NDLI: Superpixel texture analysis for classification of breast masses in dense background

Content Provider	IET Digital Library
Author	Sajeev, Shelda Bajger, Mariusz Lee, Gobert
Abstract	Finding masses in dense background is a difficult task for even experienced radiologist. It is due to the similarity of intensity between the masses and the overlapped normal dense tissues. A novel method for classification of masses localised in dense background of breast is proposed. Nine structured superpixel patterns were generated using local binary pattern technique on superpixels. Analysis of these nine structured superpixel patterns revealed the most prominent ones, allowing for successful classification of malignant masses and normal dense breast regions. Two mammographic databases were used to evaluate the proposed approach: the publicly available digital database for screening mammography (DDSM), and a local database of mammograms (BreastScreen SA, BSSA). A total of 525 regions of interest (ROIs) were used (301 extracted from DDSM and 224 from BSSA). All 525 ROIs were localised in dense backgrounds of breasts. The results indicate that features generated from structured superpixel patterns can produce very effective and efficient texture descriptors of breast masses localised in dense background. Using Fisher linear discriminant analysis classifier, an area under the receiver operating characteristic curve score of 0.96 was achieved for DDSM and 0.93 for BSSA with only six features.
Starting Page	779
Ending Page	786
Page Count	8
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 6, Sep (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2017.0586
Journal	IET Computer Vision
Publisher Date	2018-03-21
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biology And Medical Computing Biomedical Imaging/measurement Breast Masses Cancer Computer Vision And Image Processing Technique Dense Background Feature Extraction Image Classification Image Texture Local Binary Pattern Technique Malignant Masses Mammography Medical Image Processing Normal Dense Breast Region Optical, Image And Video Signal Processing Overlapped Normal Dense Tissues Patient Diagnostic Method And Instrumentation Radiography And Computed Tomography Statistics Structured Superpixel Pattern Superpixel Texture Analysis Superpixels X-Ray Technique X-Rays And Particle Beam
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Analysis of 2D singularities for mammographic mass classification

Curvelet analysis of breast masses on dynamic magnetic resonance mammography

Mammographic mass classification according to Bi-RADS lexicon

Breast mass classification method based on convolutional neural networks

Anomaly classification in digital mammography based on multiple-instance learning

Mammographic mass classification using filter response patches

Text segmentation using superpixel clustering

Fast and robust superpixel generation method

Development of an intelligent CAD system for mass detection in mammographic images

Superpixel texture analysis for classification of breast masses in dense background

Similar Documents

Analysis of 2D singularities for mammographic mass classification

Curvelet analysis of breast masses on dynamic magnetic resonance mammography

Mammographic mass classification according to Bi-RADS lexicon

Breast mass classification method based on convolutional neural networks

Anomaly classification in digital mammography based on multiple-instance learning

Mammographic mass classification using filter response patches

Text segmentation using superpixel clustering

Fast and robust superpixel generation method

Development of an intelligent CAD system for mass detection in mammographic images

Superpixel texture analysis for classification of breast masses in dense background