CVIP Dundee AN OVERVIEW OF IMAGE PROCESSING RESEARCH. Emanuele Trucco, DUNDEE

Transcription

1 CVIP Dundee AN OVERVIEW OF IMAGE PROCESSING RESEARCH DUNDEE Emanuele Trucco,

2 THE CVIP GROUP Computer Vision and Image Processing School of Computing Nov 2011: 3 academics, 4 RAs, 5 PhD students MEDICAL IMAGE ANALYSIS IMAGE ANALYSIS FOR THE LIFE SCIENCES UNDERSTANDING HUMAN ACTIVITIES IMAGE BROWSING AND RETRIEVAL... please see our leaflets and USB sticks!

3 CONTENTS PART 1: CVIP SELECTED PROJECTS Security, biometrics and object recognition (Jianguo Zhang) Content based retrieval, breast cancer screening, assistive technologies (Stephen McKenna) PART 2: RETINAL IMAGE ANALYSIS Retinal image analysis (Emanuele Trucco) The VAMPIRE project Analysis of angiographic sequences PART 3: THREE SUGGESTIONS FOR COLLABORATION

4 AGE CLASSIFICATION Jianguo ZHANG Age classification: increasing safety and security issues. Major challenge: anatomical changes on faces and feature drifts. Our Approach: Randon Transform plus Scaling SVM Original images Tony Blair from Google Images. Red circles indicate the areas experiencing anatomical variations, and yellow lines point to corresponding areas. Adaptive Difference of Gaussian (DoG) Randon Transform (RT) Feature selection/clas sification Months 4 years 7 years 14 years Comparisons: 5 fold SVM with PCA, LBP, HOG, DoG+RT, DoG+RT+SSVM, HOG+E SSVM and proposed Two Databases: FG NET and MORTH. Two age classes are concerned: < 20y and > 20y

5 ACTION CLASSIFICATION Wenqi LI and Jianguo ZHANG 1. Motivation Spatial temporal pyramid model: Constructs feature vectors by simply concatenating weighted descriptors that derived at different resolutions. The weights are fixed for every pyramid levels. Could we learn to soft combine those descriptors from different sub regions to strengthen feature vectors? 2. Proposed Strategy 3. Results Datasets: KTH, Youtube actions soft combination of descriptors showed higher average accuracies on both

6 OBJECT RECOGNITION BY BAG OF FEATURES Jianguo ZHANG Bag of Local Features + Nonlinear SVM Classifier (Won Pascal Challenge 2005 and 2006 ) [Zhang, Marszalek, Lazebnik & Schmid, IJCV 2007]

7 GENDER RECOGNITION AT A DISTANCE FROM STATIC IMAGES Jianguo ZHANG, M Collions(QUB), M Paul (QUB) MIT Dataset ( 62M, 63 F) VIPeR Dataset (292M, 291 F) LHSV + HOG + Stacked SVM

8 ANALYSIS OF BREAST TISSUE HISTOPATHOLOGY IMAGES Stephen McKENNA, Shazia AKBAR, Telmo AMARAL (School of Computing) Lee JORDAN, Alastair THOMPSON (Ninewells Hospital, Dundee) Tissue and tumour segmentation Automated immunohistochemical scoring Manual segmentation Automated segmentation

9 CONTENT-BASED IMAGE BROWSING Stephen McKENNA, Ruixuan WANG, Annette WARD

10 RECOGNITION OF FOOD PREPARATION ACTIVITIES FOR SITUATIONAL SUPPORT S. STEIN, S. McKENNA, P. OLIVIER, J. HOEY (U of Waterloo, CAN) Aim and Motivation Guidance for people with cognitive impairments Stay longer independent; increased quality of life Challenges Highly complex domain with large variability Situational support requires detailed activity models and descriptions Integration of multiple types of sensors seems inevitable Methodology Fusion of vision and embedded accelerometers Action Recognition and Ingredient Tracking High level reasoning with statistical relational learning methods

11 Vessel Assessment and Measurement Platform for Images of the REtina A Perez Rovira, T MacGillivray, E Trucco, K S Chin, K Zutis, C Lupascu, D Tegolo, A Giachetti, PJ Wilson, A Doney, B Dhillon

