Authors:
Mohamed Abdel-Nasser
1
;
Domenec Puig
1
;
Antonio Moreno
1
;
Adel Saleh
1
;
Joan Marti
2
;
Luis Martin
3
and
Anna Magarolas
3
Affiliations:
1
Rovira i Virgili University, Spain
;
2
University of Girona, Spain
;
3
Hospital Universitari Joan XXIII, Spain
Keyword(s):
Feature Extraction, Fuzzy Logic, Classification, X-ray Images, Ultrasound Images.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Medical Image Applications
Abstract:
Accurate breast mass detection in mammographies is a difficult task, especially with dense tissues. Although
ultrasound images can detect breast masses even in dense breasts, they are always corrupted by noise. In this
paper, we propose fuzzy local directional patterns for breast mass detection in X-ray as well as ultrasound
images. Fuzzy logic is applied on the edge responses of the given pixels to produce a meaningful descriptor.
The proposed descriptor can properly discriminate between mass and normal tissues under different conditions
such as noise and compression variation. In order to assess the effectiveness of the proposed descriptor, a
support vector machine classifier is used to perform mass/normal classification in a set of regions of interest.
The proposed method has been validated using the well-known mini-MIAS breast cancer database (X-ray
images) as well as an ultrasound breast cancer database. Moreover, quantitative results are shown in terms of
area under the curve o
f the receiver operating curve analysis.
(More)