- Email: [email protected]
Development of an accurate CAD model of femur bone
Materials Today: Proceedings xxx (xxxx) xxx
Contents lists available at ScienceDirect
Materials Today: Proceedings journal homepage: www.elsevier.com/locate/matpr
Development of an accurate CAD model of femur bone K.C. Nithin Kumar a,⇑, Subhash Chavadaki b, Vaishali Chaudhry a, Durgeshwar Pratap Singh a, Amir Shaikh a a b
Graphic Era (Deemed to be University), Dehradun, Uttarakhand 248002, India School of Technology, GITAM (Deemed to be University), Bengaluru, Karnataka 561203, India
a r t i c l e
i n f o
Article history: Received 15 November 2019 Accepted 4 December 2019 Available online xxxx Keywords: ITK-SNAP CT scan Segmentation Femur bone 3D cad model
a b s t r a c t This research work explores the area of biomedical image processing. A CAD model of human femur structure has been developed from computerized tomographic image taken as input data, which is further modified and segmented to form an accurate CAD model of a human femur by varying the segmentation methods and parameters, multi component model of whole femur structure is obtained as an assembly of bone. The segmentation is performed using an open source code ITK SNAP. This segmented CAD model of human femur structure can be used for engineering analysis such as static structural analysis, Model analysis and also used in manufacturing of handicapped aids, artificial bones, safety in sports equipment, design and implementing the rapid prototyping for Multi scaled model development. Ó 2019 Elsevier Ltd. All rights reserved. Selection and of the scientific committee of the 10th International Conference of Materials Processing and Characterization.
1. Introduction ITK-SNAP is an intuitive programming application that enables clients to explore three-dimensional medicinal images. ITK-SNAP is an open source software which is appropriated under general public license. It is composed in C++ language and can keep running on the various operating systems. It can read and compose different kinds of medical image formats. SNAP (Snake Automatic Partitioning) tool developed as a group programming venture in a computer science course at UNC drove by Guido Gerig amid 1999–2002. Amid 2002–03 SNAP incorporated into the NIH Insight Toolkit (ITK) and renamed as ITK-SNAP [8]. ITK-SNAP can fragment a variety of images. The images must be homogeneous, i.e. having solitary power esteem for every pixel. SNAP can be utilized with MRI, CT and PET images, but with no shading cryo-area or diffuser tensor images. SNAP can read a variety of images formats including NIFTI, DICOM, RAW, GIPL, Analyze, and Meta-Image. ITK-SNAP underpins each of the 13 formats. These configurations vary by the kind of images and measure of metadata they can store. ITK-SNAP can be utilized as a part of various modes: manual segmentation, semi-automatic segmentation, and fully automatic segmentation. The manual mode is utilized for segmentation utilizing hand forming and for tidying up the aftereffects of automatic
⇑ Corresponding author. Tel.: +918449264274. E-mail address: [email protected] (K.C. Nithin Kumar).
segmentation. Manual mode is, for the most part, utilized when structure to section is mind-boggling (little complex structures) or 2D data is accessible in the type of low-quality images. This is a tedious strategy. This is a refining technique; it refines segmented data acquired from automatic mode. In Semi-automatic mode, the client directs the computer to perform segmentation of anatomical structure. The client chooses estimations of parameters which guides active contour called ‘‘Snake”. Active contour is a bend that spread all through the structure and segments it. In automatic segmentation mode, the computer does everything. It processes the therapeutic image itself, so no compelling reason to include parameters from the client. This is a quick mode subsequently saving a considerable measure of time. Automatic mode of segmentation can’t be utilized when the quality of the medicinal image isn’t great i.e. the contrast between intensities of structures is little. Above all ITK-SNAP was produced for the clinical clients; a client who as of now utilizes a computer for image segmentation and along these lines comprehends the principal of 3D medical imaging (Fig. 1). Snake Automatic Partitioning shows segmentation with relegating labels to voxels in the input image. For example, while segmentation of cerebrum Magnetic Resonance Image, a portion of pixels in the image might be allocated label ’grey matter’, others shall be allotted label ’lateral ventricle’, and so on. This is on the user to concoct rundown of labels to be used in specific segmentation assignment. Every single voxel of input image could be doled out
https://doi.org/10.1016/j.matpr.2019.12.030 2214-7853/Ó 2019 Elsevier Ltd. All rights reserved. Selection and of the scientific committee of the 10th International Conference of Materials Processing and Characterization.
Please cite this article as: K. C. Nithin Kumar, S. Chavadaki, V. Chaudhry et al., Development of an accurate CAD model of femur bone, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.030
2
K.C. Nithin Kumar et al. / Materials Today: Proceedings xxx (xxxx) xxx
Fig. 1. Grey-scale image representations.
a solitary label. Volumetric image of labels is the result of Snake Automatic Partitioning [9–11]. The majority of the window of the SNAP consists of four boards, three out of which indicate perpendicular cuts through an image and the last one, situated at the base left demonstrates 3Dimensional perspective of segmentation. The left part of the window is involved by a tall and thin zone called control board. At the highest point, the control board contains menu bar, utilized to load and save images, for fixing alternatives, and for retrieving help system. Whatever remains of the control board consist of different buttons, sliders, and different controls, which show up and vanish contingent upon present operation mode. Notwithstanding the primary window, a few different windows appear during the session of Snake Automatic Partitioning. These windows are utilized to organize particular assignments, for example, saving and loading images, or choosing parameters in semiautomatic segmentation mode [5].
