate the CAD model of human elbow. II. INPUT DATA The 2D medical images were obtained from CT scan images and MRI data (grey scale images) from Radiology department. Fig. 1 shows the overall patient elbow that is being analyzed. The images later on show the focused elbow region in different planes. I. INTRODUCTION Biomedical image processing enables analysis and visualization of human body parts and internal structures such as bones, ligaments, tendons, etc. This is generally accomplished through radiography, Magnetic Resonance Imaging (MRI), Ultrasound, Computerized tomography (CT scan) data, thermography, nuclear medicine, etc. Images obtained involve basic image processing techniques such as improving intensity, noise cleaning, filtering, etc. [1]. In the past, this field was related to experimental analysis, however, with the development of computers and image processing capabilities this area has seen drastic improvement over past decades. Various methods and algorithms have been used for image enhancement, grey-level mapping, spectral analysis, region extraction, etc. [2]. Further image extraction and 3D model reconstruction from MRI and CT scan data has received significant attention [3]. The 3D reconstructed CAD models are used in tissue engineering [4], [5]. CAD models are also used for developing models using rapid prototyping [6]. Hacene Ameddah et al. [7], [8] successfully published their work on mechanics using 2D medical images and their successful segmentation to create a 3D model of human Knee for analysis purpose. Figure 1 Greyscale image of human elbow The images were processed and parameters were manipulated to obtain better resolution and clarity in the image. The first processing was done on the image intensity level, which made the image appear distinct from the background. Further processing and manipulation were done such as slice display order, contrast adjustment, layer inspection, display appearance etc. Final 2D output for a single image is show in Fig. 2.
2 view. The corresponding region on interest in another view can also be selected by drawing appropriate region. This process is manually repeated for various sectional views and the model is formed. This method is quite laborious, however it gives the discretion of selection to the user. Figure 2 Greyscale image of one sectional view The images were given as input to the software ITK-snap. The view in the software is show in fig. 3, where three windows show axial, coronal, and sagittal planes views of the human elbow. The fourth window is the region where 3D model will be developed. Figure 4 Manual segmentation of MRI data. IV. AUTOMATIC SEGMENTATION The methodology behind automatic segmentation in ITK-snap is based on an algorithm called as snake evolution. The term snake is used to refer to a closed curve (or surface in 3D) that represents segmentation. In snake evolution method, the snake evolves from a very rough estimate of the anatomical structure of interest to a very close approximation of the structure; there are two methods to perform automatic segmentation. One is intensity based region method and another method is image edge based method. In this paper, work is accomplished by performing intensity region based method because the results obtained by this method are more accurate and reliable than the image edge based method. And the probabilities of errors are also reduced as compare to image edge based method. Figure 3 Software view showing three cross-sectional view of the input data. III. MANUAL SEGMENTATION After basic image manipulation and enhancement, the segmentation process is initiated. There are different methods for 3D model construction. The simplest, but lengthiest is the manual segmentation. Segmentation in bio images in SNAP stands for allocating a label to each voxel in the structure. A label is a number between 0 and 255 according to the software. Associated with each label is a name and a set of display settings, such as the color used to display the label. The green portion around the polygon indicates all currently selected vertices. To paint closed polygons, the polygon tool is used. A closed loop is drawn over the region of interest (such as tissue or bone etc as shown in Fig. 4) in one cross-sectional A. INTENSITY BASED REGIONS This procedure starts with filling up the intensity regions using intensity filler, here we assign the value of intensity on a scale of 0-1. Maximum intensity assigned is 1 and minimum is 0. These parameters are altered by modifying the parameters lower threshold, upper threshold and smoothness. Lower threshold value was kept as , upper threshold was kept and smoothness was kept as The intensity based region method is demonstrated in Fig. 5. In this figure, blue region is of maximum intensity and white region, which is indicating elbow portion is assigned to minimum with varying intensity.
