a two stage svm-based mammographic cbir for ca dx
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03/05/2023 TSOCHATZIDIS ET AL. A TWO-STAGE SVM-BASED MAMMOGRAPHIC CBIR FOR CADX, MICCAI-BIA 2015 1
A two-stage SVM-based mammographic CBIR for CADxL. TSOCHATZIDIS 1
A. KARAHALIOU 2
K. ZAGORIS 1
S. SK IADOPOULOS 2
N. ARIK IDIS 2
L. COSTARIDOU 2
I . PRATIKAKIS 1
University of Patras 2School of MedicineDepartment of Medical Physics
Democritus University of Thrace 1Department of Electrical and Computer EngineeringVisual Computing Group
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03/05/2023 TSOCHATZIDIS ET AL. A TWO-STAGE SVM-BASED MAMMOGRAPHIC CBIR FOR CADX, MICCAI-BIA 2015 2
CADx in MammographyMammography is a dominant imaging modality for early detection of breast cancer
Often, diagnosis leads to unnecessary biopsies
Two types of CADx:• Single-stage: Classification schemes for benign-
malignant discrimination• Two-stage: Content-Based Image Retrieval that
feeds the diagnosis step which discriminates between benign and malignant
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03/05/2023 TSOCHATZIDIS ET AL. A TWO-STAGE SVM-BASED MAMMOGRAPHIC CBIR FOR CADX, MICCAI-BIA 2015 3
Proposed CBIR-CAD SystemCAD system that incorporates a CBIR step and a decision step
Retrieves similar images based on low-level image features
Margin specific CBIR
Diagnosis is based on the ranked lists produced by CBIR
Provides visual aid and enables consultation of previous cases, leading to increased confidence into incorporating CAD-cued results
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03/05/2023 TSOCHATZIDIS ET AL. A TWO-STAGE SVM-BASED MAMMOGRAPHIC CBIR FOR CADX, MICCAI-BIA 2015 4
Margin-type classesCircumscribed
Spiculated
Microlobulated
Ill defined (+Obscured)
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CBIR-CAD’s pipeline
BENIGN / MALIGNANT
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03/05/2023 TSOCHATZIDIS ET AL. A TWO-STAGE SVM-BASED MAMMOGRAPHIC CBIR FOR CADX, MICCAI-BIA 2015 6
Semi-automatic SegmentationThe Dijkstra’s shortest path algorithm is exploited to obtain the optimal path between sequential pairs of landmark points upon mass boundaries
A new cost function is proposed to avoid background correction techniques that may deform mass contour and introduce additional adjustment parameters
Arikidis, N., Skiadopoulos, S., Karahaliou, A., Kazantzi, A., Vassiou, K., Tsochatzidis, L., Pratikakis, I., Costaridou, L.: Shortest paths of mass contour estimates in mammography. In: MICCAI-BIA 2015
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03/05/2023 TSOCHATZIDIS ET AL. A TWO-STAGE SVM-BASED MAMMOGRAPHIC CBIR FOR CADX, MICCAI-BIA 2015 7
CBIR Architecture
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Feature Extraction – Global ShapeSolidity factor: The degree that the shape deviates from its convex hull
Compactness factor: The degree that a shape deviates from a perfect circle
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03/05/2023 TSOCHATZIDIS ET AL. A TWO-STAGE SVM-BASED MAMMOGRAPHIC CBIR FOR CADX, MICCAI-BIA 2015 9
Feature Extraction – Global Shape
Circumscribed Microlobulated Spiculated Ill-defined
Compactness=Solidity=0.99
Compactness=Solidity=0.92
Compactness=Solidity=0.32
Compactness=Solidity=0.93
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Feature Extraction – DFT of NRLNormalized Radial Length Function
1. The distance of each contour point to the shape’s center of gravity
2. Normalized by the average radial length
3. Computation of Discrete Fourier Transform coefficients
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Feature Extraction - Texture-basedRubber Band Straightening Transform (RBST)
•Unfolding the ribbon around the contour as a flat image
•RBST Column line segment normal to the contour
•RBST Row iso-distant to the contour paths
•Intensity profiles at every contour point along a line segment normal to the contour
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Feature Extraction - Texture-based
RBST Image
Sobel gradient magnitude operator
Detected edge points
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Feature Extraction - Texture-based
