Next Article in Journal
Interactive Compression of Digital Data
Next Article in Special Issue
Recognition of Pulmonary Nodules in Thoracic CT Scans Using 3-D Deformable Object Models of Different Classes
Previous Article in Journal / Special Issue
A Robust and Fast System for CTC Computer-Aided Detection of Colorectal Lesions
Article Menu

Export Article

Open AccessArticle
Algorithms 2010, 3(1), 44-62; doi:10.3390/a3010044

Breast Cancer Detection with Gabor Features from Digital Mammograms

Alcorn State University, