| Announcements | Medical Image Analysis | Guest Lectures |
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13 Apr. 2015 :: Term Project Presentation in Class
30 Mar. 2015 :: Assignment 10 11 Feb. 2015 :: Class Test (10.02.15) Solution key |
EE61008
Subject Type: Elective LTP: 3-1-0 | Credits: 4 Location: N232, Dept. Electrical Engineering Time: Slot U / Mon (2:30 PM - 4:25 PM) + Tue (1:30 PM - 3:25 PM) Instructor: Dr. Debdoot Sheet Tutor: Biswajoy Ghosh |
7 Apr. 2015
Image Analysis for Magnetic Resonance Imaging. Prof. Nirmalya Ghosh Department of Electrical Engineering, IIT Kharagpur |
| Why this subject? | |
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| You get to learn about current technology in processing and analysis of medical images; a rapidly growing industry expected to reach $2.4 billion by 2017. If you are looking forward to a career in medical imaging instrument and softwares design, medical imaging, medical visualization, medical robotics and augmented reality, this is the key subject you should enroll for.
The best parts just do not end here. A mix of hands-on tutorials coupled with lectures provide you with a balanced exposure to nuances of medical image analysis. Text books and reading materials are completely electronic, online and available as web-browser and smatphone apps. Experience advanced learning as a fun within classroom and beyond with interactive graphics and online contents from the comfort of your smartphone or tab. Prerequisite: Digital Image Processing |
Medical image registration Organ localization Organ segmentation Medical visualization Virtual anatomy Digital angiography Optical and ultrasonic despeckling 3D optical microscopy Digital pathology Computational imaging |
| 1. Basic human anatomy | H. Gray, Anatomy of the human body, 1918. |
| 2. Whole body imaging | K. D. Toennies, "Digital image acquisition", Guide to Medical Image Analysis, pp. 21-82, 2012.
K. Doi, "Diagnostic imaging over the last 50 years", Phys. Med. Biol. vol. 51, pp. R5–R27, 2006. K. Kamm, "Fundamental aspects of digital imaging", Phys., Med. Imaging, pp. 3-22, 2007. |
| 3. Mesoscopic imaging | S. Hu, K. Maslov, L. V. Wang, "Three-Dimensional Optical-Resolution Photoacoustic Microscopy", Biomed. Opt. Imaging Tech., pp. 55-77, 2013.
M. Fatemi, et al, "Vibro-acoustic tissue mammography", IEEE Trans. Med. Imaging, vol. 21, no. 1, pp. 1-8, 2002. |
| 4. Microscopic imaging | M. W. Davidson and M. Abramowitz, "Optical microscopy", Encyclopedia of Imaging Science and Technology, 2002.
Illustrated resources from MicroscopyU.com |
| 5. Medical picture archival | K. D. Toennies, "Image storage and transfer", Guide to Medical Image Analysis, pp. 83-109, 2012.
A. Wong and S.L. Lou, "Ch. 50: Medical Image Archive, Retrieval, and Communication", Handbook Med. Image Process., Anal. pp. 861-873, 2008. |
| 1. Shape modelling | K. D. Toennies, "Detection and Segmentation by Shape and Appearance", Guide to Medical Image Analysis, pp. 333-378, 2012.
A. Frangi, et al, "Multiscale vessel enhancement filtering", Proc. MICCAI, pp. 130-137, 1998. |
| 2. Region growing and clustering | K. D. Toennies, "Segmentation: Principles and Basic Techniques", Guide to Medical Image Analysis, pp. 171-209, 2012.
K. D. Toennies, "Classification and Clustering", Guide to Medical Image Analysis, pp. 379-412, 2012. |
| 3. Texture analysis | M. Petrou, "Texture in Biomedical Images", Biomedical Image Processing, pp. 157-176, 2011. |
| 4. Graph cuts and Random walks | K. D. Toennies, "Segmentation as a Graph Problem", Guide to Medical Image Analysis, pp. 235-259, 2012.