2. Literature review The survey gives thought for the advancement of CAD model of Femur Bone. Here are a couple of papers referred in this undertaking and are recorded beneath. W. Sun, B. Starley, J. Nam shows biological CAD modelling and its application. CAD is generally used for designing, modelling, manufacturing, and analysis. Development in Biomedicine and information technology makes use of CAD for different biomedical applications, especially for bone engineering where CAD based bone model gives basic data about the bone’s biological characteristics to design, modelling and manufacturing of difficult bone replacements. This paper will show some advancement in bio cad modelling and its application in designing and manufacturing bone implants [1]. Philosophy to produce bio-computer aided design models from high determination non-intrusive image, medical image process, and three-dimensional remaking method will be portrayed. Empowering advance software in helping
three-dimensional reproduction and bio-modelling advancement shall be presented. Usage of Bio-Computer Aided Designing model for depiction and portrayal of organic life structure such as morphology, heterogeneous and hierarchical and creation of bio-blueprint, and authoritative structure, and production of bioblueprint modelling will be introduced. Akshay Vishnoi, Sharad Mehta, Arpan Gupta (2014) displayed their work on bio-mechanical image processing of human elbow bone. The paper displays the technique to develop a 3D model of human elbow utilizing segmentation of Magnetic Resonance Image information utilizing automatic strategy and manual strategies [2]. Paul A. Yushkevich, Joseph Piven, Heather Cody Hazlett, Rachel Gimpel Smith, Sean Ho, James C. Well, and Guido Gerig (2006) exhibited their work on 3D dynamic shape segmentation of anatomical structure. The paper portrays the techniques and software designing rationality behind dynamic form segmentation and gives the aftereffects of approval tests performed with regards to a continuous kid Austin neuroimaging study [3]. Jayesh J. Dange, Mohammad Ali Ansari, Jitesh Bhai, Madvi (2013) manufactured a CAD model of human knee bone. The achievement of the uniquely crafted human joint substitution medical procedure relies upon correct extraction of geometric shape and size of the joint. Additionally, empowering the modelling and assembling exact prostheses structure must lead to the patient’s long-haul solace and scope of movement. The geometry of the knee joint is extricated from Computed Tomography scanning. Three- dimensional model of knee reproduction helps for superior comprehension of morphological examination and anatomical usefulness. The threedimensional strong model is developed from two-dimensional Computer Tomography images utilizing computer-aided software like PRO-Engineer, Creo, NX 6.0 and MIMIC10.0. Created model was approved by estimating different parameters such as anteroposterior diameter and lateral and medial femoral condyle width, height and width of the articular surface of the patella and so forth on CAD model. Outcomes are exhibited in an arranged manner. The philosophy can be adjusted for different joints such as shoulder, lower leg and so forth [4]. Hacene Ameddah, et. al. displayed a
Please cite this article as: K. C. Nithin Kumar, S. Chavadaki, V. Chaudhry et al., Development of an accurate CAD model of femur bone, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.030
K.C. Nithin Kumar et al. / Materials Today: Proceedings xxx (xxxx) xxx
paper in fifteenth International conference of exploratory mechanics, college of Batna, Algerie on the best possible assessment of two-dimensional medicinal images, and their fruitful segmentation to make a three-dimensional computerized model of human knee-joint, for investigation purposes. Medical images (CT, X-Ray, and MR) of human organs are broadly utilized as a part of the regular clinical praxis. More profound knowledge of the anatomical properties can be got by building a three-dimensional model from the images. Keeping in mind the end goal to appropriately assess the 2D images and furthermore the 3D objects engineers need proficient and powerful graphical and geometrical tools. The motivation behind this paper was to build up a technique for achieving solid models of the human knee to be utilized as a part of the continuous analysis of the FEA knee arrangements.3D models of distal end of the femur and tibial level were made to surmised human knee ‘‘tibio-femoral joint”. For developing these models, focuses taken from Magnetic Resonance Image were primarily handled by various strategies for edge recognizing utilizing Mat-lab tool kit image processing and afterwards changed over to point cloud having a predictable point in every cross-sectional area. Now onwards, B-splines were made from points in every crosssection. At last, B-splines were processed using the lofted command to shape a solid computer-aided model [5]. Daniela Tarnita, C. Boborelu, D. Popa (2010) from University of Oraiova, Romania, and Harvard University, USA, displayed their work on 3D virtual human Elbow joint utilizing CT images, titled ‘‘3D modelling of the complex virtual human elbow joint”. The paper introduces the calculation to acquire a 3D virtual human elbow joint utilizing CT images. For that reason, we utilized computer-aided parametric design software, which allows characterizing models with a strong abnormal state of trouble including complex 3Dshapes. The virtual biomechanical arrangement of the human elbow containing bones, ligaments, and muscles is considered utilizing the Finite Element Method and will be set up for kinematical and dynamical simulations. The 3D virtual model will be valuable for future Studies Concerning prosthesis optimization, enhancing the exhibitions of endo-prosthetic and exo-prosthetic gadgets, diverse implants and prosthetic frameworks