Category Archives: LECTURE NOTES

Lecture Notes on Biomedical Instrumentation and Design

Course Objectives:


Students will be able to apply the principles of electronic circuits and devices to the use and design of instrumentation in the biomedical area. They will have gained a basic knowledge of the operating principles of electrical and other transducers, analog and digital instrumentation, applied signal acquisition and processing, electrical safety in the medical environment, electrical properties of nerve and muscle physiology; and instrumentation used in cardiopulmonary, neurological, surgical, and rehabilitation areas of medicine.


Lecture 1 – Introduction


Lecture 2 – Sensor Models


Lecture notes on Engineering Principles of Radiation Imaging

50pman medical imaging

50pman medical imaging (Photo credit: Wikipedia)


About the Lectures


The effect of radiation transport and quantum noise on image quality is explored in the context of both conventional and newly developed systems. Radiation sources for imaging, mathematical descriptions of image quality, and the performance of humans as visual observers are covered. Specific systems considered include phosphor screen and direct digital radiography systems, Anger camera systems, x-ray computed tomography (CT) systems, and Positron Emission Tomography (PET) systems. Particular emphasis is given to the statistical processes important in radiographic and nuclear medicine imaging systems.


Lecture notes : Algortihms used for Medical Image Processing

This course has two components. On the one hand, it is an introduction to digital image analysis presenting selected fundamental problems in medical image analysis, computer vision, photo/video editing, and graphics. We cover such basic concepts as image segmentation, registration, object recognition/matching, tracking, texture, etc. On the other hand, this is an applied course on standard computer science algorithms where students develop practical understanding of dynamic programming, graph based algorithms, computational geometry methods, etc. In fact, image analysis provides a stimulating environment for studying algorithms as their outputs can be intuitively visualized. Students with previous background in algorithms will be exposed to applications in image analysis, while students already familiar with problems in imaging will learn efficient methods based on standard CS algorithms. The course emphasizes the design, analysis, and implementation of algorithms in the context of 2D/3D medical images, photo and video data.

Lecture Notes on Biostatistics

Histogram of sepal widths for Iris versicolor ...

Image via Wikipedia

Introduction, descriptive statistics, diagnostic tests and method comparison, theoretical distributions and probability, point estimation and confidence interval, hypothesis tests, parameter and parameter free tests, comparing groups, correlation and regression, relation between several variables (ANOVA, multiple regression, logistic regression, factor analysis), analysis of survival times (Kaplan-Meier curves, cox regression, hazard ratio), experimental design, clinical trials, biological and medical literature, microarray analyses
Douglas G. Altman: Practical Statistics for Medical Research, Chapman&Hall/CRC
Martin Bland: An Introduction to Medical Statistics, Oxford Medical Publications
Harvey Motulsky: Intuitive Biostatistics, Oxford University Press
Lothar Sachs, Jürgen Hedderich: Angewandte Statistik (Methodensammlung mit R), Springer
John Verzani: Using R for Introductory Statistics, Chapman&Hall/CRC

Lecture notes on Biomaterials

DESCRIPTIONIntroduction to materials science and technology with special reference to metals, ceramics and polymeric materials used as biomaterials (processing, properties, testing), changes of materials during sterilization and in vivo, biocompatibility, selected cases of application and of damage, quality assurance.


Lecture PDF
1 [pdf]
2 [pdf]
3 [pdf]
4 [pdf]
5 [pdf]
6 [pdf]
7 [pdf]
8 [pdf]
9 [pdf]
10 [pdf]
11 [pdf]
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Lecture notes on Medical Informatics

Introduction 2008 [pdf]
Modelling [pdf]Introduction (Zlatko Trajanoski) [pdf]
Introduction to SVM (Vojislav Kecman) [pdf]
Basics of Support Vector Machines (Vojislav Kecman) [pdf]
Gene Expression Clustering (Alexander Sturn) [pdf]
Neural Networks (Zlatko Trajanoski) [pdf]
PCA (Zlatko Trajanoski) [pdf]
SOM (Zlatko Trajanoski) [pdf]
Decision Trees (Zlatko Trajanoski) [pdf]
Introduction to Probability Theory (Fatima Sanchez Cabo) [pdf]
Introduction to Statistical Inference (Fatima Sanchez Cabo) [pdf]
ELGA and eHealth (Karl Pfeiffer) [pdf]

Combined document (contains all of the documents above) [pdf]

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