Graph analysis and Random Walks Matlab Toolbox @ Leo Grady |
| 5. Active contours and level sets | K. D. Toennies, "Active Contours and Active Surfaces", Guide to Medical Image Analysis, pp. 261-297, 2012.
A. Alfiansyah, "Deformable Models and Level Sets in Image Segmentation", Medical Image Processing, pp 59-87, 2011. Chan-Vese's active contour without edges @ MatlabCentral FileExchange |
| 6. Supervised and unsupervised learning | K. D. Toennies, "Segmentation in Feature Space", Guide to Medical Image Analysis, pp. 211-233, 2012.
A. Criminisi, J. Shotton (Eds.), Decision Forests for Computer Vision and Medical Image Analysis, Springer, 2013. J. Kalpathy-Cramer, H. Müller, "Systematic Evaluations and Ground Truth", Biomedical Image Processing, pp. 497-520, 2011. K. D. Toennies, "Validation", Guide to Medical Image Analysis, pp. 413-442, 2012. |
| 1. X-ray image formation | M. A. Haidekker, "X-ray projection imaging", Medical Imaging Technology, pp. 13-35, 2013. |
| 2. Digital angiography | O. Tankyevych, et al, "Angiographic Image Analysis", Medical Image Processing, pp. 115-144, 2011. |
| 3. Digital mammography | Y. Songyang and L. Guan, "A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films", IEEE Trans. Med. Imaging, vol. 19, no. 2, pp. 115-126, 2002. |
| 4. MRI image formation | M. A. Haidekker, "Magnetic resonance imaging", Medical Imaging Technology, pp. 67-96, 2013. |
| 5. CT image formation | M. A. Haidekker, "Computed tomography", Medical Imaging Technology, pp. 37-53, 2013.
A. C. Kak and M. Slaney, "Algebraic reconstruction algorithms", Principles of Computerized Tomographic Imaging, pp. 275-296, 1988. |
| 6. Medical image and volume registration | K. D. Toennies, "Registration and Normalization", Guide to Medical Image Analysis, pp. 299-331, 2012.
J. Tian, Y. Wang, X. Dai, X. Zhang, "Medical Image Processing and Analysis", Molecular Imaging, pp. 415-469, 2013. |
| 7. SPECT, PET and CT/MRI hybrid imaging | E. Even-Sapir, et al, "Hybrid Imaging (SPECT/CT and PET/CT)—Improving the Diagnostic Accuracy of Functional/Metabolic and Anatomic Imaging", Seminars in Nuclear Medicine, vol. 39, no. 4, pp. 264–275, 2009.
S. R. Cherry, "Multimodality Imaging: Beyond PET/CT and SPECT/CT", Seminars in Nuclear Medicine, vol. 39, no. 5, pp. 348–353, 2009. |
| 1. Ultrasonic image formation | M. Ali, D. Magee and U. Dasgupta, "Signal Processing Overview of Ultrasound Systems for Medical Imaging", Texas Instruments White Paper, no. SPRAB12, Nov., 2008.
M. A. Haidekker, "Ultrasound imaging", Medical Imaging Technology, pp. 97-110, 2013. B-mode ultrasound image simulator @ MatlabCentral FileExchange |
| 2. Speckle reduction and image compounding | Z. Tao, "Evaluation of Four Probability Distribution Models for Speckle in Clinical Cardiac Ultrasound Images", IEEE Trans. Med. Imaging, vol. 25, no. 11, pp. 1483-1491, 2006.
