Detection and Assessment of Abnormality in Medical Images MS Thesis Presentation Candidate: K Sai Deepak Adviser: Prof. Jayanthi Sivaswamy Center for Visual Information Technology IIIT Hyderabad India 31 -March-2012
Computer Aided Diagnosis Disease Screening Proposed Methodology Agenda • Computer Aided Diagnosis – Modes of Healthcare – CAD in Primary Care (examples) • Disease Screening – CAD in Disease Screening – Challenges for existing CAD • Proposed Methodology – Detecting Abnormality Instead of Disease – Detection of Lesions using Motion Patterns • Detection and Assessment of Retinopathy – – • Diabetic Macular Edema Method Experiments and Results Detection of Multiple Lesions Classification of Lesions in Mammograms – Mammographic Lesions – Experiments and Results Source of all the figures are explicitly mentioned in the MS Thesis -Showcase 1 Retinopathy -Showcase 2 Breast Cancer
Computer Aided Diagnosis Disease Screening Proposed Methodology -Showcase 1 Retinopathy -Showcase 2 Breast Cancer PART I – Computer Aided Diagnosis
Computer Aided Diagnosis (CAD) • Aid of computers in the process of diagnosis • Computer aided diagnosis (CAD) has become one of the major support systems assisting medical experts in diagnosis through images • CAD tools are used for measurement, display and analysis of both the structural and functional aspects of the body from images
Computer Aided Diagnosis CAD with Images • Visualization – enhancement for visual analysis (Ex. Windowing, MIP, MAP, AIP, Zoom, Contrast Inversion etc. ) • Detection – detect the presence of disease manifestation • Localization and Segmentation – Localize or segment the spatial regions containing disease manifestation • Other utilities can be used for measurement of various structures from images (length, volume etc. )
Computer Aided Diagnosis Healthcare – Primary Care and Disease Screening Point of Consultation in basic healthcare Patients with Symptoms arrive Undergo specialized tests if required for Diagnosis Treatment is planned based on Diagnosis Performed on Public health initiative Most patients have no disease symptoms Detection is performed by a trained professional Referred to expert on positive detection Secondary and Tertiary Care Centers – are where patients usually visit on referral for advanced care
Computer Aided Diagnosis CAD in Primary Care • Traditionally CAD has been used in Primary Care
Computer Aided Diagnosis CAD in Primary Care • Patient visits the doctor with a complaint • If required, the patient is then referred by the doctor for specific imaging in order to diagnose the problem • Acquired images are analyzed by the experts (Ophthalmologist, Radiologist) to arrive at a diagnosis • The diagnosis report is used by doctor for planning treatment
Computer Aided Diagnosis Disease Screening Proposed Methodology -Showcase 1 Retinopathy PART II – Disease Screening -Showcase 2 Breast Cancer
Disease Screening • Disease screening is performed at specific community healthcare centers to prevent ensuing mortality and suffering from chronic ailments • Challenges: Geographical reach, Disease awareness and Social barriers and Availability of experts are common in screening • Tele-radiology provides significant help but the work load of a medical expert increases significantly due to large number of patients participating in population screening • Diabetic Retinopathy and Breast Cancer screening are already conducted or being adopted in several countries and is the focus of this work
Disease Screening CAD in Disease Screening • Existing CAD tools use a disease centric approach for disease detection • It requires application of several methods/tools for detecting all the possible lesions in a disease – Multiple CAD tools are used for identifying different Diabetic Retinopathy (DR) manifestations • Existing CAD systems are not able to meet the needs of disease screening in Diabetic Retinopathy [1] – Poor sensitivity of disease detection – Large number of normal patients are detected as abnormal [1] M. D. Abramoff, M. Niemeijer, M. S. Suttorp-Schulten, M. A. Viergever, S. R. Russell, and B. van Ginneken. Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes. Journal of Diabetes Care, 31: 193– 198, 2007.