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Feature Extraction - Texture-based
Extracted Features
•Distance between edge points of consecutive columns
•Distance between edge points and middle row of RBST image
•Magnitude of gradient on y-axis
•Gradient orientation divergence from vertical direction
•Acutance (The sum of the difference of gray-level values between pixels that are iso-distant from either sides of the contour)
Mean and SD value of the above functions
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03/05/2023 TSOCHATZIDIS ET AL. A TWO-STAGE SVM-BASED MAMMOGRAPHIC CBIR FOR CADX, MICCAI-BIA 2015 15
Feature Extraction - Texture-based
Feature Name Circumscribed Ill-defined
Avg. dist. edge points 0.036 0.747
Avg. dist center row 0.323 0.865
SD dist. edge points 0.105 0.879
SD dist center row 0.077 0.952
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CBIR Architecture
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03/05/2023 TSOCHATZIDIS ET AL. A TWO-STAGE SVM-BASED MAMMOGRAPHIC CBIR FOR CADX, MICCAI-BIA 2015 17
The SVM Layer – Support Vector MachinesBinary Linear Classifiers
For non-linear problems: Projection of samples to a higher dimensionality space.
Finds a hyper-plane that optimally separates the two classes
Decision function:
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03/05/2023 TSOCHATZIDIS ET AL. A TWO-STAGE SVM-BASED MAMMOGRAPHIC CBIR FOR CADX, MICCAI-BIA 2015 18
The SVM Layer – Structure
An ensemble of binary SVM classifiers is employed
One SVM for each class – Four SVMs in total
Each SVM outputs the participation level of a sample in the corresponding class
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CBIR Architecture
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03/05/2023 TSOCHATZIDIS ET AL. A TWO-STAGE SVM-BASED MAMMOGRAPHIC CBIR FOR CADX, MICCAI-BIA 2015 20
The Diagnosis StageGOAL: Provide the likelihood of malignancy for a query case.
•Based on the K most similar ROIs retrieved
•Similarity between query and an item: ,
•Two decisions indices investigated:
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03/05/2023 TSOCHATZIDIS ET AL. A TWO-STAGE SVM-BASED MAMMOGRAPHIC CBIR FOR CADX, MICCAI-BIA 2015 21
Experimental ResultsExperiments on a dataset of total 400 mammograms from DDSM
Precise contour delineation from expert radiologist
CC and MLO views are treated independently
5-fold cross validation
Grid search for SVM and kernel parameters
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Experimental Results – Evaluation metricsPrecision at N (P@R): The percentage of correct images at the top-R places of the rank list
Mean Average Precision (MAP): Measures the overall performance of a query
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Experimental Results - CBIRClasses P@R MAP
Circumscribed
Microlobulated
Spiculated
Ill-defined
Average
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Area under ROC curve (AUC)The Receiver Operating Characteristic (ROC) curve illustrates the performance of a binary classifier as its discrimination threshold is varied
The curve is created by plotting the true positive rate (sensitivity) against the false positive rate (1-specificity) at various threshold settings
The AUC of a classifier is equivalent to the probability that the classifier will rank a randomly chosen positive instance higher than a randomly chosen negative instance
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Experimental Results - Decision
3 4 5 6 7 8 9 10 11 12 13 14 150.74
0.75
0.76
0.77
0.78
0.79
0.8
0.81
0.82
Classification performance of D1 and D2 in terms of Az index.
D1 D2
Maximum using D2 for K=13 ranked items
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ConclusionsTwo-stage CBIR-CAD:• Margin-specific CBIR stage• Diagnosis stage
Incorporation of training into the feature extraction (SVM ensemble)
High-performance for spiculated and microlobulated masses
Lack of standard datasets leads to difficulty in comparison between methods
Future efforts:• Performance improvement for ill-defined masses• Feature selection for each SVM independently• Introduction of weights in decision calculation modifying the significance of each retrieved ROIs
• Use of relevance feedback mechanism to improve performance