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Applying Blob Detection in Slicer-3D by Chaithanya UNIVERSITY OF COLORADO AT COLORADO SPRINGS APPLYING BLOB DETECTION IN SLICER-3D BY CHAITHANYA KUMAR CHAVA 1 | P a g e UNIVERSITY OF COLORADO AT COLORADO SPRINGS A project report submitted to the Graduate Faculty of the University of Colorado at Colorado Springs in the partial fulfillment of the requirements for the degree of Master of Science in Computer Science Engineering Department of Computer Science 2017 This report for the Master of science degree By Chaithanya Kumar Chava has been approved for the Department of Computer Science By ______________________________________________________________________________ Advisor: Dr. Sudhanshu Semwal Graduate advisor for MS computer science: Focus GMI program. ______________________________________________________________________________ Committee member: Dr. Edward Chow Professor of computer science at University of Colorado at Colorado Springs ______________________________________________________________________________ Committee member: Dr. T.S. Kalkur Professor of Electrical and Computer Engineering at University of Colorado at Colorado Springs _____________________________ Date 2 | P a g e UNIVERSITY OF COLORADO AT COLORADO SPRINGS ACKNOWLEDGEMENTS This project would not have been possible without the kind support, patience and help of many individuals. First and foremost is my academic advisor, Professor Sudhamshu Semwal, for accepting me into his group. His guidance and constant supervision and providing necessary information regarding the project helped me a lot in successfully accomplishing my goals for the completion of project. Additionally, I would like to thank my committee members Professor Edward Chow and Professor T.S. Kalkur for their interest in my work. My sincere gratitude for their insightful suggestions and encouragement. Beside my committee, I would like to thank my parents, my sister and brother-in-law for their continuous support and encouragement throughout my Master’s. I would like to thank all the people who contributed in some way to the work described in this project. 3 | P a g e UNIVERSITY OF COLORADO AT COLORADO SPRINGS Abstract The main aim of this project is to implement blob detection technique in Slicer3D, a medical application available as opensource. Implementing the blob detection algorithm may allow, we identification of the infected region in the image. Slicer3D software is being used mainly on image processing algorithms. Slicer3D has been used in the area cardiovascular, neuro surgery, prostate cancer and multiple sclerosis applications. Slicer3D works perfectly with these LAYER- HEIGHT, SHELL THICKNESS, RETRACTION, FILL DENSITY, PRINT SPEED, SUPPORTS, PLATFORM ADHESION TYPE and INITIAL LAYER THICKNESS. Slicer3D has all the MRI, X-ray scanned, and other images available in its repository. When there is an MRI scanned image of brain tumor available, one could run the blob detection algorithm by import this image. Perhaps an infected area of the image can be isolated by our algorithm. Requirements for this project are C-Make, GIT, QT, SilkSVN and 3D Slicer. Slicer3D is one of the very useful software for medical image analysis, and has powerful plugin capabilities for adding algorithms. Both development, and maintenance use standard operating procedures and well-documented. Moreover, Slicer3D can be used on multiple operating systems. 4 | P a g e UNIVERSITY OF COLORADO AT COLORADO SPRINGS Table of Contents Table of Contents Abstract: ………………………………………………………………………………………………………………………………… .4 1. Introduction: ……………………………………………………………………………………………………………………………7 2. Setting up the slicer3D……………………………………………………………………………………………………………..8 3. Existing Techniques Survey: …………………………………………………………………………………………………....10 3.1 Algorithms: ………………………………………………………………………………………………………………...10 3.1.1 LOG (Laplacain of gaussian) ……………………………………………………………………………10 3.1.2 Blob Detection……………………………………………………………………………………………….11 3.2 Goal of the project……….…………………………………………………………………………………………….11 3.3 Expected Output ……………………………………………………………………………………………………….12 4. Process and Implementation …………………………………………………………………………………………………12 4.1 Process Flow: …………………..……………………………………………………………………………………….12 4.2 Implementation: ……………………………………………………………………………………………………….13 4.2.1 Preparation: ………………………………………………………………………………………………….13 4.2.2 Laplacian: …………………………..