3 C. SNAKE PROPAGATION To propagate snake in such a required fashion, we need to set propagation parameters and indulge forces like balloon force and curvature force. Balloon force may vary from contracting nature to expanding nature and as well as to static mode also. The balloon force governs the nature of bubbles, while final iterations are in process. Curvature forces can vary from detailed, smooth and spherical nature. Here balloon force being expanding 0.8 and curvature force being detailed Figure 7 describes the fashion of snake propagation. Figure 5 Intensity based regions. B. SNAKE INITIALISATION In next step to complete automatic segmentation bubbles are introduced in such a fashion so that they expands three dimensionally to cover the region which is assigned to zero or minimum intensities. These bubbles may vary in quantities according to the requirement and structure. Initially bubbles are placed in 2D planes, but simultaneously they take place three dimensionally, that means, we assign bubbles in one single plane only automatically they allocate their location in other respective plane, and expands. Figure 7 Bubbles expansion and propagation At the same time a 3D outlook of allocated bubbles can be obtained in 3D output window as shown in figure 8. Figure 6 Bubbles initialization Figure 8 3-D Outlook of allocated bubbles
4 V. RESULTS 3D reconstructed model is obtained after performing number of iterations (~2200). The model is shown in the fourth window of the software (Fig. 9, 10). The model is exported in CAD format i.e. STL (stereolithography model), which can be further used for mechanical analysis in finite element softwares. for bones and ligaments is kept at red color and rest of the elbow structure is kept blue in color, which helps in differentiating between elbow bone and elbow structure. The CAD model is viewed by using an open source software ParaView. This software helps in visualization of the final 3D CAD model of human elbow structure. Figure 9 shows the CAD model under process in 2D window as well as in 3D window. Figure 11 Final 3D CAD model in ParaView. Figure 9 Model under process window Figure 10 shows the 3D outlook window of ITK-snap software complete CAD model of elbow structure in ITK-snap software. VI. DISCUSSION The developed three dimensional CAD model of human elbow bone can be used for further research work. Researchers can use this model for further mechanical analysis such as force/stress analysis, vibrational analysis, etc. This can be carried out using finite element analysis or other analysis methods. The 3D CAD models developed can be used to develop instruments and artificial organs which can help handicaps. This model and analysis details will also help to innovate new medical tools which will help patients in different sense. This work can help to develop a CAD model for different body parts like bones, soft tissue, ligaments, skin etc. with their different stuff properties which is important for safety point of view. Figure 10 3D CAD model in ITK-snap Figure 11 represents the final 3D CAD model of human elbow, which is successfully visualized by ParaView software. ParaView software also allows to represent the CAD model of human elbow with different labels, as in figure 11, the cavity VII. CONCLUSION In this work, biomedical image processing has been carried out for MRI data of a human elbow. The 2D greyscale image for different cross-sectional views are segmented to form a 3D restructured model of a human elbow. The segmentation can be broadly performed using manual and automatic methods. The automatic methods have various algorithms and parameters that can be varied to obtain various 3D models. The segmentation was performed using open source software ITK-snap. The 3D model obtained was exported to STL format, which can be used for mechanical finite element analysis, to perform stress and vibration analysis. Thus the
5 study can be of great help in designing biomedical instruments and devices, aids for handicaps, and sport equipments. REFERENCES [1] H. K. Huang, Biomedical image processing., Crit. Rev. Bioeng., vol. 5, no. 3, pp , [2] A. P. Dhawan, A review on biomedical image processing and future trends, Comput. Methods Programs Biomed., vol. 31, no. 3, pp , [3] B. Starly, Z. Fang, W. Sun, A. Shokoufandeh, and W. Regli, Three-dimensional reconstruction for medical- CAD modeling, Comput. Aided. Des. Appl., vol. 2, no. 1 4, pp , [4] W. Sun and P. Lal, Recent development on computer aided tissue engineering a review, Comput. Methods Programs Biomed., vol. 67, no. 2, pp , [5] W. Sun, B. Starly, J. Nam, and A. Darling, Bio-CAD modeling and its applications in computer-aided tissue engineering, Comput. Des., vol. 37, no. 11, pp , [6] R. Jamieson and H. Hacker, Direct slicing of CAD models for rapid prototyping, Rapid Prototyp. J., vol. 1, no. 2, pp. 4 12, [7] H. Ameddah and M. Assas, BIO-CAD MODELING OF HUMAN KNEE. [8] H. Ameddah and M. Assas, Three-Dimensional (3D) Bio-Cad Modeling of Human Knee, Adv. Sci. Lett., vol. 19, no. 3, pp , 2013.
MODELLING OF PROSTHETIC HIP JOINT GENERATED FROM CT SCAN DATA Mahender Koduri 1, G Krishna Teja 2, O Rajender 3 1,2,3
MODELLING OF PROSTHETIC HIP JOINT GENERATED FROM CT SCAN DATA Mahender Koduri 1, G Krishna Teja 2, O Rajender 3 1,2,3 Asst. Professor, Dept. of Mech. Engg. AGI ABSTRACT Total hip arthroplasty is a surgical
More informationComputed tomography (Item No.: P )
Computed tomography (Item No.: P2550100) Curricular Relevance Area of Expertise: Biology Education Level: University Topic: Modern Imaging Methods Subtopic: X-ray Imaging Experiment: Computed tomography
More informationLearn Image Segmentation Basics with Hands-on Introduction to ITK-SNAP. RSNA 2016 Courses RCB22 and RCB54
Learn Image Segmentation Basics with Hands-on Introduction to ITK-SNAP RSNA 2016 Courses RCB22 and RCB54 RCB22 Mon, Nov 28 10:30-12:00 PM, Room S401CD RCB54 Thu, Dec 1 2:30-4:30 PM, Room S401CD Presenters:
More informationCopyright 2017 Medical IP - Tutorial Medip v /2018, Revision
Copyright 2017 Medical IP - Tutorial Medip v.1.0.0.9 01/2018, Revision 1.0.0.2 List of Contents 1. Introduction......................................................... 2 2. Overview..............................................................
