# DTU Informatics Introduction to Medical Image Analysis Rasmus R. Paulsen DTU Informatics TexPoint fonts.

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DTU Informatics Introduction to Medical Image Analysis Rasmus R. Paulsen DTU Informatics rrp@imm.dtu.dk http://www.imm.dtu.dk/courses/02511 TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A AA A A A A A A A A AA

DTU Informatics 29/4/2009Introduction to Medical Imaging 2 DTU Informatics, Technical University of Denmark Lecture 11 – Hough Transformation and Path Tracing 9.00Lecture Exercises 12.00 – 13.00Lunch break 13.00-Exercises

DTU Informatics 29/4/2009Introduction to Medical Imaging 3 DTU Informatics, Technical University of Denmark What can you do after today? Use the Hough transform for line detection Describe the slope-intercept, the general form and the normalised form of lines Describe the connection between lines and the Hough space Use edge detection to enhance images for use with the Hough transform Use dynamic programming to trace paths in images Describe how an image can be used as a graph Describe the fundamental properties of a cost image Compute the cost of path Compute an accumulator image for path tracing Compute a backtracing image for path tracing Describe how circular structures can be located using path tracing

DTU Informatics 29/4/2009Introduction to Medical Imaging 4 DTU Informatics, Technical University of Denmark Line Detection Find the lines in an image

DTU Informatics 29/4/2009Introduction to Medical Imaging 5 DTU Informatics, Technical University of Denmark What is a line? It can be the entire object –Large scale Can also be the border between an object and the background –Small scale Normally only locally defined

DTU Informatics 29/4/2009Introduction to Medical Imaging 6 DTU Informatics, Technical University of Denmark Enhancing the lines We want to locate the borders –Enhance them Filtering Edge detection OriginalPrewitt Edge

DTU Informatics 29/4/2009Introduction to Medical Imaging 7 DTU Informatics, Technical University of Denmark What is a line II? Result of the edge filter is a selection of white pixels Some of them define a line –Not a perfect straight line –“Linelike” How do we find the collection of points that define a line?

DTU Informatics 29/4/2009Introduction to Medical Imaging 8 DTU Informatics, Technical University of Denmark Mathematical line definition The classical definition (slope-intercept form) Slope Intercept Can not represent lines that are vertical

DTU Informatics 29/4/2009Introduction to Medical Imaging 9 DTU Informatics, Technical University of Denmark Mathematical line definition General definition With Line normal

DTU Informatics 29/4/2009Introduction to Medical Imaging 10 DTU Informatics, Technical University of Denmark Mathematical line definition Normal parameterisation where – is the distance from the origin – is the angle

DTU Informatics 29/4/2009Introduction to Medical Imaging 11 DTU Informatics, Technical University of Denmark Mathematical line definition Normal parameterisation Therefore a line can be defined by two values – A line can therefore also be seen as a point in a (, )- space

DTU Informatics 29/4/2009Introduction to Medical Imaging 12 DTU Informatics, Technical University of Denmark Something about angles In Matlab and in this presentation In the course notes

DTU Informatics 29/4/2009Introduction to Medical Imaging 13 DTU Informatics, Technical University of Denmark Hough Space

DTU Informatics 29/4/2009Introduction to Medical Imaging 14 DTU Informatics, Technical University of Denmark More about angles Why? but Matlab only allows look at the mirror-projection of the normal is used to determine if it is the “upper” or “lower line”

DTU Informatics 29/4/2009Introduction to Medical Imaging 15 DTU Informatics, Technical University of Denmark How do we use the Hough space? ?

DTU Informatics 29/4/2009Introduction to Medical Imaging 16 DTU Informatics, Technical University of Denmark How do we use the Hough space? What if every little “line-segment” was plotted in the Hough- space?

DTU Informatics 29/4/2009Introduction to Medical Imaging 17 DTU Informatics, Technical University of Denmark Filled Hough-Space All “line segments” in the image examined A “global line” can now be found as a cluster of points In practice it is difficult to identify clusters

DTU Informatics 29/4/2009Introduction to Medical Imaging 18 DTU Informatics, Technical University of Denmark Hough transform in practise Hough Space is represented as an image It is quantisized – made into finite boxes Pixel A line segment here Belongs to this pixel

DTU Informatics 29/4/2009Introduction to Medical Imaging 19 DTU Informatics, Technical University of Denmark Hough transform as a voting scheme The pixels in the Hough space are used to vote for lines. Each line segment votes by putting one vote in a pixel The pixels are also called accumulator cells

DTU Informatics 29/4/2009Introduction to Medical Imaging 20 DTU Informatics, Technical University of Denmark Hough transform per pixel In practise we do not use line segments Each pixel in the input image votes for all potential lines going through it.

DTU Informatics 29/4/2009Introduction to Medical Imaging 21 DTU Informatics, Technical University of Denmark Hough transform per pixel Go through all and calculate (x, y) are fixed Sinusoid!

