{"id":6692,"date":"2019-12-14T16:45:22","date_gmt":"2019-12-14T16:45:22","guid":{"rendered":"https:\/\/www.inspirenignite.com\/vtu\/medical-image-processing-bme-6th-sem-syllabus-for-vtu-be-2017-scheme\/"},"modified":"2019-12-14T16:45:22","modified_gmt":"2019-12-14T16:45:22","slug":"medical-image-processing-bme-6th-sem-syllabus-for-vtu-be-2017-scheme","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/vtu\/medical-image-processing-bme-6th-sem-syllabus-for-vtu-be-2017-scheme\/","title":{"rendered":"Medical Image Processing BME 6th Sem Syllabus for VTU BE 2017 Scheme"},"content":{"rendered":"

Medical Image Processing detail syllabus for Biomedical Engineering (Bme), 2017 scheme is taken from VTU<\/a> official website and presented for VTU students. The course code (17BM62), and for exam duration, Teaching Hr\/week, Practical Hr\/week, Total Marks, internal marks, theory marks, duration and credits do visit complete sem subjects post given below.<\/p>\n

For all other bme 6th sem syllabus for be 2017 scheme vtu you can visit BME 6th Sem syllabus for BE 2017 Scheme VTU Subjects<\/a>. The detail syllabus for medical image processing is as follows.<\/p>\n

Module 1 <\/h4>\n

Introduction: Background, Examples of fields that use DIP, Fundamental steps in Digital Image Processing (DIP), Components of DIP system, Image sensing and acquisition, A simple image formation model, Image sampling and quantization. Basic relationship between pixels, Colour image processing fundamentals and models. Text: Chapter 1, 2.3, 2.4, .2.5, 6.1, 6.2\n<\/p>\n

Module 2 <\/h4>\n

For complete syllabus and results, class timetable and more pls download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n

Module 3 <\/h4>\n

Image Enhancement In Frequency Domain: Background, 2D-Discrete Fourier Transform and its Inverse, Basic properties of the 2D-Discrete Fourier Transform, Basicsof filtering in the frequency domain. Image smoothing using frequency domain filters – Ideal lowpass filters, Butterworth lowpass filters, Gaussian lowpass filters; Image sharpening using frequency domain filters – Ideal highpass filters, Butterworth highpass filters, Gaussian highpass filters, Homomorphic filtering. Text: 4.1, 4.2, 4.5.5, 4.6, 4.7,4.8, 4.9.\n<\/p>\n

Module 4 <\/h4>\n

Image Restoration: Model of the Image degradation\/restoration process, Noise models, Restoration using spatial filtering: Mean filters, Order statistic filters – Median filter, Min and Max filters, Midpoint filter. Image Compression: Fundamentals, Image compression models, Basic compression methods -Huffman coding, Arithmetic coding, LZW coding, Run-length coding. Text: 5.1, 5.2, 5.3.1, 5.3.2, 8.1, 8.2.1, 8.2.3, 8.2.4, 8.2.5.\n<\/p>\n

Module 5 <\/h4>\n

For complete syllabus and results, class timetable and more pls download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n

Course Outcomes:<\/h4>\n

After studying this course, students will be able to,<\/p>\n

    \n
  1. Define the general terminology of digital image processing.<\/li>\n
  2. Identify the need for image transforms and their types both in spatial and frequency domain.<\/li>\n
  3. Identify different types of image degradation and apply restoration techniques.<\/li>\n
  4. Describe image compression models and learn image compression techniques.<\/li>\n
  5. Explain and apply various methodologies for image segmentation.<\/li>\n
  6. Implement image processing and analysis algorithms.6<\/li>\n<\/ol>\n

    Note: It is suggested to give assignments \/ hands-on-experience on the above image processing concepts using Matlab \/ C programming on medical images like x-ray \/ CT \/ MRI.\n<\/p>\n

    Question paper pattern:<\/h4>\n