Disease Screening Summary of Challenges • Existing CAD tools use a disease centric approach for detection and segmentation of disease – In Screening most of the patients are normal (80 -90% for DR & BC) • Multiple tools result in cascading effect of detected FPs • Doctors spend a lot of time in rejecting normal patients – Other challenges in disease centric approach • Illumination and Contrast • Tissue Pigmentation • A disease centric CAD system has to robustly learn all possible manifestations of a disease which is challenging • Patients with diseases outside the purview of screening are ignored – referral could be useful for a patient suffering non DR disease detected in DR screening
Disease Screening Other Challenges – Disease Vs Normal Background
Computer Aided Diagnosis Disease Screening Proposed Methodology -Showcase 1 Retinopathy -Showcase 2 Breast Cancer PART III – Proposed Methodology
Proposed Methodology Detecting Abnormality instead of Disease • Non conformance to expected behaviour (normal) in the data is considered as abnormality Y Abnormal • Features of normal medical images can be used to model expected normal behaviour • Abnormality detection is relevant in disease screening where detecting the presence of abnormality is of initial interest: – Retinal image screening for detecting Diabetic Retinopathy – Mammographic screening for detecting malignancy of lesions Abnormal Normal Feature Space Normal CFI X Abnormal CFI with lesions
Proposed Methodology Two Stage Methodology for CAD • Stage 1 - Detection of abnormality – Derive motion pattern for detection of lesions – Extract relevant features to represent normal sub-space – Detect outliers as abnormal • Stage 2 -Assessment of abnormality – Derive relevant features based on domain knowledge from abnormal cases – Determine the severity of disease
Proposed Methodology Two Stage Methodology for CAD • Stage 1 - Detection of abnormality – Only normal cases are required for disease detection – Variations observed in the normal cases are captured by the normal feature sub-space – Single point of control on the permitted figure of false alarms • Stage 2 -Assessment of abnormality – Fewer normal cases to be examined by experts
Proposed Methodology – Spatial/temporal averaging of intensities in retina – Smearing of intensities corresponding to moving object is observed in images acquired with camera • Inducing motion in images – Lesions can be observed as a set of localized pixels with contrast against background – A smear of pixel along the direction of motion can be observed in motion pattern – Spread and extent of lesions in motion pattern depends on the sampling rate at each location and duration of motion – Contrast of the spatially enhanced lesions in motion pattern relies on the coalescing function • Motion pattern on Background – Uniformity in motion pattern for textured background can be observed Rotational Motion Pattern • Motivation - Effect of motion on human visual system and detectors in camera Original Image (Textured Background) Original Image (Uniform Background) Motion Pattern – Detecting Localized Lesions
Computer Aided Diagnosis Disease Screening Proposed Methodology -Showcase 1 Retinopathy -Showcase 2 Breast Cancer PART IV – Detection and Assessment of Macular Edema
-Showcase 1 Retinopathy Macular Edema Detection and Assessment • Diabetic Macular Edema (DME) is a sight threatening condition that occurs due to diabetic retinopathy • DME requires immediate referral to Ophthalmologists • Presence of Hard Exudates is used as an indicator of DME during retinal disease screening Color Retinal Image Severe and moderate cases of DME
-Showcase 1 Retinopathy Existing Approaches in DME Detection • Several local and global schemes have been proposed for DME detection • Local Schemes – local schemes try to successfully segment or localize the exudate clusters – Techniques including adaptive intensity thresholding, background suppression (median filtering, morphology), color and edge detection have been proposed – several normal pixels are also detected as candidates in normal images increasing the number of false alarms in the system • Global Schemes – global schemes try to ensure that at least the brightest pixels corresponding to HE in the image are detected – Techniques based on intensity thresholding, edge strength, and visual words using features on SIFT keypoints have been used to classify images
-Showcase 1 Retinopathy Proposed Workflow Steps • Landmark Detection and Region of Interest Extraction • Generation of Motion Patterns • Feature Selection • Abnormality Detection • Abnormality Assessment
-Showcase 1 Retinopathy Detection of Landmarks in CFI Singh, J. and Joshi, G. D. and Sivaswamy, J. Appearance-based object detection in colour retinal images. In ICIP, pages 1432– 1435, 2008. G. D. Joshi and J. Sivaswamy and K Karan and S. R. Krishnadas. Optic disk and cup boundary detection using regional information. ISBI, pp. 948– 951, 2010.