for ordinary and neurotic circumstances, structures which are followed up on by SMA counterfeit muscles or which contain SMA components. In reality, leg and its skeleton were exposed to stresses which are diverse. It is realized that human bone stands out amongst most critical materials which are composite. A technique is introduced in the paper for considering and for the means to acquire virtual bones of the human body. For that design a computer-aided parametric software was utilized which grants to characterize models having an abnormal state of multifaceted nature. To acquire a bone cross-sectional area of the tibia bone, Computer Tomography was utilized. The threedimensional model is examined utilizing FEM, contemplating the genuine structure of bone and mechanical attributes of spongy and cortical bone, and we get stress distribution for various requesting [6]. Daniela Tarnita C Popa, D.N. Tarnita, D. Grecu (2006) from University of Oraiova exhibited a diary at Romanian diary of morphology and Embryology, titled ‘‘CAD model for a three-dimensional model of tibia bone and analysis for stresses using the FEM”. These researchers from sibiu exhibited their work at Annals of the Orcdea University, titled Research with respect to CAD modelling of the human appendages bones (femur) and pointed this to contemplate sicknesses of bones [7]. Craig M. Goeher (2007) a student graduated in ‘‘Aerospace and Mechanical engineering from Notre Dame, Indiana”, exhibited his work on designing of a Humanoid shoulder Elbow complex displaying a thesis on Dynamic Simulation of Knee joint contact during human movements. Themodels were alluring for contemplating knee contact mechanics under various loading conditions [12].
3
3. Methodology In real life, human femur bone experiences most diverse stresses. It is known that human bone is one of the most important natural composite materials. The construction of a 3-D CAD model of such a bone is quite difficult, so various methodologies will be applied for its construction. To start this project we collected the relevant scientific data (consist of MRI data and CT scan images). These are 2-D images of a real bone which will be processed to obtain a 3-D CAD model. ITK-SNAP software is used to construct a 3-D model of the femur bone. This software reads MRI data and CT scan images. The model will be constructed as follows: 1. Grey-scale data input and modification. 2. Manual segmentation. 3. Semi-Automatic segmentation. Segmentation is the process of locating the structure of interest from an image and separates it from other structures of background. 3.1. Grey-scale data input and modification CT scan data and MRI images obtained from MAHENT INDRESH HOSPITAL, DEHRADUN uses as grey-scale data input. These images used as grey-scale data are in DICOM format. DICOM is a standard format used in industry. One important advantage of this format is anonymized data i.e. it does not provide any information about the source so there is no violation of privacy. 3.2. Manual segmentation In manual segmentation user outline structure of interest and model is separated manually. Manual segmentation is generally used in case of complex structures and poor images data (where the intensity of structures are very likely similar). Manual segmentation is a more time-consuming process. 3.3. Semi-automatic segmentation This presents the essential ideas driving the semi-automatic division segment of SNAP. The system behind Snake Automatic Partitioning is known as snake evolution. Snake term is utilized to allude to shut surface which shows segmentation. During snake development techniques, snake advances from an unpleasant gauge of the anatomical structure important to a nearby guess of the structure, as delineated in the figure beneath (Fig. 2). The red bend in this figure is known as the snake. It begins instated as a little hover within the ventricle, and after some time it develops, or advances, to come to fruition of the ventricle. Snake development is administered by the scientific condition that portrays the velocity of each point on the snake at a specific time. The velocity of every point depends (I) on snake state and (2) forces of images in the area of the point. There is an outline of a portion of velocities which can follow up on the snake (Fig. 3). In the given figure, velocities which are acting on a few points are indicated by yellow and blue arrows. Velocity vectors always act in the direction which is perpendicular to the snake direction. Velocity in yellow color is based on properties of image: these velocities are powerful in the area where the intensity of the image is uniform or homogenous and are weaker in the areas where there are discontinuities in the intensity of the image. We make the
Please cite this article as: K. C. Nithin Kumar, S. Chavadaki, V. Chaudhry et al., Development of an accurate CAD model of femur bone, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.030
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K.C. Nithin Kumar et al. / Materials Today: Proceedings xxx (xxxx) xxx
Fig. 2. 2-D Snake evolution.
(a)
(b) Fig. 3. (a) Velocity distribution (b) Velocity intervals.
snake assume to form around these regions, by forcing the snake to expand at a lower rate at image edges than at the homogenous regions. The velocities in blue color are based on the shape of the snake: they are shorter at points where the snake is in a straight shape and larger at points where the snake is more curved in
shape. We make the snake to follow a smooth shape by putting higher velocities with inward directions at points where the snake is sharp. By adding the velocities at a particular point on the snake, we can calculate the movement of the snake at that point. Evolving stages of the snake under the influence of velocities are given in the
Fig. 4. Setting up parameters regarding intensity regions.
