1. Signal preprocessing: Fundamentals of digital signal processing, Basics of
digital filtering, z transform, design of FIR, IIR, and Integer filter, Data reduction
techniques, Feature extraction of biomedical signals.
2. Image Processing: Image storage and display operation properties of digital
image. Image enhancement and restoration techniques: statistical features of
images and their application in image enhancement, histogram equalization,
Correlation and convolution, and application of convolution for designing of
smoothing filters, sharpening filters, gradient and Laplacian and zero crossing edge
detectors.
3. Image Segmentation techniques: segmentation of image by threshold, Edge
based and Region based techniques.
4. Image analysis: Features of images based on statistical, spectral and syntactical
properties of images and extraction of features. Application of features for
segmentation and analysis.
Course Code: BM-502
Course Name: Biomedical Signal & Image Processing
Overview of the subject:
It is expected that after the completion of the course, students will be able to
understand various biomedical image processing techniques such as noise reduction,
enhancement, restoration, segmentationm, Analysis (AI) etc. They will also be able to
understand the different medical imaging modalities.
L-T-P Credits: 09
Name of the Teacher: Dr. Neeraj Sharma
Teaching Assistants: Mr. Balendra
Plan for the whole semester (week-wise):
S.No Topic
week-wise
1.
Fundamentals of Digital signal processing, basics of digital filtering
and z- transforms.
1st Week
2.
Design of FIR filters
2nd Week
3.
Design of IIR and Integer filters
3rd Week
4.
Data reduction techniques and feature extraction of Biomedical
Signals.
4th Week
5.
Concepts of medical image processing and Properties of medical
images.
5th Week
6.
Image storage, display operations and Image enhancement and
restoration techniques.
6th Week
7.
Statistical features of images and their application in image
enhancement, Histogram equalization and adaptive histogram
equalization.
7th Week
8.
Correlation and convolution, and application of convolution for
designing of smoothing filters, sharpening filters, Laplacian and zero
crossing edge detectors.
8th Week
9.
Introduction to image segmentation, Segmentation of image by
thresholding
9th week
10.
Edge based segmentation
10th Week
11.
Region based segmentation
11th Week
12.
Features of images based on statistical properties
12th Week
13.
Features of images based on spectral and syntactical properties and
Feature extraction and reduction methods, Analysis (AI).
13th week
Weightages
1. Home assignments: 20
2. Mid semester Exam: 30
3. Final exams: 50
Book list

Text books:
1. BIOMEDICAL DIGITAL SIGNAL PROCESSING by WILLIS J. TOMPKINS Editor University of
Wisconsin-Madison, : PRENTICE HALL, Upper Saddle River, New Jersey 07458.
2. Gonzalez, Rafael C., and Richard E. Woods. "Image processing." Digital image
processing.
3. Robb, Richard A. Biomedical imaging, visualization, and analysis. John Wiley &
Sons, Inc., 1999.
4. DIGITAL IMAGE PROCESSING by WILLIAM K. PRATT PixelSoft, Inc. Los Altos, California
WILEY-INTERSCIENCE A John Wiley & Sons, Inc., Publication.

Reference books
1. Bankman, Isaac, ed. Handbook of medical image processing and analysis.
academic press, 2008.
2. Sonka, Milan, Vaclav Hlavac, and Roger Boyle. Image processing, analysis,
and machine vision. Cengage Learning, 2014
Home Assignment-1:
Q1. State and explain sampling theorem
Q2. Explain the working of Sample and hold circuit
Q3. Explain briefly the term Aliasing effect and Fold over with example.
Q4. Give the principle of working of Analog to digital converter, along with data sheet of
commercially available ADC devices.
Q5. Design a 50 Hz notch filter to remove power frequency noise from ECG signal. What
you will do to increase the Q factor of this filter.
Q6. Write short note on polynomial filter and their application.
Q7. Give the concept of Integer filter design.
Home Assignment-2:
1. Give the different image storage formats with their features, including DICOM format.
2. List the mathematical and logical functions that can be applied for basic processing of image
(Explain with suitable examples).
3. With respect to image smoothing give the algorithm ( MATLAB program ) for:
a. Nearest neighbor filter
b. Sigma filtering
c. Median filtering
4. Explain the concept of adaptive adjustment of mean and standard deviation. Write program in
MATLAB realization of same.
5. What is thresholding? Give different types of thresholding methods. How the value of threshold is
selected for optimal image segmentation. Give MATLAB program for band thresholding.
6. Explain the concept of look-up table. How they can be used for image processing.
7. Explain the concept of histogram equalization. Write a program for histogram equalization.
8. Explain the concept of histogram specification. Give necessary equation and write program for
a. Trapezoidal
b. Gaussian specification of histogram.
9. Generate your own concept to realize adaptive selection of window size i.e. window size is
adjusted adaptively for border region. Write an algorithm for same.
Home Assignment-3:
1. What is the most typical problem of edge-based segmentation? Explain the main
concept of border detection using edge relaxation?
2. What is the main idea of principle of optimality? How it is used in dynamic
programming?
3. Explain the main conceptual differences in edge-based and region-based approaches
to image segmentation. Are these two approaches dual? If an edge-based and a
region-based segmentation method are applied to the same image data, will the
resulting segmentation be identical? Consider ideal noise-free image data and realworld digital images.
4. Specify mathematically the goal of region-based segmentation using the criterion of
region homogeneity.
5. Explain the principles of and differences among the three basic approaches to region
growing-merging, splitting and split-and-merge.
6. Explain why watershed segmentation tends to over-segment images?
Home Assignment-4:
1. What is the texel? Explain the differences between weak and strong textures, also
explain the difference between fine and coarse textures.
2. Specify the main texture description and recognition strategies employed in statistical,
syntactical and hybrid methods. For each of three general approaches, describes the
texture types for which the approach can be expected to perform well and for which it
is not appropriate.
3. Explain the rationale for primitive grouping. What are its advantages? To which
textures is this approach applicable? How can it be used for texture description?
4. Texture characteristics can be used in image segmentation. Describe how texture
descriptors may be used in region growing segmentation.
5. Develop functions for computing the following texture features in an image of a given
size:
a. Co-occurrence descriptors
b. Average edge frequency
c. Primitive length descriptors
d. Law’s energy descriptor
e. Fractal texture descriptor