More informationDeveloping 3D Finite element model of Head using Magnetic resonance imaging and algorithm developed in MATLAB
Developing 3D Finite element model of Head using Magnetic resonance imaging and algorithm developed in MATLAB Dr Chandrashekhar Bendigeri 1, Sachin Patil 2 1 Associate Professor, Department of Mechanical
More informationThree-Dimensional Reconstruction for Medical-CAD Modeling
431 Three-Dimensional Reconstruction for Medical-CAD Modeling B. Starly 1 (Binil.Starly@drexel.edu), Z. Fang 1 (zhibin.fang@drexel.edu), W. Sun 1 (sunwei@drexel.edu), A. Shokoufandeh 2 (ashokouf@cs.drexel.edu)
More informationDigital Image Processing
Digital Image Processing SPECIAL TOPICS CT IMAGES Hamid R. Rabiee Fall 2015 What is an image? 2 Are images only about visual concepts? We ve already seen that there are other kinds of image. In this lecture
More informationComputer Aided Design for Osseo-Integration Applications
Computer Aided Design for Osseo-Integration Applications Anoop Parthasarathy 1, Dr Ramkumar P S 2, Yogesh Jain 3 1 Engineer, Applied Cognition Systems Pvt. Ltd. Bangalore, India, 2 Adjunct Professor,Manipal
More informationComputed tomography of simple objects. Related topics. Principle. Equipment TEP Beam hardening, artefacts, and algorithms
Related topics Beam hardening, artefacts, and algorithms Principle The CT principle is demonstrated with the aid of simple objects. In the case of very simple targets, only a few images need to be taken
More informationA Procedure for the 3D Reconstruction of Biological Organs from 2D Image Sequences
A Procedure for the 3D Reconstruction of Biological Organs from 2D Image Sequences Kirana Kumara P Centre for Product Design and Manufacturing Indian Institute of Science Bangalore, 560 012 India Ashitava
More informationUGviewer: a medical image viewer
Appendix A UGviewer: a medical image viewer As a complement to this master s thesis, an own medical image viewer was programmed. This piece of software lets the user visualize and compare images. Designing
More informationFINITE ELEMENT EVALUATION OF THE MECHANICAL BEHAVIOUR OF A DETAILED FOOT/FOOTWEAR MODEL
Proceedings of the 6th International Conference on Mechanics and Materials in Design, Editors: J.F. Silva Gomes & S.A. Meguid, P.Delgada/Azores, 26-3 July 215 PAPER REF: 5479 FINITE ELEMENT EVALUATION
More informationComputer-Aided Diagnosis in Abdominal and Cardiac Radiology Using Neural Networks
Computer-Aided Diagnosis in Abdominal and Cardiac Radiology Using Neural Networks Du-Yih Tsai, Masaru Sekiya and Yongbum Lee Department of Radiological Technology, School of Health Sciences, Faculty of
More informationImage-based simulation of the human thorax for cardio-pulmonary applications
Presented at the COMSOL Conference 2009 Milan Image-based simulation of the human thorax for cardio-pulmonary applications F. K. Hermans and R. M. Heethaar, VU University Medical Center, Netherlands R.
More informationProcess to Convert DICOM Data to 3D Printable STL Files
HOW-TO GUIDE Process to Convert DICOM Data to 3D Printable STL Files Mac Cameron, Application Engineer Anatomical models have several applications in the medical space from patient-specific models used
More informationSupplementary Information
Supplementary Information Magnetic resonance imaging reveals functional anatomy and biomechanics of a living dragon tree Linnea Hesse 1,2,*, Tom Masselter 1,2,3, Jochen Leupold 4, Nils Spengler 5, Thomas
More informationMethodological progress in image registration for ventilation estimation, segmentation propagation and multi-modal fusion
Methodological progress in image registration for ventilation estimation, segmentation propagation and multi-modal fusion Mattias P. Heinrich Julia A. Schnabel, Mark Jenkinson, Sir Michael Brady 2 Clinical
More informationA Study of Medical Image Analysis System
Indian Journal of Science and Technology, Vol 8(25), DOI: 10.17485/ijst/2015/v8i25/80492, October 2015 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 A Study of Medical Image Analysis System Kim Tae-Eun
More informationMedical Images Analysis and Processing
Medical Images Analysis and Processing - 25642 Emad Course Introduction Course Information: Type: Graduated Credits: 3 Prerequisites: Digital Image Processing Course Introduction Reference(s): Insight
More informationWhole Body MRI Intensity Standardization
Whole Body MRI Intensity Standardization Florian Jäger 1, László Nyúl 1, Bernd Frericks 2, Frank Wacker 2 and Joachim Hornegger 1 1 Institute of Pattern Recognition, University of Erlangen, {jaeger,nyul,hornegger}@informatik.uni-erlangen.de
More informationCT IMAGE PROCESSING IN HIP ARTHROPLASTY
U.P.B. Sci. Bull., Series C, Vol. 75, Iss. 3, 2013 ISSN 2286 3540 CT IMAGE PROCESSING IN HIP ARTHROPLASTY Anca MORAR 1, Florica MOLDOVEANU 2, Alin MOLDOVEANU 3, Victor ASAVEI 4, Alexandru EGNER 5 The use
More informationNIH Public Access Author Manuscript Proc Soc Photo Opt Instrum Eng. Author manuscript; available in PMC 2014 October 07.