Please cite this article as: K. C. Nithin Kumar, S. Chavadaki, V. Chaudhry et al., Development of an accurate CAD model of femur bone, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.030
K.C. Nithin Kumar et al. / Materials Today: Proceedings xxx (xxxx) xxx
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Fig. 5. Filling up intensity regions.
(a)
(b)
Fig. 6. (a) Final Outlook After Filling Up Intensity Regions; (b) Final outlook of the intense region.
Fig. 7. Snake initialization.
figure below. The position of the snake at next time gap is represented by the dashed lines (Fig. 4). SNAP executes two well-known 3-dimensional active contour segmentation methods – Geodesic Active Contours by (1993,
1997) Caselles et al. and Region Competition by Yuille and Zhu. In both techniques, the evolving estimate of the structure of interest is represented by at least one contour. An evolving contour is a closed surface C (u, v, t) parameterized by u, v and t (time)
Please cite this article as: K. C. Nithin Kumar, S. Chavadaki, V. Chaudhry et al., Development of an accurate CAD model of femur bone, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.030
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K.C. Nithin Kumar et al. / Materials Today: Proceedings xxx (xxxx) xxx
Fig. 8. Snake parameters.
Fig. 9. Bubble 3D structure.
(a)
(b) Fig. 10. (a) 3D Processing 1; (b) 3D Processing 2.
variables. The contour evolves according to following partial differential equation (PDE):
3.4. Preprocessing
! d ðt; u; vÞ ¼ F N dt
Once the file is imported in the software then the window appears as shown in Fig. 5. Under this section snake initialization and segmentation is carried out using intensity-based features (Fig. 6).
where F represents the sum of various forces acting on the contour in the normal direction and N is the unit normal to the contour.
Please cite this article as: K. C. Nithin Kumar, S. Chavadaki, V. Chaudhry et al., Development of an accurate CAD model of femur bone, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.030
K.C. Nithin Kumar et al. / Materials Today: Proceedings xxx (xxxx) xxx
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3.5. Filling up intensity regions
3.7. Segmentation (final)
The intensity level is set based on the requirement. The processing window must be clear enough to carry out the further steps.
This is the final section where the setting of Snake Parameters for the final segmentation is carried out (Figs. 8 and 9). This is how bubbles set up in a single plane and further expand in 3-D fashion in order to complete the 3-D CAD model, which are as follows (Figs. 10–13). Carrion model, this tells about how complete 3-D structure is being formed. This is the final 3-D CAD model of the human femur bone.
3.6. Snake initialization The snake is initialized using a circular bubble. More than one bubble can be used to initialize the snake and as the snake evolves, its scattered components can merge together as shown in the figure below (Fig. 7):
(a)
(b) Fig. 11. (a) 3D Processing 3; (b) 3D Processing 4.
(a)
(b) Fig. 12. (a) 3D Processing 5; (b) 3D Processing 6.
(a)
(b) Fig. 13. (a) 3D processing 7; (b) 3D processing 8.
Please cite this article as: K. C. Nithin Kumar, S. Chavadaki, V. Chaudhry et al., Development of an accurate CAD model of femur bone, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.030
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4. Conclusions
References
ITK-SNAP is the easiest and simplest design software that can provide the best view if the input image displayed in a preview section is split into four parts. All tools used during the operation are stored on one side of the window and can be used with a single click like – for viewing, drawing or management of layers. In this software, we can work with most of the image formats available which are DICOM, GE, GIPL, NiFTL, NRRD, VTK, Meta image and some more. To process a particular file just drag that file over the main window, this will help in understanding the medical image effectively. Depending on the results requirement, multiple layers can be created with the help of on-time updating preview section and fewer brushes can be used to improve certain areas under interest simultaneously updating in real-time. Performed segmentation (Manual/Automatic) on 2-D images to transform them into the 3-D output. Derived a 3D CAD model of Femur bone, which can be further used for Analysis purposes, for example, Creating Bone implants, Stress analysis, vibrational analysis, and Force analysis.
[1] W. Sun, B. Starly, J. Nam, A. Darling, Bio-CAD modeling and its applications in computer-aided tissue engineering, Comput. Des. 37 (11) (2005) 1097–1114. [2] Akshay Vishnoi, Sharad Mehta, Arpan Gupta, Biomedical Image Processing for Human Elbow. [3] Paul A. Yushkevich, Joseph Piven, Heather Cody Hazlett, Rachel Gimpel Smith, Sean Ho, James C. Gee, Guido Gerig, User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability, NeuroImage 31 (2006) 1116–1128. [4] Jayesh J. Dange, Mohammad Ali Ansari, Jitesh Bhai, Madvi, Extraction and analysis of knee joint parameter, using CAD base solid modeling techniques, ICGCT (1) (2013) 21–25. [5] Hacene Ameddah, Mekki Assas, 3D Bio-CAD modeling of the human knee, J. Comput. Theor. Nanosci. 19 (3) (2013) 932–936, https://doi.org/10.1166/ asl.2013.4830. [6] Daniela Tarnita, C. Boborelu, D. Popa, The three-dimensional modeling of the complex virtual human elbow joint, Rom. J. Morphol. Embryol. 51 (2010) 489– 495. [7] Daniela Tarnita C. Popa, D.N. Tarnita, D. Grecu, CAD model for a 3D model of the tibia bone and study of Stresses using the finite element method, Rom. J. Morphol. Embryol. 47 (2) (2015) 181–186. [8] K. Van Leemput, F. Maes, D. Vandermeulen, P. Suetens, Automated modelbased bias field correction of MR images of the brain, IETEEE Trans. Med. Imag. 18 (1999) 885–896. [9] ITK-SNAP documentation 3.2. [10] Bala Murali Gunji, B.B.V.L. Deepak, Bijaya Kumar Khamari, CAD-Based Automatic Clash Analysis for Robotic Assembly, Int. J. Math., Eng. Manage. Sci. 4 (2) (2019) 432–441. [11] Shshank Chaube, Pushpa Koranga, Garima Singh, Dikendra Verma, Anuj Kumar, Sangeeta Pant, Image Denoising Based on Wavelet Transform using VisuThresholding Technique, Int. J. Math., Eng. Manage. Sci. 3 (4) (2018) 444– 449. [12] Craig M. Goeher, Design of a Humanoid Shoulder-Elbow Complex, Graduate School of the University of Notre Dame, 2007.
