IMAGE PROCESSING FOR MEASUREMENT OF INTIMA MEDIA THICKNESS
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1 3rd SPLab Workshop IMAGE PROCESSING FOR MEASUREMENT OF INTIMA MEDIA THICKNESS Ing. Radek Beneš Department of Telecommunications FEEC, Brno University of Technology
2 OUTLINE 2 Introduction and motivation System for IMT measurement Results Conclusion
3 INTRODUCTION AND MOTIVATION
4 MEASUREMENT OF IMT 4 IMT = thickness two innermost layers on CCA wall Measurement can be performed in B-mode US images Measurement of IMT is a common medical examination Increase of IMT value can predict risk of cardiovascular events Manual measurement (mostly performed) time consuming Motivation to automatize such measurement, save time, increase efficiency, etc.
5 THE COMMON CAROTID ARTERY (CCA) 5 CCA is an artery that ascends within the neck (on both sides) CCA supplies the head and neck with blood It bifurcates into two branches External carotid artery (ECA) Supplying the exterior of the head and face Internal carotid artery (ICA) Supplying the parts within the cranial and orbital cavities Arteries of the neck
6 THE COMMON CAROTID ARTERY (CCA) 6 Source of useful information Not only IMT, but also lumen diameter, arterial stiffness, etc. Can be visualized non-invasively by US Transverse vs. longitudinal sections (each suitable for different parameters) Transverse section CCA Longitudinal section
7 SYSTEM FOR IMT MEASUREMENT
8 SYSTEM FOR IMT MEASUREMENT 8 US image Artery localization IMT measurement IMT value Two main parts Artery localization IMT measurement Automatic system must have both step automatic
9 ARTERY LOCALIZATION US image Artery localization IMT measurement IMT value
10 ARTERY LOCALIZATION 10 The goal? Found the exact contour of arterial wall in US image Find it automatically (no user initialization or interaction) Find it precisely (second block depends on this result) Related work Often not very robust Do not handle all possible geometric constellation of arteries Bended arteries, non-horizontally, images with small overall contrast Often have high computational needs
11 ARTERY LOCALIZATION PRINCIPLE Analyze the textural parameters of each pixel 2. Classify pixel to two classes inside X outside artery 3. Globally analyze inside pixels to find the coarse localization 4. Analyze gradient along coarse localization in order to find points on arterial wall 5. Post-processing of points on arterial wall
12 ARTERY LOCALIZATION Analyze the textural parameters Extract a set of local features containing e.g. mean value, standard deviation, median, auto-covariance, skewness for each pixel 2. Classify pixel into two classes inside vs. outside artery Use the SVN classifier with radial basis Best performance Input image with inside pixels highlighted
13 ARTERY LOCALIZATION Global analysis of inside pixels to find the coarse localization Apply modified RANSAC method to find the most appropriate interpolation of pixels RANSAC = interpolate without assuming of outliers
14 ARTERY LOCALIZATION Search points on arterial wall Based on gradient analysis (maximal gradient) Gradient perpendicularly to coarse localization in many places along artery set of points
15 ARTERY LOCALIZATION Interpolation of points detected on arterial wall Interpolation have to minimize influence of outliers RANSAC
16 IMT MEASUREMENT US image Artery localization IMT measurement IMT value
17 IMT MEASUREMENT THE GOAL 17 Measure of mean IMT (single value) Measure in many places along artery (in longitudinal section) and compute resulting IMT Measure automatically No user interaction during measurement Achieve the highest precision It is direct result of the method Required by a medical utilization The IMT complex is tiny ( mm)
18 IMT MEASUREMENT RELATED WORK 18 IMT and its measurement is a popular topic publications/year publications/year publications/year Methods are mostly based on Dynamic programming (DP) Snakes And their modifications Often very complex (high computational cost) Especially for snake based methods Processing time can be up to few minutes Problems with noisy images Often the layers are visible only on part of artery
19 IMT MEASUREMENT 19 Problems with noisy images Current methods trying to interpolate LI and MA interfaces along whole artery Implements plenty of workarounds to track interfaces even in noisy parts of image In noisy parts the error of measurement can raise and it can affect the final precision
20 IMT MEASUREMENT OUR METHOD 20 Do not continually interpolate interfaces in whole image (as snake based methods or DP) Selects only few points along artery for measurement In this places the measurement is very precise Advantages of approach Increase of the robustness when a certain part of image is corrupted by noise Decrease initialization needs (some snake based methods needs to be initialized precisely) Decrease computational complexity
21 IMT MEASUREMENT THE MAIN PRINCIPLE 21 Two step process 1. Select the most suitable points for measurement Most suitable points = places, where the measurement will be performed with the highest precision 2. Analyze gradient profile to detect interfaces Lumen-intima interface (LI interface) Media-adventitia interface (MA interface) Distance between these two interfaces is IMT
22 IMT MEASUREMENT THE MAIN PRINCIPLE 22 Measurements are based on intensity profile Achieved perpendicularly to the arterial wall contour in many place along artery
23 IMT MEASUREMENT THE MAIN PRINCIPLE Select the most suitable points along artery Selection is based on the intensity profile functions Requirements on the suitable intensity profile (IP) Contrast of IP must be higher than a defined threshold value Threshold is defined statistically The IP must contain one valley (with a minimal length of descend)
24 IMT MEASUREMENT THE MAIN PRINCIPLE Identification of LI and MA (IMT measurement) LI interface is defined as a first increase in intensity profile MA interface is defined as a center between valley and a maximum in intensity profile MA interface LI interface
25 RESULTS 25 Compare designed method against the ground truth (mean value selected by few operators) Localization step Classifier accuracy: 92.85% Higher accuracy was assured thanks to advanced selection of the features in feature space Localization fails in 2% of cases Average error of wall localization: 4.62 px (0.35 mm)
26 RESULTS 26 LI MA identification (IMT measurement) Average error 0.82px ± 70 px (63µm ± 54µm) Computational costs 0,2 s Author of the method Average error Computational costs Delsanto et al. 63µm ± 49µm 32s Molinaty et al. 54µm ± 35µm 3s Gutierez et al. 90µm ± 60µm - Liang et al. 42µm ± 20µm 42s Direct comparison of accuracy is not very relevant, because the other methods are often tested on the cropped images
27 CONCLUSION 27 Proposed a novel method Complete process of IMT measurement Input = whole ultrasound image Output = value of IMT Properties of the proposed method Automatic Fast Accurate
28 THANK YOU FOR YOUR ATTENTION 28
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