………………………………………………………………………...14 4.2.3 Implementation of blob detection ………………………………………………………………..15 4.2.4 Code Explanation ………………………………………………………………………………………….17 4.2.5 Filters parameters and their work ……………………………………………………………….17 5. Conversion of images to gray scaled images …………………………………………………………………………..18 6. Accuracy …………………………………………………………………………………………………………………………………19 7. Future Works …………………………………………………………………………………………………………………………20 USERS MANUAL ……………………………………………………………………………………………………………………..21 5 | P a g e UNIVERSITY OF COLORADO AT COLORADO SPRINGS CODE …………………………………………………………………………………………………………………………………….21 REFERENCES ………………………………………………………………………………………………………………………… 35 FIGURES: Figure 1 Eco system …………………………………………………………………………………………………………… 7 Figure 2 Process Flow ………………………………………………………………………………………………………… 12 Figure 3 ExtOpenCv……………………………………………………………………………………………………………. 13 Figure 4 Sample Data…………………………………………………………………………………………………………. 14 Figure 5 Filter ……………………………………………………………………………………………………………………..14 Figure 6 Laplacian applied……………………………………………………………………………………………………15 Figure 7 Blob Detected………………………………………………………………………………………………………. 16 Figure 8 Test Laplacian …………………………………………………………………………………………………… 19 Figure 9 Test Blob Detection ………………………………………………………………………………………… 19 6 | P a g e UNIVERSITY OF COLORADO AT COLORADO SPRINGS 1. INTRODUCTION: To advance the role of imaging as a biomarker of treatment, the National Cancer Institute (NCI) launched the Quantitative Imaging Network (QIN) initiative [1]. In this paper, we are using Slicer3D. Slicer3D is open source free software. It is an extensible application for medical image computing and visualization. There are different image processing algorithms that can be used in 3D slicer. Our algorithm called Gaussian blob detection and Laplacian filter. These algorithms are already used to visualize the images like MRI and X-RAY Scans and planning of possible treatment. As this type of image processing algorithms are not yet approved to use in an actual surgical/hospital environment. Many researchers are working on this image processing algorithms to make the results accurate and reliable to bring it to real time practice. 3D Slicer is one image processing software which has more increased usage in last decade. 3D Slicer is a cross platform but still it needs certain requirements. [1] Figure 1 Eco System 3d slicer follows a modular and layered approach. At the below level are the libraries which are not included in slicer. And the above level are the libraries that provides higher functionalities. [1] 7 | P a g e UNIVERSITY OF COLORADO AT COLORADO SPRINGS 2. SETTING UP THE SLICER3D First we need to download the slicer codes. After downloading we need to clone the GIT Hub Repository using Git (2) Bash (create a folder). git clone git://github.com/Slicer/Slicer.git Once the cloning completes, the codes will be downloaded to the folder which is created. Setting up the developer Environment: In GIT Bash opens the Slicer Folder that is downloaded and enter the following code ./Utilities/SetupForDevelopment.sh Which will prompt you to enter your Personal Information (Name and E-mail Address) 8 | P a g e UNIVERSITY OF COLORADO AT COLORADO SPRINGS Configure the git and SVN Bridge By using the following set of commands in GIT Bash. cd Slicer git svn init http://svn.slicer.org/Slicer4/trunk git update-ref refs/remotes/git-svn refs/remotes/origin/master git checkout master (2) git svn rebase (2) This will rebuild the code by connecting the bridge between SVN and GIT. By doing this all the requirements for starting the slicer is setup. 9 | P a g e UNIVERSITY OF COLORADO AT COLORADO SPRINGS 3. EXISTING TECHNIQUES SURVEY: 3.1 ALGORITHMS: Image processing algorithms are the algorithms which are used to create, process, communicate and display digital images. This may come under several types like removing noise and reducing blur in the images. For this project, we are using Laplacian of Gaussian and blob detection. 3.1.1 LOG (Laplacian of Gaussian): [12], [13], [14], [15] LOG is a filter that is applied to an image. The process of Laplacian starts by taking an image and applying Laplacian filter to it. Let’s say the filtered image as X and original Image as O. The result L by adding the Original image and the filter image is the output for laplacian of Gaussian. And the Laplacian of Gaussian is applied to a noise image or blur image for getting a reduced bl ur or noisy image. And this is done by reducing the contrast of the image. For example, see figure below O-------------- ORIGINAL IMAGE X---------------FILTERED IMAGE 10 | P a g e UNIVERSITY OF COLORADO AT COLORADO SPRINGS L---------------RESULT IMAGE 3.1.2 BLOB DETECTION: Blob detection methods or algorithms are used to detect regions in an image that differ in properties of image. Some of these properties of an image are: color, shape, size, when compared to their surroundings. Blob detection is used because important changes in information can be obtained accurately when compared to edge detectors. This blob detection can be implemented by using a property and using an equivalent function. 3.2 GOAL OF THE PROJECT: The main aim of this project is to create blobs in an image which
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