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The authors are thankful to Management of Graphic Era Deemed to be University, Dehradun for their motivation towards the publication of this work.
Please cite this article as: K. C. Nithin Kumar, S. Chavadaki, V. Chaudhry et al., Development of an accurate CAD model of femur bone, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.030
Contents lists available at ScienceDirect
Materials Today: Proceedings journal homepage: www.elsevier.com/locate/matpr
Development of an accurate CAD model of femur bone K.C. Nithin Kumar a,⇑, Subhash Chavadaki b, Vaishali Chaudhry a, Durgeshwar Pratap Singh a, Amir Shaikh a a b
Graphic Era (Deemed to be University), Dehradun, Uttarakhand 248002, India School of Technology, GITAM (Deemed to be University), Bengaluru, Karnataka 561203, India
a r t i c l e
i n f o
Article history: Received 15 November 2019 Accepted 4 December 2019 Available online xxxx Keywords: ITK-SNAP CT scan Segmentation Femur bone 3D cad model
a b s t r a c t This research work explores the area of biomedical image processing. A CAD model of human femur structure has been developed from computerized tomographic image taken as input data, which is further modified and segmented to form an accurate CAD model of a human femur by varying the segmentation methods and parameters, multi component model of whole femur structure is obtained as an assembly of bone. The segmentation is performed using an open source code ITK SNAP. This segmented CAD model of human femur structure can be used for engineering analysis such as static structural analysis, Model analysis and also used in manufacturing of handicapped aids, artificial bones, safety in sports equipment, design and implementing the rapid prototyping for Multi scaled model development. Ó 2019 Elsevier Ltd. All rights reserved. Selection and of the scientific committee of the 10th International Conference of Materials Processing and Characterization.
1. Introduction ITK-SNAP is an intuitive programming application that enables clients to explore three-dimensional medicinal images. ITK-SNAP is an open source software which is appropriated under general public license. It is composed in C++ language and can keep running on the various operating systems. It can read and compose different kinds of medical image formats. SNAP (Snake Automatic Partitioning) tool developed as a group programming venture in a computer science course at UNC drove by Guido Gerig amid 1999–2002. Amid 2002–03 SNAP incorporated into the NIH Insight Toolkit (ITK) and renamed as ITK-SNAP [8]. ITK-SNAP can fragment a variety of images. The images must be homogeneous, i.e. having solitary power esteem for every pixel. SNAP can be utilized with MRI, CT and PET images, but with no shading cryo-area or diffuser tensor images. SNAP can read a variety of images formats including NIFTI, DICOM, RAW, GIPL, Analyze, and Meta-Image. ITK-SNAP underpins each of the 13 formats. These configurations vary by the kind of images and measure of metadata they can store. ITK-SNAP can be utilized as a part of various modes: manual segmentation, semi-automatic segmentation, and fully automatic segmentation. The manual mode is utilized for segmentation utilizing hand forming and for tidying up the aftereffects of automatic
⇑ Corresponding author. Tel.: +918449264274. E-mail address: [email protected] (K.C. Nithin Kumar).
segmentation. Manual mode is, for the most part, utilized when structure to section is mind-boggling (little complex structures) or 2D data is accessible in the type of low-quality images. This is a tedious strategy. This is a refining technique; it refines segmented data acquired from automatic mode. In Semi-automatic mode, the client directs the computer to perform segmentation of anatomical structure. The client chooses estimations of parameters which guides active contour called ‘‘Snake”. Active contour is a bend that spread all through the structure and segments it. In automatic segmentation mode, the computer does everything. It processes the therapeutic image itself, so no compelling reason to include parameters from the client. This is a quick mode subsequently saving a considerable measure of time. Automatic mode of segmentation can’t be utilized when the quality of the medicinal image isn’t great i.e. the contrast between intensities of structures is little. Above all ITK-SNAP was produced for the clinical clients; a client who as of now utilizes a computer for image segmentation and along these lines comprehends the principal of 3D medical imaging (Fig. 1). Snake Automatic Partitioning shows segmentation with relegating labels to voxels in the input image. For example, while segmentation of cerebrum Magnetic Resonance Image, a portion of pixels in the image might be allocated label ’grey matter’, others shall be allotted label ’lateral ventricle’, and so on. This is on the user to concoct rundown of labels to be used in specific segmentation assignment. Every single voxel of input image could be doled out
https://doi.org/10.1016/j.matpr.2019.12.030 2214-7853/Ó 2019 Elsevier Ltd. All rights reserved. Selection and of the scientific committee of the 10th International Conference of Materials Processing and Characterization.
