A hybrid hierarchical framework for classification of breast density using digitized film screen mammograms

A hybrid hierarchical framework for classification of breast density using digitized film screen... In the present work, a hybrid hierarchical framework for classification of breast density using digitized film screen mammograms has been proposed. For designing of an efficient classification framework 480 MLO view digitized screen film mammographic images are taken from DDSM dataset. The ROIs of fixed size i.e. 128 × 128 pixels are cropped from the center area of the breast (i.e. the area where glandular ducts are prominent). A total of 292 texture features based on statistical methods, signal processing based methods and transform domain based methods are computed for each ROI. The computed feature vector is subjected to PCA for dimensionality reduction. The reduced feature space is fed to the classification module. In this work 4-class breast density classification has been conducted using hierarchical framework where the first classifier is used to classify an unknown test ROI into B-I/other class. If the test ROI is predicted as other class, it is inputted to second classifier for the classification into B-II/dense class. If the test ROI is predicted as belonging to dense class, it is inputted to classifier for the classification into B-III/B-IV class. In this work five hierarchical classifiers designs consisting of 3 PCA-kNN, 3 PCA-PNN, 3 PCA-ANN, 3 PCA-NFC and 3 PCA-SVM classifiers has been proposed. The obtained maximum OCA value is 80.4% using PCA-NFC in hierarchical approach. Further, the best performing individual classifiers are clubbed together in a hierarchical framework to design hybrid hierarchical framework for classification of breast density using digitized screen film mammograms. The proposed hybrid hierarchical framework yields the OCA value of 84.1%. The result achieved by the proposed hybrid hierarchical framework is quite promising and can be used in clinical environment for differentiation between different breast density patterns. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

A hybrid hierarchical framework for classification of breast density using digitized film screen mammograms

Loading next page...
 
/lp/springer_journal/a-hybrid-hierarchical-framework-for-classification-of-breast-density-jK18cf5bIK
Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-016-4340-z
Publisher site
See Article on Publisher Site

Abstract

In the present work, a hybrid hierarchical framework for classification of breast density using digitized film screen mammograms has been proposed. For designing of an efficient classification framework 480 MLO view digitized screen film mammographic images are taken from DDSM dataset. The ROIs of fixed size i.e. 128 × 128 pixels are cropped from the center area of the breast (i.e. the area where glandular ducts are prominent). A total of 292 texture features based on statistical methods, signal processing based methods and transform domain based methods are computed for each ROI. The computed feature vector is subjected to PCA for dimensionality reduction. The reduced feature space is fed to the classification module. In this work 4-class breast density classification has been conducted using hierarchical framework where the first classifier is used to classify an unknown test ROI into B-I/other class. If the test ROI is predicted as other class, it is inputted to second classifier for the classification into B-II/dense class. If the test ROI is predicted as belonging to dense class, it is inputted to classifier for the classification into B-III/B-IV class. In this work five hierarchical classifiers designs consisting of 3 PCA-kNN, 3 PCA-PNN, 3 PCA-ANN, 3 PCA-NFC and 3 PCA-SVM classifiers has been proposed. The obtained maximum OCA value is 80.4% using PCA-NFC in hierarchical approach. Further, the best performing individual classifiers are clubbed together in a hierarchical framework to design hybrid hierarchical framework for classification of breast density using digitized screen film mammograms. The proposed hybrid hierarchical framework yields the OCA value of 84.1%. The result achieved by the proposed hybrid hierarchical framework is quite promising and can be used in clinical environment for differentiation between different breast density patterns.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Feb 6, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off