-Showcase 1 Retinopathy Selection of ROI around center of macula
-Showcase 1 Retinopathy Motion Pattern – Rotational Motion Effect of sampling rate on motion pattern (decreasing rotation steps)- Coalescing Function • Mean - Arithmetic mean of all samples were taken • Extrema – Maximum or Minimum of all samples are taken at each pixel location
-Showcase 1 Retinopathy Selection of Motion Pattern normal abnormal • A normal retinal image was created by averaging the green channel of 400 retinal images • The abnormal retina is modeled by adding a bright lesion to emulate HE “effect of abnormality (lesion) on retinal background can be observed as change in local information with respect to the motion pattern of normal retina” - motion pattern - Gradient magnitude of motion pattern - Shannon’s entropy
-Showcase 1 Retinopathy Selection of Parameters – Class Discriminability Size of normal retina – 150*150 Neighborhood size – 7*7
-Showcase 1 Retinopathy Motion Pattern for Edema Detection • A circular ROI is determined around macula and the Optic disc is masked to avoid false positives • Rotational motion is induced in the green channel image • Maxima is used as the coalescing function • Features derived on motion pattern are used for learning the normal subspace and detecting abnormality Sample ROI and Motion Pattern (S- Subtle Hard Exudates)
More Motion Patterns -Showcase 1 Retinopathy Abnormal ROI Normal ROI Sample ROIs and Motion Pattern (S- Subtle Hard Exudates)
Feature Extraction -Showcase 1 Retinopathy • To effectively describe motion pattern, we use a descriptor derived from the Radon space Integral of motion pattern along a line • The desired feature vector is obtained by concatenating 6 projections (0 -180 degrees) • Each projection has 6 bins resulting in a feature vector of length 36
Abnormality Detection -Showcase 1 Retinopathy PCA Data Description • The eigenvectors corresponding to the covariance matrix of the training set is used to describe the normal subspace • Feature vector for a new case is projected to this subspace (first 6 eigen vectors) Residual e is defined as, • Classification between normal and abnormal cases is then performed using an empirically determined threshold on e
-Showcase 1 Retinopathy Detection Performance (ROC Curves) • DMED - 122 images Receiver Operating Characteristic curve • MESSIDOR – 400 images o o • Normal - 274 Abnormal – 126 Immediate referral - 85 Normal images used for training – 74 DMED o Normal - 68 o Abnormal – 54 o Normal images used for training - 18 Diaretdb 0 & db 1 – 122 images o Normal – 25 o Abnormal - 97 Combined Dataset – 644 images o Normal – 367 o Abnormal - 277 MESSIDOR •
-Showcase 1 Retinopathy Comparison against Disease Centric Methods DMED Normal - 68 Abnormal – 54 Normal images used for training - 18 MESSIDOR Normal - 274 Abnormal – 126 Normal images used for training – 74 [23] L. Giancardo, F. Meriaudeau, T. P. Karnowski, Y. Li, K. W. Tobin Jr, and E. Chaum. Automatic retina exudates segmentation without a manually labelled training set. IEEE ISBI, pages 1396 – 1400, April 2011. [2] C. Agurto, V. Murray, E. Barriga, S. Murillo, M. Pattichis, H. Davis, S. Russell, M. Abramoff, and P. Soliz. Multiscale am-fm methods for diabetic retinopathy lesion detection. IEEE TMI, 29(2): 502 – 512, feb. 2010.
-Showcase 1 Retinopathy Detection of subtle hard exudates
-Showcase 1 Retinopathy Assessment of Severity • Macula is devoid of significant vasculature • It is characterized by rough rotationally symmetry - Abnormal image - Symmetry measure on abnormal macula and are the minimum and maximum symmetry values for normal cases
-Showcase 1 Retinopathy Assessment of Severity Dataset: MESSIDOR The threshold is expressed as a percentage (p) of the symmetry measure S of normal ROIs used in the abnormality detection task
-Showcase 1 Retinopathy Detection of Multiple Abnormalities: Hemorrhage, Hard Exudates, Drusen Dataset: DMED, MESSIDOR and Diaretdb 0 Normal Cases - 362 Abnormal Cases - 302
Computer Aided Diagnosis Disease Screening Proposed Methodology -Showcase 1 Retinopathy -Showcase 2 Breast Cancer PART V – Classification of Lesions in Mammograms
-Showcase 2 Breast Cancer Assessment of Mammographic Lesions • Breast cancer is responsible for about 30 percent of all new cancer cases with a high mortality rate in women • Screening for its early detection with mammograms has been explored for more than 3 decades now with moderate success • Correct classification of anomalous areas in the mammograms through visual examination is challenging even for experts Sample Benign and Malignant lesions in Mammograms
-Showcase 2 Breast Cancer Existing Approaches in Mammogram Analysis • 1 - Lesions are first detected from mammograms • 2 - Malignancy of detected lesions are identified using several texture and shape features • Typical features used – – – size shape density Smoothness of borders Brightness and contrast local intensity distribution • The feature space is very large and complex due to the wide diversity of the normal tissues and the variety of the abnormalities
