Medical Image Processing and Analysis MSc in Biomedical Engineering João Manuel R. S. Tavares tavares@fe.up.pt www.fe.up.pt/~tavares December 10, 2015
Outline • Introduction • Segmentation • Motion Tracking • Analysis of Objects: Matching, Morphing and Registration • 3D Reconstruction • Conclusions • Research Team • Publications & Events @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 2
Introduction
Presentation • Associate Professor at FEUP (DEMec) • Senior Research and Projects Coordinator of the Institute of Science and Innovation in Mechanical and Industrial Engineering (INEGI) • Habilitation in Mechanical Engineering from the University of Porto • PhD and MSc degrees in Electrical and Computer Engineering from FEUP • BSc degree in Mechanical Engineering from FEUP • Research Areas: Image Processing and Analysis, Medical Imaging, Biomechanics, Human Posture and Control, Product Development @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 4
Introduction • The researchers of Image Processing and Analysis aim the development of algorithms to perform fully or semi-automatically tasks performed by the (quite complex) human vision system Original images Computational 3D voxelized and poligonized models built Azevedo et al. (2010) Computer Methods in Biomechanics and Biomedical Engineering 13(3):359-369 @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 5
Introduction • Image processing and analysis are topics of the most importance for our Society • Algorithms of image processing and analysis are frequently used, for example, in: – Natural Sciences – Sports – Biology – Industry – Engineering – Medicine @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 6
Introduction • Examples of common tasks involving algorithms of image processing and analysis: – Noise removal – Geometric correction – Segmentation , recognition (2D-4D) – Motion and deformation tracking and analysis, including matching, registration and morphing – 3D reconstruction – Assisted medical diagnosis and intervention @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 7
Introduction • Image: a matrix with n rows and m columns (and l in 3D) , being each basic element known as pixel (or voxel in 3D) @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 8
Introduction • Image Processing: by applying mathematical operations/rules using the values of the image pixels (or voxels in 3D) in the Cartesian or in other domain Sobel operator ⎡ ⎤ − 1 + 1 0 ⎢ ⎥ ∗ − 2 = G x 0 2 ⎢ ⎥ − 1 + 1 ⎢ ⎥ 0 ⎣ ⎦ 2 + G y 2 = G G x ⎡ ⎤ − 1 − 2 − 1 ⎢ ⎥ ∗ = G y 0 0 0 ⎢ ⎥ + 1 + 2 + 1 ⎢ ⎥ ⎣ ⎦ ( denotes convolution) ∗ @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 9
Introduction • Image Acquisition: a sensor captures the energy reflected or emitted by the imaged object http://what-when-how.com/introduction-to-video-and-image-processing/image-acquisition-introduction-to- video-and-image-processing-part-1 @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 10
Introduction • Difficulties : noise, artifacts, occlusion, poor illumination, reflections, complex objects and backgrounds https://rahaddadi.files.wordpress.com/2011/05/face_black_and_white_optical_illusion_cool-s453x562-92306-5803.jpg http://s1.cdn.autoevolution.com/images/news/the-longest-traffic-jam-in-history-12-days-62-mile-long-47237_1.jpg @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 11
Introduction Usual Pipeline Image(s) Image(s) segmentation / enhancement / features extraction Image(s) correction image (pre)processing tracking 3D vision computer vision matching motion analysis registration image analysis / morphing computational vision @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 12
Introduction • (Pre) processing of noisy images using an intelligent worm Original images (a), noisy corrupted images (b) and smoothed images using different smoothing methods (c-h) Araujo et al. (2014) Expert Systems with Applications 41(13):5892-5906 @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 13
Segmentation
Segmentation • It is intended to identify in a fully or semi- automatically manner objects (2D/3D) presented in static images or in image sequences • The most usual methodologies are based on thresholding, region growing, template matching, statistical, geometric or physical modeling, or artificial classifiers • It is one of the most usual operations involved in the computational analysis of objects in images • Frequent problems: noise, artifacts, low resolution, reduced contrast, shapes not previously known, occlusion, multiple objects, etc. @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 15
Segmentation • Image segmentation by threshold (binarization) Ma et al. (2010) Computer Methods in Biomechanics and Biomedical Engineering 13(2):235-246 @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 16
Segmentation • Example: segmentation of contours in dynamic pedobarography (Otsu’s method, morphologic dilation, xor operation) pressure opaque layer lamp transparent layer lamp glass reflected light contact layer + glass mirror camera Segmented images Original images Bastos & Tavares (2004) Lecture Notes in Computer Science 3179:39-50 @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 17
Segmentation • Image segmentation by region growing Ma et al. (2010) Computer Methods in Biomechanics and Biomedical Engineering 13(2):235-246 @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 18
Segmentation • Example: segmentation of ear structures (region growing) Region Growing, x=215; y=254 X: 254 Y: 214 Index: 116.7 RGB: 0.459, 0.459, 0.459 Original Image Segmentation obtained (bony labyrinth) Barroso et al. (2011) CNME 2011 Ferreira et al. (2014) Computer Methods in Biomechanics and Biomedical Engineering 17(8):888-904 @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 19
Segmentation • Segmentation based on neuronal networks Original metallographic After segmentation images (material microstructures ) Albuquerque et al. (2008) Nondestructive Testing and Evaluation 23(4):273-283 Albuquerque et al. (2009) NDT & E International 42(7):644-651 @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 20
Segmentation • Segmentation of objects based on deformable templates Example of a deformable template (for the eye) Carvalho & Tavares (2006) CompIMAGE 2006, 129-134 Carvalho & Tavares (2007) VipIMAGE 2007, 209-215 @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 21
Segmentation • Example: segmentation of eye features (deformable geometric template) Original image and associated Segmentation of the iris using a force (or energy) fields deformable template (a circle) Segmentation of an eye using an Carvalho & Tavares (2006) CompIMAGE 2006, 129-134 deformable template Carvalho & Tavares (2007) VipIMAGE 2007, 209-215 @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 22
Segmentation • Statistical modeling of objects ( point distribution models ) Vasconcelos & Tavares (2008) Computer Modeling in Engineering & Sciences 36(3):213-241 @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 23
Segmentation • Segmentation of based on active shape models (point distribution models, optimization) Vasconcelos & Tavares (2008) Computer Modeling in Engineering & Sciences 36(3):213-241 @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 24
Segmentation • Example: analysis of the vocal tract during speech production from MR images (active shape model) Original image Final segmentation Vasconcelos et al. (2011) Journal of Voice 25(6):732-742 @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 25
Segmentation • Segmentation of objects based on active appearance models (statistical models, optimization) Vasconcelos & Tavares (2008) Computer Modeling in Engineering & Sciences 36(3):213-241 @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 26
Segmentation • Example: analysis of the vocal tract shape during speech production from MR images (active appearance model) Initial segmentation Final segmentation Vasconcelos et al. (2011) Journal of Engineering in Medicine 225(1):68-76 Vasconcelos et al. (2012) Journal of Engineering in Medicine 226(3):185-196 @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 27
Segmentation • Segmentation of objects based on active contours (i.e. snakes – parametric models ) 1 ∫ E snake = ( v ( s )) + E ext ( v ( s )) ds E int s = 0 2 2 + β ( s ) d 2 v ( s ) E int = α ( s ) dv ( s ) ds 2 ds Tavares et al. (2009) International Journal for Computational Vision and Biomechanics 2(2):209-220 @2015 João Manuel R.S. Tavares Medical Image Processing and Analysis 28
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