NIH Public Access Author Manuscript Published in final edited form as: Proc Soc Photo Opt Instrum Eng. 2014 March 21; 9034: 903442. doi:10.1117/12.2042915. MRI Brain Tumor Segmentation and Necrosis Detection
More informationGeometrical Modeling of the Heart
Geometrical Modeling of the Heart Olivier Rousseau University of Ottawa The Project Goal: Creation of a precise geometrical model of the heart Applications: Numerical calculations Dynamic of the blood
More informationImprovement of contrast using reconstruction of 3D Image by PET /CT combination system
Available online at www.pelagiaresearchlibrary.com Advances in Applied Science Research, 2013, 4(1):285-290 ISSN: 0976-8610 CODEN (USA): AASRFC Improvement of contrast using reconstruction of 3D Image
More informationImage Acquisition Systems
Image Acquisition Systems Goals and Terminology Conventional Radiography Axial Tomography Computer Axial Tomography (CAT) Magnetic Resonance Imaging (MRI) PET, SPECT Ultrasound Microscopy Imaging ITCS
More informationEPILOG PREOP. Viewer Interpretation Guideline
EPILOG PREOP Viewer Interpretation Guideline Starting the viewer Launch the viewer for a patient by clicking this icon. A new browser tab or window will open. Data loading To ensure smooth switching between
More informationDENOISING OF COMPUTER TOMOGRAPHY IMAGES USING CURVELET TRANSFORM
VOL. 2, NO. 1, FEBRUARY 7 ISSN 1819-6608 6-7 Asian Research Publishing Network (ARPN). All rights reserved. DENOISING OF COMPUTER TOMOGRAPHY IMAGES USING CURVELET TRANSFORM R. Sivakumar Department of Electronics
More informationSimpleware: Converting 3D Images into Models for Visualisation, Measurement and Computational Simulation
converting 3d images into numerical models Simpleware: Converting 3D Images into Models for Visualisation, Measurement and Computational Simulation Dr Ross Cotton (Senior Application Engineer) r.cotton@simpleware.com
More informationDevelopment of Cranium Using Mimics and Rapid Prototyping Using ANSYS
Development of Cranium Using Mimics and Rapid Prototyping Using ANSYS Sadhasivam.C 1, Sai Krishna Gunda 2 1 Assistant Professor, Department of Mechanical Engineering, Saveetha School of Engineering, Saveetha
More informationInteractive segmentation and visualization. system for medical images on mobile devices
J. ADV. SIMULAT. SCI. ENG. Vol. 2, No. 1, 96-107. 2015 Japan Society for Simulation Technology Interactive segmentation and visualization system for medical images on mobile devices Titinunt Kitrungrotsakul,
More informationAvailable Online through
Available Online through www.ijptonline.com ISSN: 0975-766X CODEN: IJPTFI Research Article ANALYSIS OF CT LIVER IMAGES FOR TUMOUR DIAGNOSIS BASED ON CLUSTERING TECHNIQUE AND TEXTURE FEATURES M.Krithika
More informationMedical Image Analysis
Computer assisted Image Analysis VT04 29 april 2004 Medical Image Analysis Lecture 10 (part 1) Xavier Tizon Medical Image Processing Medical imaging modalities XRay,, CT Ultrasound MRI PET, SPECT Generic
More information6th International DAAAM Baltic Conference INDUSTRIAL ENGINEERING April 2008, Tallinn, Estonia. Radu, C. & Roşca, I.C.
6th International DAAAM Baltic Conference INDUSTRIAL ENGINEERING 24-26 April 2008, Tallinn, Estonia ON THE DESIGN OF A MEDICAL IMPLANT USED FOR OSTEOSYNTHESIS OF THE TRANSSINDESMOTIC FIBULAR FRACTURE PART
More informationMEDICAL IMAGE ANALYSIS
SECOND EDITION MEDICAL IMAGE ANALYSIS ATAM P. DHAWAN g, A B IEEE Engineering in Medicine and Biology Society, Sponsor IEEE Press Series in Biomedical Engineering Metin Akay, Series Editor +IEEE IEEE PRESS
More informationTUBULAR SURFACES EXTRACTION WITH MINIMAL ACTION SURFACES
TUBULAR SURFACES EXTRACTION WITH MINIMAL ACTION SURFACES XIANGJUN GAO Department of Computer and Information Technology, Shangqiu Normal University, Shangqiu 476000, Henan, China ABSTRACT This paper presents
More informationThe Modeling of 3D Tibia Bone Using the CT Images and Printing
2016 Published in 4th International Symposium on Innovative Technologies in Engineering and Science 3-5 November 2016 (ISITES2016 Alanya/Antalya - Turkey) The Modeling of 3D Tibia Bone Using the CT Images
More information3D Surface Reconstruction of the Brain based on Level Set Method
3D Surface Reconstruction of the Brain based on Level Set Method Shijun Tang, Bill P. Buckles, and Kamesh Namuduri Department of Computer Science & Engineering Department of Electrical Engineering University
More informationCopyright 2018 Medical IP - Tutorial Medip v x 06/2018, Revision
Copyright 2018 Medical IP - Tutorial Medip v.1.2.0.x 06/2018, Revision 1.0.0.1 List of Contents 1. Introduction......................................................... 2 2. Overview..............................................................
