eprintid: 6297
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/62/97
datestamp: 2023-11-09 16:18:03
lastmod: 2023-11-09 16:18:03
status_changed: 2023-11-09 16:05:35
type: article
metadata_visibility: show
creators_name: Gardezi, S.J.S.
creators_name: Faye, I.
title: Fusion of completed local binary pattern features with curvelet features for mammogram classification
ispublished: pub
note: cited By 12
abstract: In this paper, fusion of texture features to improve classification accuracy by false positive reduction in mammograms is proposed. The method uses texture features obtained from completed local binary pattern (CLBP) and grey level texture features obtained from the Curvelet sub-bands. In the current experiments, mass and normal patches were obtained from Mammographic image analysis Society (MIAS) and Image retrieval in medical applications (IRMA) datasets for mammograms. Texture features from both methods are combined together to obtain the feature fusion matrix. Then Nearest neighbor classifier was used for classification to evaluate the individual as well as enhanced features obtained from CLBP and curvelet. The classifier produces a classification accuracy of 96.68 with 98.9 sensitivity and the false positive (FP) rates drop by 40 and 78 respectively for the enhanced features as compared to the original results produced by both methods. The experimental results suggest that fusion of features improves the performance of the system and is statistically significant. © 2015 NSP Natural Sciences Publishing Cor.
date: 2015
publisher: Natural Sciences Publishing Co.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938895674&doi=10.12785%2famis%2f090633&partnerID=40&md5=42ce5c1bd4e85407525441f018691de9
id_number: 10.12785/amis/090633
full_text_status: none
publication: Applied Mathematics and Information Sciences
volume: 9
number: 6
pagerange: 3037-3048
refereed: TRUE
issn: 19350090
citation:   Gardezi, S.J.S. and Faye, I.  (2015) Fusion of completed local binary pattern features with curvelet features for mammogram classification.  Applied Mathematics and Information Sciences, 9 (6).  pp. 3037-3048.  ISSN 19350090