12 VAMPIRE consortium of 10 international research groups cluster of projects hinging on retinal image analysis software platform RETINAL IMAGE ANALYSIS Quantitative measurements of retinal features Validation (annotated sets) Measurement requests CLINICAL RESEARCH GWAS GENETICS COGNITIVE EPIDEMIOLOGY BIOMARKER DISCOVERY SUPPORT FOR QUANTITATIVE DIAGNOSIS

13 THE VAMPIRE CONSORTIUM Manchester Royal Eye Hospital Singapore Ophthalmology Clinical radiology Diabetes screening Ageing and Health Ophthalmology Clinical radiology Los Angeles, USA

14 THE VAMPIRE TOOL

15 Currently in tool: CAPABILITIES OD location (ellipse), retinal co ordinates Vessel width Vessel branching angles and coefficients Vessel tortuosity Interactive location of interest points Result spreadsheet generation Currently ex tool: Fractal dimension AVR (manual A/V classification) Fovea and macular area location Fluorescein angiogram summarization, analysis Annotation tools (vessels, OD, bifurcations)

16 VAMPIRE BASED STUDIES: EXAMPLES Lothian Birth Cohort Determinants of differences in cognitive ageing. N. Patton, T. Aslam, T. J. MacGillivray, A. Pattie, I. J. Deary, and B. Dhillon, Retinal vascular image analysis as a potential screening tool for cerebrovascular disease, Journal of Anatomy, vol. 206, pp , Retinal vasculature changes induced by AMD drugs. S. S. Tiew, A. Perez Rovira, E. Trucco, S. Mahmood, P. Bishop, T. M. Aslam: Experience In Using The Vampire Retinal Analysis Tool To Assess Tortuosity In Patients Undergoing Bevacizumab (Avastin ) Treatment For Wet Age related Macular Degeneration (AMD). Proc ARVO (Assoc for Research in Vision and Ophthalmology), Fort Lauderdale (USA), Retinal biomarkers for stroke. Retinal microvascular abnormalities as biomarkers for cerebral small vessel disease. F. N. Doubal, T. J.MacGillivray, P. E. Hokke, B. Dhillon, M. S. Dennis, and J. M. Wardlaw, Differences in retinal vessels support a distinct vasculopathy causing lacunar stroke, Neurology, vol. 72, pp , 2009.

17 ANALYSIS OF FLUORESCEIN ANGIOGRAM SEQUENCES Diagnostic value: show clearly vasculature and flow Challenges: changes in content + nonrigid deformations OPTOS SLO, ~200 deg horiz FOV, non mydriatic, ~3000x3000 pixels

18 DEFORMABLE REGISTRATION WITH TIME VARIABLE CONTENTS: RERBEE Perez Rovira, Cabido, Trucco, McKenna, Hubschman, IEEE Trans Medical Imaging 2011

19 COLLABORATION: RESEARCH THEME 1 CLASSIFICATION, DETECTION WITH IMPERFECT TRAINING DATA Especially FA sequences WHY DIFFICULT? Generally, small training set Variable annotations WHY RELEVANT? Normal situation in medical image analysis ML with imperfect training data needs exploring HOW IS IT DONE NOW? Classic learning algorithms (mostly SVM)

20 COLLABORATION: RESEARCH THEME 2 VALIDATION IN RETINAL IMAGE ANALYSIS: Does the algorithm work? WHY DIFFICULT? No established principled paradigm No exact ground truth; subjective annotations only Variable annotations WHY RELEVANT? Conclusions drawn from subjective, partial methods Access to substantial amounts of data HOW IS IT DONE NOW? Classic learning algorithms (mostly SVM)

21 COLLABORATION: RESEARCH THEME 3 CLASSIFICATION, SEGMENTATION OF FLUORESCEIN ANGIOGRAM SEQUENCES WITH WEAK DYNAMIC MODELS Dynamic models: intensity evolution (dynamic textures?) Ultra wide field of view sequences (OPTOS SLO) WHY DIFFICULT? Strongly irregular sampling rate Automatic intensity adjustment (enhancement) Variable annotations WHY RELEVANT? Monitoring diabetes progress Quantifying lesions for rich diagnosis, e.g., summarization HOW IS IT DONE NOW? It isn t! Only work published so far is ours...

22 THANK YOU FOR YOUR ATTENTION QUESTIONS? REMINDER! 1) classification, detection with imperfect training data 2) Validation in retinal image analysis 3) classification, segmentation of FA sequences with weak dynamic models