More informationMORPHOLOGY ANALYSIS OF HUMAN KNEE USING MR IMAGERY
MORPHOLOGY ANALYSIS OF HUMAN KNEE USING MR IMAGERY D. Chetverikov 1,2, G. Renner 1 1 Computer and Automation Research Institute, Budapest, Hungary; 2 Eötvös Loránd University, Budapest, Hungary We present
More information[PDR03] RECOMMENDED CT-SCAN PROTOCOLS
SURGICAL & PROSTHETIC DESIGN [PDR03] RECOMMENDED CT-SCAN PROTOCOLS WORK-INSTRUCTIONS DOCUMENT (CUSTOMER) RECOMMENDED CT-SCAN PROTOCOLS [PDR03_V1]: LIVE 1 PRESCRIBING SURGEONS Patient-specific implants,
More informationGeoInterp: Contour Interpolation with Geodesic Snakes Release 1.00
GeoInterp: Contour Interpolation with Geodesic Snakes Release 1.00 Rohit R. Saboo, Julian G. Rosenman and Stephen M. Pizer July 1, 2006 University of North Carolina at Chapel Hill Abstract The process
More informationRapid Prototyping and Multi-axis NC Machining for The Femoral Component of Knee Prosthesis
Rapid Prototyping and Multi-axis NC for The Femoral Component of Knee Prosthesis Jeng-Nan Lee 1*, Hung-Shyong Chen 1, Chih-Wen Luo 1 and Kuan-Yu Chang 2 1 Department of Mechanical Engineering, Cheng Shiu
More informationProstate Detection Using Principal Component Analysis
Prostate Detection Using Principal Component Analysis Aamir Virani (avirani@stanford.edu) CS 229 Machine Learning Stanford University 16 December 2005 Introduction During the past two decades, computed
More informationA Model-Independent, Multi-Image Approach to MR Inhomogeneity Correction
Tina Memo No. 2007-003 Published in Proc. MIUA 2007 A Model-Independent, Multi-Image Approach to MR Inhomogeneity Correction P. A. Bromiley and N.A. Thacker Last updated 13 / 4 / 2007 Imaging Science and
More information3D VISUALIZATION OF SEGMENTED CRUCIATE LIGAMENTS 1. INTRODUCTION
JOURNAL OF MEDICAL INFORMATICS & TECHNOLOGIES Vol. 10/006, ISSN 164-6037 Paweł BADURA * cruciate ligament, segmentation, fuzzy connectedness,3d visualization 3D VISUALIZATION OF SEGMENTED CRUCIATE LIGAMENTS
More informationA comparative approach to computer aided design model of a dog femur
O R I G I N A L A R T I C L E Folia Morphol. Vol. 75, No. 4, pp. 550 554 DOI: 10.5603/FM.a2016.0023 Copyright 2016 Via Medica ISSN 0015 5659 www.fm.viamedica.pl A comparative approach to computer aided
More informationAutomatic segmentation of the cortical grey and white matter in MRI using a Region Growing approach based on anatomical knowledge
Automatic segmentation of the cortical grey and white matter in MRI using a Region Growing approach based on anatomical knowledge Christian Wasserthal 1, Karin Engel 1, Karsten Rink 1 und André Brechmann
More informationPROSTATE CANCER DETECTION USING LABEL IMAGE CONSTRAINED MULTIATLAS SELECTION
PROSTATE CANCER DETECTION USING LABEL IMAGE CONSTRAINED MULTIATLAS SELECTION Ms. Vaibhavi Nandkumar Jagtap 1, Mr. Santosh D. Kale 2 1 PG Scholar, 2 Assistant Professor, Department of Electronics and Telecommunication,
More informationA fast breast nonlinear elastography reconstruction technique using the Veronda-Westman model
A fast breast nonlinear elastography reconstruction technique using the Veronda-Westman model Mohammadhosein Amooshahi a and Abbas Samani abc a Department of Electrical & Computer Engineering, University
More informationExtraction and recognition of the thoracic organs based on 3D CT images and its application
1 Extraction and recognition of the thoracic organs based on 3D CT images and its application Xiangrong Zhou, PhD a, Takeshi Hara, PhD b, Hiroshi Fujita, PhD b, Yoshihiro Ida, RT c, Kazuhiro Katada, MD
More informationDetection & Classification of Lung Nodules Using multi resolution MTANN in Chest Radiography Images
The International Journal Of Engineering And Science (IJES) ISSN (e): 2319 1813 ISSN (p): 2319 1805 Pages 98-104 March - 2015 Detection & Classification of Lung Nodules Using multi resolution MTANN in
More informationChapter 3 Set Redundancy in Magnetic Resonance Brain Images
16 Chapter 3 Set Redundancy in Magnetic Resonance Brain Images 3.1 MRI (magnetic resonance imaging) MRI is a technique of measuring physical structure within the human anatomy. Our proposed research focuses
