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Microcalcification Oriented Content Based Mammogram Retrieval for Breast Cancer DiagnosisL. TSOCHATZIDISK. ZAGORISM. SAVELONASN.PAPAMARKOSI. PRATIKAKISN. ARIKIDISL. COSTARIDOU
Visual Computing Group Department of Electrical and Computer EngineeringDemocritus University of Thrace
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
International Conference on Imaging Systems & Techniques (IST 2014)
2Mammography
Diagnostic and screening tool of breasts
Dominant imaging modality for early detection of breast cancer
Breast cancer appears as a mass and/or microcalcifications
The diagnosis is difficult that leads to unnecessary biopsies
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
3Computer Aided Decision (CAD)
Consists of two sub-categories: Systems for detecting an abnormality - Computer Aided Detection (CADe) Systems for diagnosing an abnormality - Computer Aided Diagnosis (CADx)
CAD systems usually employ classification schemes for benign-malignant discrimination
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
4Proposed CBIR-CAD System
Proposed CAD system incorporates a CBIR step prior to decision
Retrieve similar images based on low-level image features Provide visual aid Enables consulting previous cases Leading to increased confidence into incorporating CAD-cued results
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
5CBIR-CAD’s pipeline
October14-17, 2014
BENIGN
International Conference on Imaging Systems & Techniques (IST 2014)
6CBIR Architecture
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
7Feature Extraction (1)
Cluster area: The number of pixels inside the convex hull of the cluster Density: The number of detected MCs divided by the cluster’s area Mean distance to cluster centroid Standard Deviation of areas: The standard deviation of the calculated
area of each individual MC (number of pixels). Standard Deviation of perimeters: The standard deviation of the
calculated perimeter of each individual MC (number of pixels).
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
8Feature Extraction (2)
Standard deviation of Compactness factor:
Mean and Standard deviation of Elongation factor:
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
9Feature Extraction – Contourlets (3)
Sub-bands represent local image structure Oriented and multi-scale edge filtering mechanism
Textural features extracted from sub-bands: Entropy Correlation Information correlation
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
10CBIR Architecture
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
11The SVM Layer – Support Vector Machines
Binary Linear Classifiers For non-linear problems: Projection
of samples to a higher dimensionality space.
Finds a hyper-plane that optimally separates the two classes
Participation value:
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
12The SVM Layer – Structure
An ensemble of binary SVM classifiers is employed
One SVM for each class – Four SVMs in total
those four classes are BI-RADS MC classes
Each SVM outputs the participation level of a sample in the corresponding class
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
13Classes Definition – Amorphous
Hazy and Indistinct Without a clearly defined shape
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
14Classes Definition – Fine Linear Branching
Thin, linear or curvilinear May be discontinuous
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
15Classes Definition – Pleomorphic
Vary in size and shape More conspicuous than the
amorphous calcifications
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
16Classes Definition – Punctate
Round Very small ( < 0.5 mm) Uniform in appearance
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
17CBIR Architecture
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
18Experimental Results
Experiments on a dataset of total 87 mammograms (CC views) from DDSM
Semi-automatic contour delineation from expert radiologist
The 2/3 of dataset was used for the SVM training The Rest 1/3 was used as test set
Comparison between proposed method and the typical, unsupervised one.
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
19Experimental Results – Evaluation metrics
Precision at N (P@N): The percentage of correct images at the top-N places of the rank list (N=5)
Mean Average Precision (MAP): Measures the overall performance of a query
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
20Experimental Results
Classes Unsupervised CBIR Supervised CBIRP@5 MAP P@5 MAP
Amorphous 0.56 0.62 0.60 0.63FLB 0.53 0.58 0.60 0.61Pleomorphic 0.48 0.49 0.60 0.62Punctate 0.26 0.42 0.46 0.52Average 0.46 0.52 0.57 0.60
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
21Experimental Results – Amorphous
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
22Experimental Results – Fine Linear Branching
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
23Experimental Results – Pleomorphic
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
24Experimental Results – Punctate
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
25Conclusions
CBIR system for retrieval of microcalcification clusters on mammograms
The supervised CBIR offers enhanced results as compared to the unsupervised one.
The final vectors used are very small compared to the initial feature vectors
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
26Future Work
Customization of feature sets per SVM Consider accompanying clinical/textual/multimodal data Integration of CBIR within the context of a complete mammographic
CAD system
October14-17, 2014
International Conference on Imaging Systems & Techniques (IST 2014)
27
Thank you!Ευχαριστώ Πολύ!
October14-17, 2014