Please cite this article as: K. C. Nithin Kumar, S. Chavadaki, V. Chaudhry et al., Development of an accurate CAD model of femur bone, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.030
2
K.C. Nithin Kumar et al. / Materials Today: Proceedings xxx (xxxx) xxx
Fig. 1. Grey-scale image representations.
a solitary label. Volumetric image of labels is the result of Snake Automatic Partitioning [9–11]. The majority of the window of the SNAP consists of four boards, three out of which indicate perpendicular cuts through an image and the last one, situated at the base left demonstrates 3Dimensional perspective of segmentation. The left part of the window is involved by a tall and thin zone called control board. At the highest point, the control board contains menu bar, utilized to load and save images, for fixing alternatives, and for retrieving help system. Whatever remains of the control board consist of different buttons, sliders, and different controls, which show up and vanish contingent upon present operation mode. Notwithstanding the primary window, a few different windows appear during the session of Snake Automatic Partitioning. These windows are utilized to organize particular assignments, for example, saving and loading images, or choosing parameters in semiautomatic segmentation mode [5].
2. Literature review The survey gives thought for the advancement of CAD model of Femur Bone. Here are a couple of papers referred in this undertaking and are recorded beneath. W. Sun, B. Starley, J. Nam shows biological CAD modelling and its application. CAD is generally used for designing, modelling, manufacturing, and analysis. Development in Biomedicine and information technology makes use of CAD for different biomedical applications, especially for bone engineering where CAD based bone model gives basic data about the bone’s biological characteristics to design, modelling and manufacturing of difficult bone replacements. This paper will show some advancement in bio cad modelling and its application in designing and manufacturing bone implants [1]. Philosophy to produce bio-computer aided design models from high determination non-intrusive image, medical image process, and three-dimensional remaking method will be portrayed. Empowering advance software in helping
three-dimensional reproduction and bio-modelling advancement shall be presented. Usage of Bio-Computer Aided Designing model for depiction and portrayal of organic life structure such as morphology, heterogeneous and hierarchical and creation of bio-blueprint, and authoritative structure, and production of bioblueprint modelling will be introduced. Akshay Vishnoi, Sharad Mehta, Arpan Gupta (2014) displayed their work on bio-mechanical image processing of human elbow bone. The paper displays the technique to develop a 3D model of human elbow utilizing segmentation of Magnetic Resonance Image information utilizing automatic strategy and manual strategies [2]. Paul A. Yushkevich, Joseph Piven, Heather Cody Hazlett, Rachel Gimpel Smith, Sean Ho, James C. Well, and Guido Gerig (2006) exhibited their work on 3D dynamic shape segmentation of anatomical structure. The paper portrays the techniques and software designing rationality behind dynamic form segmentation and gives the aftereffects of approval tests performed with regards to a continuous kid Austin neuroimaging study [3]. Jayesh J. Dange, Mohammad Ali Ansari, Jitesh Bhai, Madvi (2013) manufactured a CAD model of human knee bone. The achievement of the uniquely crafted human joint substitution medical procedure relies upon correct extraction of geometric shape and size of the joint. Additionally, empowering the modelling and assembling exact prostheses structure must lead to the patient’s long-haul solace and scope of movement. The geometry of the knee joint is extricated from Computed Tomography scanning. Three- dimensional model of knee reproduction helps for superior comprehension of morphological examination and anatomical usefulness. The threedimensional strong model is developed from two-dimensional Computer Tomography images utilizing computer-aided software like PRO-Engineer, Creo, NX 6.0 and MIMIC10.0. Created model was approved by estimating different parameters such as anteroposterior diameter and lateral and medial femoral condyle width, height and width of the articular surface of the patella and so forth on CAD model. Outcomes are exhibited in an arranged manner. The philosophy can be adjusted for different joints such as shoulder, lower leg and so forth [4]. Hacene Ameddah, et. al. displayed a
Please cite this article as: K. C. Nithin Kumar, S. Chavadaki, V. Chaudhry et al., Development of an accurate CAD model of femur bone, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.030
K.C. Nithin Kumar et al. / Materials Today: Proceedings xxx (xxxx) xxx
paper in fifteenth International conference of exploratory mechanics, college of Batna, Algerie on the best possible assessment of two-dimensional medicinal images, and their fruitful segmentation to make a three-dimensional computerized model of human knee-joint, for investigation purposes. Medical images (CT, X-Ray, and MR) of human organs are broadly utilized as a part of the regular clinical praxis. More profound knowledge of the anatomical properties can be got by building a three-dimensional model from the images. Keeping in mind the end goal to appropriately assess the 2D images and furthermore the 3D objects engineers need proficient and powerful graphical and geometrical tools. The motivation behind this paper was to build up a technique for achieving solid models of the human knee to be utilized as a part of the continuous analysis of the FEA knee arrangements.3D models of distal end of the femur and tibial level were made to surmised human knee ‘‘tibio-femoral joint”. For developing these models, focuses taken from Magnetic Resonance Image were primarily handled by various strategies for edge recognizing utilizing Mat-lab tool kit image processing and afterwards changed over to point cloud having a predictable point in every cross-sectional area. Now onwards, B-splines were made from points in