BM3652 Medical Image Processing Syllabus:
BM3652 Medical Image Processing Syllabus – Anna University Regulation 2021
COURSE OBJECTIVES
The objective of this course is to enable the student to
• Learn the fundamental concepts of medical Image Processing techniques.
• Understand the concepts of various image intensity transformation and filtering operations.
• Be familiar in the techniques of segmentation and restoration of medical images.
• Gain knowledge in medical image registration and visualization.
• Be familiar with the application of medical image analysis.
UNIT I FUNDAMENTALS OF MEDICAL IMAGE PROCESSING AND TRANSFORMS
Overview of Image Processing system and human Visual system- Image representation – pixel and voxels, Gray scale and color models- Medical image file formats- DICOM, ANALYZE 7.5, NIFTI and INTERFILE- Discrete sampling model and Quantization- Relationship between the pixels, Arithmetic and logical operations- Image quality and Signal to Noise ratio- Image Transforms- 2D DFT, DCT, KLT. Interpret the basics of image models, Digitization of images and the transformations of medical images using Matlab.
UNIT II ENHANCEMENT TECHNIQUES
Gray level transformation- Log transformation, Power law transformation, Piecewise linear transformation. Histogram processing- Histogram equalization, Histogram Matching. Spatial domain Filtering-Smoothing filters, sharpening filters. Frequency domain filtering- Smoothing filters, Sharpening filters- Homomorphic filtering -Medical image enhancement using Hybrid filtersPerformance measures for enhancement techniques. Experiment with various filtering techniques for noise reduction and enhancement in medical images using Matlab.
UNIT III SEGMENTATION AND RESTORATION TECHNIQUES
ROI definition -Detection of discontinuities–Edge linking and boundary detection – Region based segmentation- Morphological processing, Active contour models. Image Restoration- Noise models– Restoration in the presence of Noise – spatial filtering, Periodic noise reduction by frequency domain filtering- linear position- Invariant degradation- Estimation of degradation function, Inverse filter, Weiner filtering. Analyze the segmentation techniques to extract the region of interest and restoration of degraded images using Matlab.
UNIT IV REGISTRATION AND VISUALISATION
Registration–Rigid body transformation, principal axes registration, and feature based. Visualisation-Orthogonal and perspective projection in medicine, Surface based rendering, Volume visualization in medical image. Explain the significance of registration of various imaging modalities and appraise the concepts of image visualization in healthcare using Matlab
UNIT V APPLICATIONS OF MEDICAL IMAGE ANALYSIS
Medical Image compression- DCT and Wavelet transform based image compression, Preprocessing of medical images -Retinal images, Ultrasound –liver, kidney, Mammogram. Segmentation of ROI -blood vessels, lesions, tumour, lung nodules, feature extraction- shape and texture, Computer aided diagnosis system – performance measures (confusion matrix, ROC, AUC).
TOTAL : 75 PERIODS
COURSE OUTCOMES
Upon successful completion of the course, students will be able to
CO1: Explain and apply the fundamental concepts of image processing techniques for the analysis of medical images.
CO2: Identify and apply suitable filtering and intensity transformation techniques for given medical applications.
CO3: Identify and segment the Region of Interest from the given medical image.
CO4: Explore and apply current research in registration and visualization for medical image analysis.
CO5: Explain and apply the image compression techniques.
CO6: Design and evaluate the use of image processing fundamentals in healthcare applications, as well as their impact on health and society, and any underlying ethical issues, then communicate effectively through reflections, reports, and presentations (Target CO).
TEXT BOOKS
1. Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, Pearson Education, 3rd edition, 2016.
2. Isaac N. Bankman, Handbook of Medical Image Processing and Analysis, 2nd Edition, Elsevier, 2009.
3. Wolfgang Birkfellner, Applied medical Image Processing: A Basic course, CRC Press, 2011
REFERENCES
1. Atam P.Dhawan, Medical Image Analysis, Wiley-Interscience Publication, NJ, USA 2003
2. Rangaraj M. “Rangayyan, Biomedical Image Analysis”, 1st Edition, CRC Press,Published December 30, 2004.
3. Joseph V.Hajnal, Derek L.G.Hill, David J Hawkes, “Medical image registration”, Biomedical Engineering series, CRC press,2001
4. Milan Sonka, Image Processing, Analysis And Machine Vision, Brookes/Cole, Vikas Publishing House, 2nd edition, 1999.
5. Anil Jain K, Fundamentals of Digital Image Processing, PHI Learning Pvt. Ltd., 2011.