microcalcification oriented content based mammogram retrieval for breast cancer diagnosis

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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)


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

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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

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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

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5CBIR-CAD’s pipeline

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BENIGN


6CBIR Architecture

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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).

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8Feature Extraction (2)

Standard deviation of Compactness factor:

Mean and Standard deviation of Elongation factor:

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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

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10CBIR Architecture

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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:

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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

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13Classes Definition – Amorphous

Hazy and Indistinct Without a clearly defined shape

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14Classes Definition – Fine Linear Branching

Thin, linear or curvilinear May be discontinuous

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15Classes Definition – Pleomorphic

Vary in size and shape More conspicuous than the

amorphous calcifications

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16Classes Definition – Punctate

Round Very small ( < 0.5 mm) Uniform in appearance

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17CBIR Architecture

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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.

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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

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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

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21Experimental Results – Amorphous

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22Experimental Results – Fine Linear Branching

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23Experimental Results – Pleomorphic

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24Experimental Results – Punctate

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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

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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

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Thank you!Ευχαριστώ Πολύ!

October14-17, 2014