-Showcase 2 Breast Cancer Classification of Mammographic Lesions • Given a lesion, its malignancy is of question • Features derived over motion pattern is used for learning the behavior of benign class • Any deviation in lesion property is identified as a sign of malignancy Benign lesions Malignant lesions
-Showcase 2 Breast Cancer Motion Pattern – Class Discriminability • Three sample benign and malignant lesions were selected • Motion pattern was applied using rotation and translation to analyze class discriminability between benign and malignant class • Maximum and Mean are the coalescing functions used
-Showcase 2 Breast Cancer Classification Performance (ROC Curve) Mini-MIAS Benign - 68 Malignant – 51 Benign lesions for training - 20 • An evaluation of the proposed scheme for learning normal subspace was conducted using KNN classifier • The value of K was considered as 3 for computing the sensitivity and specificity values in the classification tasks • An ROC curve is drawn by varying the normalized Euclidean distance from [0 -1]
Conclusion • We identified and listed the challenges in image based disease screening for diabetic retinopathy and breast cancer • We proposed and evaluated a method for abnormality detection and assessment – a hierarchical approach to the problem of abnormality detection • Evaluation of the proposed hierarchical approach has been performed – on several publicly image datasets of CFI and mammograms – improvement in the disease detection performance over methods in literature
Acknowledgement • This work is dedicated to my Parents and Teachers • Extremely grateful to Prof. Jayanthi Sivaswamy for giving me the opportunity to pursue MS by research • Thankful to all lab mates in CVIT for their support • Guidance of Gopal and Mayank was extremely valuable • Debates and discussion with Sandeep, Kartheek and Saurabh were always insightful
Publications 1. Patents (a) Jayanthi Sivaswamy, N V Kartheek Medathati, K Sai Deepak, A System for generating Generalized Moment Patterns, Submitted to Indian Patent Office, 2010 (Application Number 3939 -CHE-2010) 2. Papers Conference (a) K Sai Deepak, Gopal Datt Joshi, Jayanthi Sivaswamy, Content-Based Retrieval of Retinal Images for Maculopathy, ACM International Health Informatics Symposium, November, 2010 Journal (a) K Sai Deepak, N V Kartheek Medathati and Jayanthi Sivaswamy, Detection and Discrimination of disease related abnormalities, Elsevier Pattern Recognition 2011 (In Press) (b) K Sai Deepak, Jayanthi Sivaswamy, Automatic Assessment of Macular Edema from Color Retinal Images, IEEE Transactions on Medical Imaging 2011
Supplementary Slides
Computer Aided Diagnosis Imaging Modalities Optical Imaging - Ophthalmology • • High resolution optical camera Pupil may be dilated before imaging Pixel resolutions typically range from 0. 5 K to ~2 K*2 K Radiometric resolution is typically 8 bits per channel X-ray Imaging - Mammography • • Low energy X-ray scanner Displays change of density among tissues Pixel resolutions can range from 1 K 2 to 3 K 2 Radiometric resolution 8 -12 bits
Disease Screening CAD in Disease Screening – Diabetic Retinopathy Hemorrhage Detection FP 1 Exudate Detection FP 2 Neovascularization Detection FP 3 Microaneurysms Detection FP 4 Maximum False alarms in disease centric approach – FP 1 + FP 2 + FP 3 + FP 4
Disease Screening CAD – Retinopathy (Color Fundus Image)
Disease Screening CAD – Breast Lesions (Mammograms) Benign Lesion Malignant Lesion
Disease Screening Illumination and Contrast • Presence of one or more of additive bias, multiplicative bias and difference in brightness • These variations often increases the complexity of modeling the normal background especially when there can be several other structures present in the normal image
Disease Screening Tissue Variation (Pigmentation & Density) • Tissue characteristics for the same structure can vary across race and often across patients, within a race. • This variation manifests as differences in intensity, hue and/or pigmentation • These variations can be significant enough for an automated disease detection technique to classify an image as abnormal
Computer Aided Diagnosis CAD with Images - Visualization 52 year old Patient with Back Pain Windowing Tissues of varying densities can be examined MAP of Sagittal view Bones appear bright in X-ray
Computer Aided Diagnosis CAD with Images - Detection Normal Retina Abnormal Retina
Computer Aided Diagnosis CAD with Images – Segmentation Original Image Vessels Segmented
Feature Extraction -Showcase 1 Retinopathy • To effectively describe motion pattern, we use a descriptor derived from the Radon space - is the integral of motion pattern along a line oriented at The desired feature vector is obtained by concatenating projections and distance from the origin from each bin at different orientations
PCA DD Wd*k is a matrix of first k eigen vectors Vector X is projected on the new sub-space Xproj = W(WTW)-1 WX Re-construction error e(X) is computed as, e(X) = || X - Xproj||2 -Showcase 1 Retinopathy
Скачать презентацию Detection and Assessment of Abnormality in Medical Images
70908b888ee3b73a7740dcc4e4e63128.ppt