D. Kaplan and Q. Ma, "On the statistical characteristics of log-compressed Rayleigh signals: theoretical formulation and experimental results", Proc. Ultrasonics Symp., pp. 961-964, 1993. A. Milkowski, et al, "Speckle Reduction Imaging", GE Medical Systems Ultrasound. C. P. Loizou, et al, "Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery", IEEE Trans. Ultrasonics, Ferroelectrics, Freq. Control, vol. 52, no. 10, pp. 1653-1669, 2005. |
| 3. Optical coherence tomography image formation | J. Fujimoto and W. Drexler, "Introduction to Optical Coherence Tomography", Optical Coherence Tomography, pp. 1-45, 2008.
P. E. Andersen, et al, "Modeling Light–Tissue Interaction in Optical Coherence Tomography Systems", Optical Coherence Tomography, pp. 73-115, 2008. |
| 4. Frequency domain image sensing and despeckling | J. F. de Boer, "Spectral/Fourier Domain Optical Coherence Tomography", Optical Coherence Tomography, pp. 147-175, 2008.
R. A. Leitgeb and M. Wojtkowski, "Complex and Coherence Noise Free Fourier Domain Optical Coherence Tomography", Optical Coherence Tomography, pp. 177-207, 2008. D. L. Marks, T. S. Ralston and S. A. Boppart, "Data Analysis and Signal Postprocessing for Optical Coherence Tomography", Optical Coherence Tomography, pp. 405-426, 2008. D. Sheet, et al, "Visual importance pooling for image quality assessment of despeckle filters in Optical Coherence Tomography", Proc. Int. Conf. Sys., Med., Biol., pp. 102-107, 2010. |
| 5. Cardiovascular and ophthalmic imaging | G. J. Tearney, I. -K. Jang and B. E. Bouma, "Imaging Coronary Atherosclerosis and Vulnerable Plaques with Optical Coherence Tomography", Optical Coherence Tomography, pp. 1083-1101, 2008.
W. Drexler and J. G. Fujimoto, "Retinal Optical Coherence Tomography", Optical Coherence Tomography, pp. 983-1045, 2008. M. D. Abramoff, et al, "Retinal Imaging and Image Analysis", IEEE Rev. Biomed. Engg., vol. 3, pp. 169-208, 2010. |
| 1. Analysis of lens based microscopy images | M. N. Gurcan "Histopathological Image Analysis: A Review", IEEE Rev. Biomed. Engg., vol. 2, pp. 147-171, 2009. |
| 2. Phase contrast and dark field image analysis | G. Gonzalez, et al, "Automated quantification of morphodynamics for high-throughput live cell time-lapse datasets", Proc. ISBI, pp. 664-667, 2013. |
| 3. Digital fluorescence and non-linear microscopy | Y. Sun, A. Periasamy, "Fluorescence Microscopy Imaging in Biomedical Sciences", Biomed. Opt. Imaging Tech., pp. 79-110, 2013.
S. Rehman, C. J. R. Sheppard, "Multiphoton Imaging", Biomed. Opt. Imaging Tech., pp. 233-254, 2013. |
| 4. 3D optical microscopy | H. Garud, et al, "Volume visualization approach for depth-of-field extension in digital pathology", Proc. Int. Cong. Image, Signal Process., pp. 335-339, 2011. |
| 1. Dynamic range mapping and 3D visualization | D. Bartz, B. Preim, "Visualization and Exploration of Segmented Anatomic Structures", Biomedical Image Processing, pp. 379-401, 2011.
Q. Zhang, et al, "Medical Image Volumetric Visualization: Algorithms, Pipelines, and Surgical Applications", Medical Image Processing, pp. 291-317, 2011. |
| 2. Organ localization in CT-MRI | O. Pauly, et al, "Fast Multiple Organ Detection and Localization in Whole-Body MR Dixon Sequences", Proc. MICCAI, pp. 239-247, 2011.
Download presentation slides for the above paper from MICCAI 2011. R. Donner, et al, "Localization of 3D Anatomical Structures Using Random Forests and Discrete Optimization", Proc. Med. Comp. Vis. Recog. Tech., Appl., pp. 86-95, 2011. |
| 3. Ultrasonic tissue characterization | A. Katouzian, S. G. Carlier, A. F. Laine, "Methods in Atherosclerotic Plaque Characterization Using Intravascular Ultrasound Images and Backscattered Signals", Atherosclerosis Disease Management, pp. 121-152, 2011.