DTU Informatics 29/4/2009Introduction to Medical Imaging 22 DTU Informatics, Technical University of Denmark Real Hough Transform

DTU Informatics 29/4/2009Introduction to Medical Imaging 23 DTU Informatics, Technical University of Denmark Real Hough Transform II

DTU Informatics 29/4/2009Introduction to Medical Imaging 24 DTU Informatics, Technical University of Denmark Real Hough Transform and lines Spot the line! A maximum where Hough pixel has value 3

DTU Informatics 29/4/2009Introduction to Medical Imaging 25 DTU Informatics, Technical University of Denmark Finding the lines in Hough space The lines are found in Hough space where most pixels have voted for there being a line Can be found by searching for maxima in Hough Space

DTU Informatics 29/4/2009Introduction to Medical Imaging 26 DTU Informatics, Technical University of Denmark The practical guide to the Hough Transform Start with an input image

DTU Informatics 29/4/2009Introduction to Medical Imaging 27 DTU Informatics, Technical University of Denmark The practical guide to the Hough Transform Detect edges and create a binary image

DTU Informatics 29/4/2009Introduction to Medical Imaging 28 DTU Informatics, Technical University of Denmark The practical guide to the Hough Transform Compute Hough transform and locate the maxima

DTU Informatics 29/4/2009Introduction to Medical Imaging 29 DTU Informatics, Technical University of Denmark The practical guide to the Hough Transform Draw the lines corresponding to the found maxima Here the cyan line is the longest

DTU Informatics 29/4/2009Introduction to Medical Imaging 30 DTU Informatics, Technical University of Denmark Exam question 09.24

DTU Informatics 29/4/2009Introduction to Medical Imaging 31 DTU Informatics, Technical University of Denmark Path Tracing The diameter as function of the distance to the optic cup tells something about the patients health We need to find the arteries and veins Path tracing is one solution Fundus image Arteries and veins

DTU Informatics 29/4/2009Introduction to Medical Imaging 32 DTU Informatics, Technical University of Denmark Path tracing A path is defined as a curve in an image defined as something that is different from the background’ In this case it is a dark line Pre-processing can for example turn edges into dark lines.

DTU Informatics 29/4/2009Introduction to Medical Imaging 33 DTU Informatics, Technical University of Denmark Path tracing A GPS device uses path tracing Based on graph algorithms –A city is a node –A road is an edge. The weight of the edge is the fuel cost How do we come from Copenhagen to Aalborg using the least fuel? Dijkstra’s algorithm Copenhagen Aalborg 2.3 l 2.1 l 1.3 l

DTU Informatics 29/4/2009Introduction to Medical Imaging 34 DTU Informatics, Technical University of Denmark Images as graphs Each pixel is a node Pixel neighbours are connected by edges The edge cost is the pixel value Directed graph Imagine a car driving on the image Called a cost image

DTU Informatics 29/4/2009Introduction to Medical Imaging 35 DTU Informatics, Technical University of Denmark Simplified problem Track dark lines Path going from top to bottom No sharp turns – smooth Problem: –from the top to the bottom –Sum of pixel values should be minimal

DTU Informatics 29/4/2009Introduction to Medical Imaging 36 DTU Informatics, Technical University of Denmark Simplified problem Pixel value at (r,c) equals the cost The path P consist of pixels The sum of pixel values in the path

DTU Informatics 29/4/2009Introduction to Medical Imaging 37 DTU Informatics, Technical University of Denmark Path cost A path is defined as (r,c) coordinates What is ? How do we compute the path P that has minimum Test all possible paths? P = [(1,3), (2,3), (3, 2), (4,3), (5,4)]

DTU Informatics 29/4/2009Introduction to Medical Imaging 38 DTU Informatics, Technical University of Denmark Path restriction Path is only allowed to –Go down –Move one pixel left or right Longer jumps not allowed

DTU Informatics 29/4/2009Introduction to Medical Imaging 39 DTU Informatics, Technical University of Denmark Accumulator image Keeps track of the accumulated cost for possible paths Optimal path ending here has cost 296

DTU Informatics 29/4/2009Introduction to Medical Imaging 40 DTU Informatics, Technical University of Denmark Computing the accumulator image Step 1: Copy first row of input image

DTU Informatics 29/4/2009Introduction to Medical Imaging 41 DTU Informatics, Technical University of Denmark Computing the accumulator image Step 2: Fill second row

DTU Informatics 29/4/2009Introduction to Medical Imaging 42 DTU Informatics, Technical University of Denmark Computing the accumulator image Step 3: Fill all rows by looking at the previous row

DTU Informatics 29/4/2009Introduction to Medical Imaging 43 DTU Informatics, Technical University of Denmark Using the accumulator image Step 4: The end of the optimal path can now be found

DTU Informatics 29/4/2009Introduction to Medical Imaging 44 DTU Informatics, Technical University of Denmark The backtracing image Keeps track of where the path came from Each pixel stores the column number

DTU Informatics 29/4/2009Introduction to Medical Imaging 45 DTU Informatics, Technical University of Denmark Using the backtracing image Step 5: Trace the path in the backtracing image

DTU Informatics 29/4/2009Introduction to Medical Imaging 46 DTU Informatics, Technical University of Denmark Using the backtracing image

DTU Informatics 29/4/2009Introduction to Medical Imaging 47 DTU Informatics, Technical University of Denmark Pre-processing We would like to track paths that are not dark curves Pre-processing –Turn edges into dark curves Any ideas?

DTU Informatics 29/4/2009Introduction to Medical Imaging 48 DTU Informatics, Technical University of Denmark Pre-processing Edge filtered image (Gaussian smoothing followed by Prewitt)

DTU Informatics 29/4/2009Introduction to Medical Imaging 49 DTU Informatics, Technical University of Denmark Path tracing on pre-processed image Paths found on pre-processed image

DTU Informatics 29/4/2009Introduction to Medical Imaging 50 DTU Informatics, Technical University of Denmark Exercises ?

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