More informationAutomated segmentation methods for liver analysis in oncology applications
University of Szeged Department of Image Processing and Computer Graphics Automated segmentation methods for liver analysis in oncology applications Ph. D. Thesis László Ruskó Thesis Advisor Dr. Antal
More informationWhat is Visualization? Introduction to Visualization. Why is Visualization Useful? Visualization Terminology. Visualization Terminology
What is Visualization? Introduction to Visualization Transformation of data or information into pictures Note this does not imply the use of computers Classical visualization used hand-drawn figures and
More informationFiber Selection from Diffusion Tensor Data based on Boolean Operators
Fiber Selection from Diffusion Tensor Data based on Boolean Operators D. Merhof 1, G. Greiner 2, M. Buchfelder 3, C. Nimsky 4 1 Visual Computing, University of Konstanz, Konstanz, Germany 2 Computer Graphics
More information3D Numerical Analysis of an ACL Reconstructed Knee
3D Numerical Analysis of an ACL Reconstructed Knee M. Chizari, B. Wang School of Engineering, University of Aberdeen, Aberdeen AB24 7QW, UK Abstract: Numerical methods applicable to the tibia bone and
More informationAnthropometric Investigation of Head Measurements for Indian Adults
Anthropometric Investigation of Measurements for Indian Adults Parth SHAH 1, Yan LUXIMON* 1, Fang FU 1, Vividh MAKWANA 2 1 School of Design, The Hong Kong Polytechnic University, Hong Kong; 2 Navneet Hi-Tech
More informationANALYSIS OF PULMONARY FIBROSIS IN MRI, USING AN ELASTIC REGISTRATION TECHNIQUE IN A MODEL OF FIBROSIS: Scleroderma
ANALYSIS OF PULMONARY FIBROSIS IN MRI, USING AN ELASTIC REGISTRATION TECHNIQUE IN A MODEL OF FIBROSIS: Scleroderma ORAL DEFENSE 8 th of September 2017 Charlotte MARTIN Supervisor: Pr. MP REVEL M2 Bio Medical
More informationMonday, Tuesday, Wednesday, and Thursday, 1 pm to 3 or 4 pm. (See Course Schedule for details)
Anatomy 6201 Course Director: Dr. Ernesto Phone: (303) 724-3430 Office: RC1 South Rm 11124 Office Hours: by appointment Email: ernesto.salcedo@ucdenver Location ED 2 South Room 2206.! Course Hours Monday,
More information2D Rigid Registration of MR Scans using the 1d Binary Projections
2D Rigid Registration of MR Scans using the 1d Binary Projections Panos D. Kotsas Abstract This paper presents the application of a signal intensity independent registration criterion for 2D rigid body
More informationMedical Imaging Introduction
Medical Imaging Introduction Jan Kybic February 16, 2010 Medical imaging: a collaborative paradigm picture from Atam P. Dhawan: Medical Imaging From physiology to information processing (what we should
More informationEdge-Preserving Denoising for Segmentation in CT-Images
Edge-Preserving Denoising for Segmentation in CT-Images Eva Eibenberger, Anja Borsdorf, Andreas Wimmer, Joachim Hornegger Lehrstuhl für Mustererkennung, Friedrich-Alexander-Universität Erlangen-Nürnberg
More informationImage-Guided Analysis of Shoulder Pathologies: Modelling the 3D Deformation of the Subacromial Space during Arm Flexion and Abduction
Image-Guided Analysis of Shoulder Pathologies: Modelling the 3D Deformation of the Subacromial Space during Arm Flexion and Abduction Alexandra Branzan Albu 1, Denis Laurendeau 1, Luc. J. Hébert 2, Hélène
More informationINDUSTRIAL SYSTEM DEVELOPMENT FOR VOLUMETRIC INTEGRITY
INDUSTRIAL SYSTEM DEVELOPMENT FOR VOLUMETRIC INTEGRITY VERIFICATION AND ANALYSIS M. L. Hsiao and J. W. Eberhard CR&D General Electric Company Schenectady, NY 12301 J. B. Ross Aircraft Engine - QTC General
More informationThe protocols used to scan the musculoskeletal system are tailored to each patient and
Chapter 22. Musculoskeletal Protocols The protocols used to scan the musculoskeletal system are tailored to each patient and region being examined. The clinical indication for the examination will also
More informationShape-Based Kidney Detection and Segmentation in Three-Dimensional Abdominal Ultrasound Images
University of Toronto Shape-Based Kidney Detection and Segmentation in Three-Dimensional Abdominal Ultrasound Images Authors: M. Marsousi, K. N. Plataniotis, S. Stergiopoulos Presenter: M. Marsousi, M.
