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Detection and Assessment of Abnormality in Medical Images


Detection and Assessment of Abnormality in Medical Images MS Thesis Presentation Candidate: K Sai Deepak Adviser: Prof. Jayanthi Sivaswamy Center for Visual ... – PowerPoint PPT presentation

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Title: Detection and Assessment of Abnormality in Medical Images

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
  • 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
PART I Computer Aided Diagnosis
Computer Aided Diagnosis (CAD)
Computer Aided Diagnosis
  • 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

CAD with Images
Computer Aided Diagnosis
  • Visualization enhancement for visual analysis
    (Ex. Windowing, MIP, MAP, AIP, Zoom, Contrast
    Inversion etc.)
  • Detection detect the presence of disease
  • Localization and Segmentation Localize or
    segment the spatial regions containing disease
  • Other utilities can be used for measurement of
    various structures from images (length, volume
    etc. )

Healthcare Primary Care and Disease Screening
Computer Aided Diagnosis
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
CAD in Primary Care
Computer Aided Diagnosis
  • Traditionally CAD has been used in Primary Care

CAD in Primary Care
Computer Aided Diagnosis
  • 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
  • The diagnosis report is used by doctor for
    planning treatment

PART II Disease Screening
Disease Screening
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

CAD in Disease Screening
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
  • Multiple CAD tools are used for identifying
    different Diabetic Retinopathy (DR)
  • 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

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,
31193198, 2007.
Summary of Challenges
Disease Screening
  • 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
  • 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

Other Challenges Disease Vs Normal Background
Disease Screening
PART III Proposed Methodology
Detecting Abnormality instead of Disease
Proposed Methodology
  • Non conformance to expected behaviour (normal) in
    the data is considered as abnormality
  • 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
  • Mammographic screening for detecting malignancy
    of lesions

Two Stage Methodology for CAD
Proposed Methodology
  • Stage 1- Detection of abnormality
  • Derive motion pattern for detection of lesions
  • Extract relevant features to represent normal
  • Detect outliers as abnormal
  • Stage2-Assessment of abnormality
  • Derive relevant features based on domain
    knowledge from abnormal cases
  • Determine the severity of disease

Two Stage Methodology for CAD
Proposed Methodology
  • Stage 1- Detection of abnormality
  • Only normal cases are required for disease
  • 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
  • Stage2-Assessment of abnormality
  • Fewer normal cases to be examined by experts

Motion Pattern Detecting Localized Lesions
Proposed Methodology
  • Motivation - Effect of motion on human visual
    system and detectors in camera
  • Spatial/temporal averaging of intensities in
  • 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

Original Image (Uniform Background)
Rotational Motion Pattern
PART IV Detection and Assessment of Macular
Macular Edema Detection and Assessment
-Showcase 1- Retinopathy
  • Diabetic Macular Edema (DME) is a sight
    threatening condition that occurs due to diabetic
  • DME requires immediate referral to
  • Presence of Hard Exudates is used as an indicator
    of DME during retinal disease screening

Existing Approaches in DME Detection
-Showcase 1- Retinopathy
  • 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

Proposed Workflow
-Showcase 1- Retinopathy
  • Steps
  • Landmark Detection and Region of Interest
  • Generation of Motion Patterns
  • Feature Selection
  • Abnormality Detection
  • Abnormality Assessment

Detection of Landmarks in CFI
-Showcase 1- Retinopathy
Singh, J. and Joshi, G. D. and Sivaswamy, J.
Appearance-based object detection in colour
retinal images. In ICIP, pages 14321435,
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.
948951, 2010.
Selection of ROI
-Showcase 1- Retinopathy
Motion Pattern Rotational Motion
-Showcase 1- Retinopathy
Effect of sampling rate on motion pattern
(decreasing rotation steps)-
  • Coalescing Function
  • Mean - Arithmetic mean of all samples were
  • Extrema Maximum or Minimum of all samples are
    taken at each pixel location

Selection of Motion Pattern
-Showcase 1- Retinopathy
  • 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
- Shannons entropy
Selection of Parameters Class Discriminability
-Showcase 1- Retinopathy
Size of normal retina 150150 Neighborhood size
Motion Pattern for Edema Detection
-Showcase 1- Retinopathy
  • 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
  • Maxima is used as the coalescing function
  • Features derived on motion pattern are used for
    learning the normal sub-space and detecting