A. Katouzian, et al, "Iterative Self-Organizing Atherosclerotic Tissue Labeling in Intravascular Ultrasound Images and Comparison With Virtual Histology", IEEE Trans. Biomed. Engg., vol. 59, no. 11, pp. 3039-3049, 2012. D. Sheet, et al, "Joint learning of ultrasonic backscattering statistical physics and signal confidence primal for characterizing atherosclerotic plaques using intravascular ultrasound", Med. Image Anal., vol 18, no. 1, pp. 103–117, 2014. |
| 4. Optical tissue characterization | G. J. Ughi, et al, "Automatic characterization of neointimal tissue by intravascular optical coherence tomography", J. Biomed. Optics, vol. 19, no. 2, pp. 021104-1-8, 2014.
D. Sheet, et al, "In situ histology of mice skin through transfer learning of tissue energy interaction in optical coherence tomography", J. Biomed. Optics, vol. 18, no.9, pp. 090503-1-3, 2013. D. Sheet, et al, "Transfer learning of tissue photon interaction in optical coherence tomography towards in vivo histology of the oral mucosa", Proc. ISBI, pp. 1389-1392, 2014. G. van Soest, et al, "Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging", J. Biomed. Optics, vol. 15, no. 1, pp. 011105-1-9, 2010. |
| 5. Clinical workflow integration | H. K. Huang, et al, "PACS-Based Computer-Aided Detection and Diagnosis", Biomedical Image Processing, pp. 455-470, 2011.
M. A. Haidekker, "Trends in medical imaging technology", Medical Imaging Technology, pp. 111-119, 2013. R. Liang, "Multimodal Biomedical Imaging Systems", Biomed. Opt. Imaging Tech., pp. 297-349, 2013. |
| BioDigital Human | Web browser app that can be handy while understanding human anatomy for medical image analysis |
| MeVisLab | Medical image processing, analysis and visualization SDK. |
| Python(x,y) | Python (2.7) based numerical computations, data analysis and data visualization, Qt graphical user interfaces and Spyder interactive scientific development environment. Can be of great use to develop image analytics tools and integrate then with MeVis projects or Android apps. |
| Android Studio and SDK | Developer studio, IDE and SDK if you are interested to develop Android apps as part of your mini-project. |
| Tortoise SVN | Version control utility for Windows implemented using the standard SVN. Helpful for managing (big / multiple file) medical software development projects. |
| MicroDicom viewer | Handy software utility to quickle view DICOM files |
| MikTeX | Up-to-date implementation of TeX/LaTeX and related programs for Windows (all current variants). Wiki for writing Hello world! using LaTeX. Template for submitting 1-Page report on the mini-project. |
| Grand Challenges in Biomedical Image Analysis | Hub for Mini-Project ideas. Collection of the grand challenges in medical image analysis hosted at ISBI, MICCAI and other conferences and symposia. Good source for you to find a challenging research problem or to look for a revenue potenial product buildup. |
| Challenges @ ISBI 2015 | Collection of challenges in medical image analysis hosted at the International Symposium on Biomedical Imaging 2015, Brooklyn, USA. |
| Mammographic Image Analysis | Collection of mammography images provided by Mammographic Image Analysis Society (MIAS). |
| MedicalImageAnalysis @ MicrosoftResearch Cambridge | A good collection of activities in medical image analysis product and technology development. |
| DRIVE: Digital Retinal Images for Vessel Extraction | A standard dataset of 40 color fundus images for comparative studies on segmentation of blood vessels in retinal images. |
| Drishti-GS Dataset | Dataset of color fundus retinal images for Glaucoma detection. |
| Farsiu's Dataset @ Duke Univ. | Collection of multiple datasets of ophthalmic OCT speckle images for tissue characterization, denoising, super-resolution and mosaicing. |
| MIT OCW Linear Algebra | Revise your Linear Algebra with video lectures and resources by Prof. Gilbert Strang. |