every crosssection. At last, B-splines were processed using the lofted command to shape a solid computer-aided model [5]. Daniela Tarnita, C. Boborelu, D. Popa (2010) from University of Oraiova, Romania, and Harvard University, USA, displayed their work on 3D virtual human Elbow joint utilizing CT images, titled ‘‘3D modelling of the complex virtual human elbow joint”. The paper introduces the calculation to acquire a 3D virtual human elbow joint utilizing CT images. For that reason, we utilized computer-aided parametric design software, which allows characterizing models with a strong abnormal state of trouble including complex 3Dshapes. The virtual biomechanical arrangement of the human elbow containing bones, ligaments, and muscles is considered utilizing the Finite Element Method and will be set up for kinematical and dynamical simulations. The 3D virtual model will be valuable for future Studies Concerning prosthesis optimization, enhancing the exhibitions of endo-prosthetic and exo-prosthetic gadgets, diverse implants and prosthetic frameworks for ordinary and neurotic circumstances, structures which are followed up on by SMA counterfeit muscles or which contain SMA components. In reality, leg and its skeleton were exposed to stresses which are diverse. It is realized that human bone stands out amongst most critical materials which are composite. A technique is introduced in the paper for considering and for the means to acquire virtual bones of the human body. For that design a computer-aided parametric software was utilized which grants to characterize models having an abnormal state of multifaceted nature. To acquire a bone cross-sectional area of the tibia bone, Computer Tomography was utilized. The threedimensional model is examined utilizing FEM, contemplating the genuine structure of bone and mechanical attributes of spongy and cortical bone, and we get stress distribution for various requesting [6]. Daniela Tarnita C Popa, D.N. Tarnita, D. Grecu (2006) from University of Oraiova exhibited a diary at Romanian diary of morphology and Embryology, titled ‘‘CAD model for a three-dimensional model of tibia bone and analysis for stresses using the FEM”. These researchers from sibiu exhibited their work at Annals of the Orcdea University, titled Research with respect to CAD modelling of the human appendages bones (femur) and pointed this to contemplate sicknesses of bones [7]. Craig M. Goeher (2007) a student graduated in ‘‘Aerospace and Mechanical engineering from Notre Dame, Indiana”, exhibited his work on designing of a Humanoid shoulder Elbow complex displaying a thesis on Dynamic Simulation of Knee joint contact during human movements. Themodels were alluring for contemplating knee contact mechanics under various loading conditions [12].
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3. Methodology In real life, human femur bone experiences most diverse stresses. It is known that human bone is one of the most important natural composite materials. The construction of a 3-D CAD model of such a bone is quite difficult, so various methodologies will be applied for its construction. To start this project we collected the relevant scientific data (consist of MRI data and CT scan images). These are 2-D images of a real bone which will be processed to obtain a 3-D CAD model. ITK-SNAP software is used to construct a 3-D model of the femur bone. This software reads MRI data and CT scan images. The model will be constructed as follows: 1. Grey-scale data input and modification. 2. Manual segmentation. 3. Semi-Automatic segmentation. Segmentation is the process of locating the structure of interest from an image and separates it from other structures of background. 3.1. Grey-scale data input and modification CT scan data and MRI images obtained from MAHENT INDRESH HOSPITAL, DEHRADUN uses as grey-scale data input. These images used as grey-scale data are in DICOM format. DICOM is a standard format used in industry. One important advantage of this format is anonymized data i.e. it does not provide any information about the source so there is no violation of privacy. 3.2. Manual segmentation In manual segmentation user outline structure of interest and model is separated manually. Manual segmentation is generally used in case of complex structures and poor images data (where the intensity of structures are very likely similar). Manual segmentation is a more time-consuming process. 3.3. Semi-automatic segmentation This presents the essential ideas driving the semi-automatic division segment of SNAP. The system behind Snake Automatic Partitioning is known as snake evolution. Snake term is utilized to allude to shut surface which shows segmentation. During snake development techniques, snake advances from an unpleasant gauge of the anatomical structure important to a nearby guess of the structure, as delineated in the figure beneath (Fig. 2). The red bend in this figure is known as the snake. It begins instated as a little hover within the ventricle, and after some time it develops, or advances, to come to fruition of the ventricle. Snake development is administered by the scientific condition that portrays the velocity of each point on the snake at a specific time. The velocity of every point depends (I) on snake state and (2) forces of images in the area of the point. There is an outline of a portion of velocities which can follow up on the snake (Fig. 3). In the given figure, velocities which are acting on a few points are indicated by yellow and blue arrows. Velocity vectors always act in the direction which is perpendicular to the snake direction. Velocity in yellow color is based on properties of image: these velocities are powerful in the area where the intensity of the image is uniform or homogenous and are weaker in the areas where there are discontinuities in the intensity of the image. We make the
Please cite this article as: K. C. Nithin Kumar, S. Chavadaki, V. Chaudhry et al., Development of an accurate CAD model of femur bone, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.030
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Fig. 2. 2-D Snake evolution.