More Motion Patterns
-Showcase 1- Retinopathy
Sample ROIs and Motion Pattern (S- Subtle Hard
Normal ROI
Abnormal ROI
Feature Extraction
-Showcase 1- Retinopathy
  • To effectively describe motion pattern, we use a
    descriptor derived from the Radon space
  • 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

Detection Performance (ROC Curves)
-Showcase 1- Retinopathy
Receiver Operating Characteristic curve
  • DMED - 122 images
  • Normal - 68
  • Abnormal 54
  • Normal images used for training - 18
  • MESSIDOR 400 images
  • Normal - 274
  • Abnormal 126
  • Immediate referral - 85
  • Normal images used for training 74
  • Diaretdb0 db1 122 images
  • Normal 25
  • Abnormal - 97
  • Combined Dataset 644 images
  • Normal 367
  • Abnormal - 277

Comparison against Disease Centric Methods
-Showcase 1- Retinopathy
  • 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.
Detection of subtle hard exudates
-Showcase 1- Retinopathy
Assessment of Severity
-Showcase 1- Retinopathy
  • Macula is devoid of significant vasculature
  • It is characterized by rough rotationally

Assessment of Severity
-Showcase 1- Retinopathy
The threshold is expressed as a percentage (p) of
the symmetry measure S of normal ROIs used in the
abnormality detection task
Detection of Multiple Abnormalities
-Showcase 1- Retinopathy
Abnormalities Hemorrhage, Hard Exudates, Drusen
Dataset DMED,MESSIDOR and Diaretdb0
Normal Cases - 362 Abnormal Cases - 302
PART V Classification of Lesions in Mammograms
Assessment of Mammographic Lesions
-Showcase 2- Breast Cancer
  • 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

Existing Approaches in Mammogram Analysis
-Showcase 2- Breast Cancer
  • 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

Classification of Mammographic Lesions
-Showcase 2- Breast Cancer
  • 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
Motion Pattern Class Discriminability
-Showcase 2- Breast Cancer
  • Three sample benign and malignant lesions were
  • Motion pattern was applied using rotation and
    translation to analyze class discriminability
    between benign and malignant class
  • Maximum and Mean are the coalescing functions

Classification Performance (ROC Curve)
-Showcase 2- Breast Cancer
  • 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
  • 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

  • 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
  • improvement in the disease detection performance
    over methods in literature

  • This work is dedicated to my Parents and Teachers
  • Extremely grateful to Prof. Jayanthi Sivaswamy
    for giving me the opportunity to pursue MS by
  • Thankful to all lab mates in CVIT for their
  • Guidance of Gopal and Mayank was extremely
  • Debates and discussion with Sandeep, Kartheek and
    Saurabh were always insightful

  • 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
Imaging Modalities
Computer Aided Diagnosis
Optical Imaging - Ophthalmology
X-ray Imaging - Mammography
  • High resolution optical camera
  • Pupil may be dilated before imaging
  • Pixel resolutions typically range from 0.5K to
  • Radiometric resolution is typically 8 bits per
  • Low energy X-ray scanner
  • Displays change of density among tissues
  • Pixel resolutions can range from 1K2 to 3K2
  • Radiometric resolution 8-12 bits

CAD in Disease Screening Diabetic Retinopathy
Disease Screening
Hemorrhage Detection
Exudate Detection
Neovascularization Detection
Microaneurysms Detection
Maximum False alarms in disease centric approach
CAD Retinopathy (Color Fundus Image)
Disease Screening
CAD Breast Lesions (Mammograms)
Disease Screening
Benign Lesion
Malignant Lesion
Illumination and Contrast
Disease Screening
  • 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

Tissue Variation (Pigmentation Density)
Disease Screening
  • 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

CAD with Images - Visualization
Computer Aided Diagnosis
52 year old Patient with Back Pain
MAP of Sagittal view Bones appear bright in X-ray
Windowing Tissues of varying densities can be
CAD with Images - Detection
Computer Aided Diagnosis
Normal Retina
Abnormal Retina
CAD with Images Segmentation
Computer Aided Diagnosis
Vessels Segmented
Original Image
Feature Extraction
-Showcase 1- Retinopathy
  • To effectively describe motion pattern, we use a
    descriptor derived from the Radon space

-Showcase 1- Retinopathy
Wdk 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 - Xproj2