More informationSurface Projection Method for Visualizing Volumetric Data
Surface Projection Method for Visualizing Volumetric Data by Peter Lincoln A senior thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science With Departmental Honors
More informationComputational Medical Imaging Analysis
Computational Medical Imaging Analysis Chapter 1: Introduction to Imaging Science Jun Zhang Laboratory for Computational Medical Imaging & Data Analysis Department of Computer Science University of Kentucky
More informationSegmentation and Modeling of the Spinal Cord for Reality-based Surgical Simulator
Segmentation and Modeling of the Spinal Cord for Reality-based Surgical Simulator Li X.C.,, Chui C. K.,, and Ong S. H.,* Dept. of Electrical and Computer Engineering Dept. of Mechanical Engineering, National
More informationGlobal Thresholding Techniques to Classify Dead Cells in Diffusion Weighted Magnetic Resonant Images
Global Thresholding Techniques to Classify Dead Cells in Diffusion Weighted Magnetic Resonant Images Ravi S 1, A. M. Khan 2 1 Research Student, Department of Electronics, Mangalore University, Karnataka
More informationMethods for increasing customization in rapid machining patient-specific bone implants
Graduate Theses and Dissertations Iowa State University Capstones, Theses and Dissertations 2011 Methods for increasing customization in rapid machining patient-specific bone implants Shawn Spencer Iowa
More informationPHYSICAL REPLICATION OF HUMAN BONE BY USING DIRECT INTEGRATION OF REVERSE ENGINEERING AND RAPID PROTOTYPING TECHNIQUES
PHYSICAL REPLICATION OF HUMAN BONE BY USING DIRECT INTEGRATION OF REVERSE ENGINEERING AND RAPID N. N. Kumbhar 1*, Dr. A. V. Mulay 2, Dr. B. B. Ahuja 3 1 Production Engg. Dept., College of Engineering,
More informationComputational Medical Imaging Analysis Chapter 4: Image Visualization
Computational Medical Imaging Analysis Chapter 4: Image Visualization Jun Zhang Laboratory for Computational Medical Imaging & Data Analysis Department of Computer Science University of Kentucky Lexington,
More informationDUE to beam polychromacity in CT and the energy dependence
1 Empirical Water Precorrection for Cone-Beam Computed Tomography Katia Sourbelle, Marc Kachelrieß, Member, IEEE, and Willi A. Kalender Abstract We propose an algorithm to correct for the cupping artifact
More informationSCIENCE & TECHNOLOGY
Pertanika J. Sci. & Technol. 26 (1): 309-316 (2018) SCIENCE & TECHNOLOGY Journal homepage: http://www.pertanika.upm.edu.my/ Application of Active Contours Driven by Local Gaussian Distribution Fitting
More informationDevelopment of 3D Kinematic Model of the Spine for Idiopathic Scoliosis Simulation
153 Development of 3D Kinematic Model of the Spine for Idiopathic Scoliosis Simulation Sasa Cukovic 1, Goran Devedzic 2, Lozica Ivanovic 3, Tanja Zecevic Lukovic 4 and K. Subburaj 5 1 Faculty of Mecahnical
More informationarxiv: v1 [cs.cv] 6 Jun 2017
Volume Calculation of CT lung Lesions based on Halton Low-discrepancy Sequences Liansheng Wang a, Shusheng Li a, and Shuo Li b a Department of Computer Science, Xiamen University, Xiamen, China b Dept.
More informationIntroducing Computer-Assisted Surgery into combined PET/CT image based Biopsy
Introducing Computer-Assisted Surgery into combined PET/CT image based Biopsy Santos TO(1), Weitzel T(2), Klaeser B(2), Reyes M(1), Weber S(1) 1 - Artorg Center, University of Bern, Bern, Switzerland 2
More informationMedical Image Processing: Image Reconstruction and 3D Renderings
Medical Image Processing: Image Reconstruction and 3D Renderings 김보형 서울대학교컴퓨터공학부 Computer Graphics and Image Processing Lab. 2011. 3. 23 1 Computer Graphics & Image Processing Computer Graphics : Create,
More informationHybrid Approach for MRI Human Head Scans Classification using HTT based SFTA Texture Feature Extraction Technique
Volume 118 No. 17 2018, 691-701 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu ijpam.eu Hybrid Approach for MRI Human Head Scans Classification using HTT
More informationA novel noise removal using homomorphic normalization for multi-echo knee MRI
A novel noise removal using homomorphic normalization for multi-echo knee MRI Xuenan Cui 1a),HakilKim 1b), Seongwook Hong 1c), and Kyu-Sung Kwack 2d) 1 School of Information and Communication Engineering,
More informationExploiting Typical Clinical Imaging Constraints for 3D Outer Bone Surface Segmentation
Exploiting Typical Clinical Imaging Constraints for 3D Outer Bone Surface Segmentation Chris Mack, Vishali Mogallapu, Andrew Willis, Thomas P. Weldon UNC Charlotte, Department of Electrical and Computer
More informationReconstruction of complete 3D object model from multi-view range images.