| Assignment 1 (Due: 6 Jan. 2015) | Create a MeVis Lab project to view the 3D OCT data dicom file and store its multi-projection views with different dynamic range. |
| Assignment 2 (Due: 13 Jan. 2015) | Download and solve in class. Class performance would be graded. |
| Assignment 3 (Due: 19 Jan. 2015) Take home assignment. |
Develop a skull stripper algorithm for Brain CT and implement it in Matlab / Python. Please make it independent of any variation in average image intensity. Demonstrate it's performance on a 16-bit CT TS-slice of the brain available in DICOM format. Demonstrate in class on due date for grading. |
| Assignment 4 (Due: 19 Jan. 2015) | Download and solve in class. Class performance would be graded. |
| Assignment 5 (Due: 20 Jan. 2015) Take home assignment. |
Develop a chroma clustering based nucleus segmentation algorithm for brightfield optical microscopy images and implement it in Matlab / Python. Please make it independent of any variation in average image intensity and color appearance. Demonstrate it's performance on Image1 and Image2 of FNAC slides. Demonstrate in class on due date for grading. |
| Assignment 6 (Due: 27 Jan. 2015) Take home assignment. |
Develop a (a) k-nearest neighbor search (k=10, k=5, k=2) (b) minimum distance search (MDC), (c) Baye's decision theory (d) chroma clustering and (e) Frangi's vesselness measure based retinal vessel segmentation algorithm for color fundus images and implement them in Matlab / Python. Please make the algorithms independent of any variation in average image intensity and color appearance. Employ contrast enhancement and choma conversions if necessary. Demonstrate it's performance on Sample Dataset. Demonstrate in class on due date for grading. |
| Assignment 7 (Due: 3 Feb. 2015) Take home assignment. |
Develop a random walks based retinal vessel segmentation method. Use the skeleton of the ground truth of the image as initial seeds for the vessel region. Dilate the vessel map in ground truth and then detect the edge and use it as background seeds. |
| Assignment 8 (Due: 9 Feb. 2015) Take home assignment. |
Develop an active contour based object boundary segmentation algorithm. |
| Assignment 9 (Due: 10 Mar. 2015) Solve in Class. |
Radon transform, Filtered backprojection for CT Recon. |
| Assignment 10(Due:30 Mar. 2015) Solve in Class. |
Download and solve in class. Class performance would be graded. |
| Coming up! |
| 16 Mar. 2015 | Introduction to Medical Image Registration. (Slides)
Sailesh Conjeti, PhD Candidate, TU Munich, Germany |
| 6 Jan. 2015 | Ultra-High Resolution and Sensitivity Molecular Imaging Platforms for Quantification of Cellular Processes.
Ipshita Chakraborty, Applications Scientist, MILabs, The Netherlands |
| The aim is to teach students advanced technology in processing and analysis of medical images. It would be beneficial to students opting for specialization in medical imaging instrument design, medical imaging, medical visualization, medical robotics and augmented reality, who can use the gained skills in order to develop newer technological innovations and regularize their high-throughput clinical translation and usage. | On completion of the course, a student would be able to:
1. Explain and discuss the scientific principles of medial image formation, tissue energy interaction as basis for different imaging modalities, inter-relation between image appearance and tissue properties, quantitative medicine, multi-scale and multi-resolution relation in medical diagnostics, medical visualization and augmented reality. 2. Demonstrate the ability of analyzing medical images using their foundations of linear algebra, graph based and machine learning based approaches. 3. Design and develop new techniques for improving clinical workflow with incorporation of medical image analysis in regular clinical usage. |