(a)
(b) Fig. 3. (a) Velocity distribution (b) Velocity intervals.
snake assume to form around these regions, by forcing the snake to expand at a lower rate at image edges than at the homogenous regions. The velocities in blue color are based on the shape of the snake: they are shorter at points where the snake is in a straight shape and larger at points where the snake is more curved in
shape. We make the snake to follow a smooth shape by putting higher velocities with inward directions at points where the snake is sharp. By adding the velocities at a particular point on the snake, we can calculate the movement of the snake at that point. Evolving stages of the snake under the influence of velocities are given in the
Fig. 4. Setting up parameters regarding intensity regions.
Please cite this article as: K. C. Nithin Kumar, S. Chavadaki, V. Chaudhry et al., Development of an accurate CAD model of femur bone, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.030
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Fig. 5. Filling up intensity regions.
(a)
(b)
Fig. 6. (a) Final Outlook After Filling Up Intensity Regions; (b) Final outlook of the intense region.
Fig. 7. Snake initialization.
figure below. The position of the snake at next time gap is represented by the dashed lines (Fig. 4). SNAP executes two well-known 3-dimensional active contour segmentation methods – Geodesic Active Contours by (1993,
1997) Caselles et al. and Region Competition by Yuille and Zhu. In both techniques, the evolving estimate of the structure of interest is represented by at least one contour. An evolving contour is a closed surface C (u, v, t) parameterized by u, v and t (time)
Please cite this article as: K. C. Nithin Kumar, S. Chavadaki, V. Chaudhry et al., Development of an accurate CAD model of femur bone, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.030
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Fig. 8. Snake parameters.
Fig. 9. Bubble 3D structure.
(a)
(b) Fig. 10. (a) 3D Processing 1; (b) 3D Processing 2.
variables. The contour evolves according to following partial differential equation (PDE):
3.4. Preprocessing
! d ðt; u; vÞ ¼ F N dt
Once the file is imported in the software then the window appears as shown in Fig. 5. Under this section snake initialization and segmentation is carried out using intensity-based features (Fig. 6).
where F represents the sum of various forces acting on the contour in the normal direction and N is the unit normal to the contour.
Please cite this article as: K. C. Nithin Kumar, S. Chavadaki, V. Chaudhry et al., Development of an accurate CAD model of femur bone, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.030
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3.5. Filling up intensity regions
3.7. Segmentation (final)
The intensity level is set based on the requirement. The processing window must be clear enough to carry out the further steps.
This is the final section where the setting of Snake Parameters for the final segmentation is carried out (Figs. 8 and 9). This is how bubbles set up in a single plane and further expand in 3-D fashion in order to complete the 3-D CAD model, which are as follows (Figs. 10–13). Carrion model, this tells about how complete 3-D structure is being formed. This is the final 3-D CAD model of the human femur bone.
3.6. Snake initialization The snake is initialized using a circular bubble. More than one bubble can be used to initialize the snake and as the snake evolves, its scattered components can merge together as shown in the figure below (Fig. 7):
(a)
(b) Fig. 11. (a) 3D Processing 3; (b) 3D Processing 4.
(a)
(b) Fig. 12. (a) 3D Processing 5; (b) 3D Processing 6.
(a)
(b) Fig. 13. (a) 3D processing 7; (b) 3D processing 8.
Please cite this article as: K. C. Nithin Kumar, S. Chavadaki, V. Chaudhry et al., Development of an accurate CAD model of femur bone, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.030
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4. Conclusions
References
ITK-SNAP is the easiest and simplest design software that can provide the best view if the input image displayed in a preview section is split into four parts. All tools used during the operation are stored on one side of the window and can be used with a single click like – for viewing, drawing or management of layers. In this software, we can work with most of the image formats available which are DICOM, GE, GIPL, NiFTL, NRRD, VTK, Meta image and some more. To process a particular file just drag that file over the main window, this will help in understanding the medical image effectively. Depending on the results requirement, multiple layers can be created with the help of on-time updating preview section and fewer brushes can be used to improve certain areas under interest simultaneously updating in real-time. Performed segmentation (Manual/Automatic) on 2-D images to transform them into the 3-D output. Derived a 3D CAD model of Femur bone, which can be further used for Analysis purposes, for example, Creating Bone implants, Stress analysis, vibrational analysis, and Force analysis.
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Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The authors are thankful to Management of Graphic Era Deemed to be University, Dehradun for their motivation towards the publication of this work.
Please cite this article as: K. C. Nithin Kumar, S. Chavadaki, V. Chaudhry et al., Development of an accurate CAD model of femur bone, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.030