Header for SPIE use Reconstruction of complete 3D object model from multi-view range images. Yi-Ping Hung *, Chu-Song Chen, Ing-Bor Hsieh, Chiou-Shann Fuh Institute of Information Science, Academia Sinica,
More informationA New Application for Displaying and Fusing Multimodal Data Sets
A New Application for Displaying and Fusing Multimodal Data Sets Karl G. Baum* ac, María Helguera a, Andrzej Krol b a Rochester Institute of Technology, 54 Lomb Memorial Drive, Rochester, NY, USA 14623-5604;
More informationMiniaturizing Components by Reverse Engineering and Rapid Prototyping Techniques
Miniaturizing Components by Reverse Engineering and Rapid Prototyping Techniques L. Francis Xavier 1, Dr. D. Elangovan 2, N.Subramani 3, R.Mahesh 4 Assistant Professor, Department of Mechanical Engineering,
More informationLecture 10 Segmentation, Part II (ch 8) Active Contours (Snakes) ch. 8 of Machine Vision by Wesley E. Snyder & Hairong Qi
Lecture 10 Segmentation, Part II (ch 8) Active Contours (Snakes) ch. 8 of Machine Vision by Wesley E. Snyder & Hairong Qi Spring 2018 16-725 (CMU RI) : BioE 2630 (Pitt) Dr. John Galeotti The content of
More informationTomographic Reconstruction
Tomographic Reconstruction 3D Image Processing Torsten Möller Reading Gonzales + Woods, Chapter 5.11 2 Overview Physics History Reconstruction basic idea Radon transform Fourier-Slice theorem (Parallel-beam)
More informationCh. 4 Physical Principles of CT
Ch. 4 Physical Principles of CT CLRS 408: Intro to CT Department of Radiation Sciences Review: Why CT? Solution for radiography/tomography limitations Superimposition of structures Distinguishing between
More informationScene-Based Segmentation of Multiple Muscles from MRI in MITK
Scene-Based Segmentation of Multiple Muscles from MRI in MITK Yan Geng 1, Sebastian Ullrich 2, Oliver Grottke 3, Rolf Rossaint 3, Torsten Kuhlen 2, Thomas M. Deserno 1 1 Department of Medical Informatics,
More informationBased on the cross section contour surface model reconstruction
International Journal of Research in Engineering and Science (IJRES) ISSN (Online): 2320-9364, ISSN (Print): 2320-9356 Volume 3 Issue 12 ǁ December. 2015 ǁ PP.07-12 Based on the cross section contour surface
More informationSegmentation and Quantification of Brain Tumor
VECIMS 2004 IEEE International Conference on Virtual Environments, Human-Computer Interfaces, and Measurement Systems Boston, MD, USA, 12-14 July 2004 Segmentation and Quantification of Brain Tumor Chunyan
More informationOssa 3D User Manual. ios App v1.0.0
Ossa 3D User Manual ios App v1.0.0 CONTENTS Description 3 Getting Started User Interface 4 Control Gestures 4 View Modes 5 Save Project 5 Settings Menu In-App Purchases 5 Enable Passcode 5 Page Tools View
More informationAutomatic Quantification of DTI Parameters along Fiber Bundles
Automatic Quantification of DTI Parameters along Fiber Bundles Jan Klein 1, Simon Hermann 1, Olaf Konrad 1, Horst K. Hahn 1, and Heinz-Otto Peitgen 1 1 MeVis Research, 28359 Bremen Email: klein@mevis.de
More informationRadiology. Marta Anguiano Millán. Departamento de Física Atómica, Molecular y Nuclear Facultad de Ciencias. Universidad de Granada
Departamento de Física Atómica, Molecular y Nuclear Facultad de Ciencias. Universidad de Granada Overview Introduction Overview Introduction Tecniques of imaging in Overview Introduction Tecniques of imaging
More informationShadow casting. What is the problem? Cone Beam Computed Tomography THE OBJECTIVES OF DIAGNOSTIC IMAGING IDEAL DIAGNOSTIC IMAGING STUDY LIMITATIONS
Cone Beam Computed Tomography THE OBJECTIVES OF DIAGNOSTIC IMAGING Reveal pathology Reveal the anatomic truth Steven R. Singer, DDS srs2@columbia.edu IDEAL DIAGNOSTIC IMAGING STUDY Provides desired diagnostic
More informationNavigation System for ACL Reconstruction Using Registration between Multi-Viewpoint X-ray Images and CT Images
Navigation System for ACL Reconstruction Using Registration between Multi-Viewpoint X-ray Images and CT Images Mamoru Kuga a*, Kazunori Yasuda b, Nobuhiko Hata a, Takeyoshi Dohi a a Graduate School of
More informationCHAPTER 3 FINITE ELEMENT MODELING OF TIBIA BONE AND IMPLANT
59 CHAPTER 3 FINITE ELEMENT MODELING OF TIBIA BONE AND IMPLANT 3.1 INTRODUCTION 3.1.1 Construction of CAD based bio modeling Although non-invasive modalities, such as CT, Micro CT, MRI and Optical Microscopy
More informationDavid Wagner, Kaan Divringi, Can Ozcan Ozen Engineering
Internal Forces of the Femur: An Automated Procedure for Applying Boundary Conditions Obtained From Inverse Dynamic Analysis to Finite Element Simulations David Wagner, Kaan Divringi, Can Ozcan Ozen Engineering
More informationConference Biomedical Engineering
Automatic Medical Image Analysis for Measuring Bone Thickness and Density M. Kovalovs *, A. Glazs Image Processing and Computer Graphics Department, Riga Technical University, Latvia * E-mail: mihails.kovalovs@rtu.lv
More informationA Design Toolbox to Generate Complex Phantoms for the Evaluation of Medical Image Processing Algorithms
A Design Toolbox to Generate Complex Phantoms for the Evaluation of Medical Image Processing Algorithms Omar Hamo, Georg Nelles, Gudrun Wagenknecht Central Institute for Electronics, Research Center Juelich,
More information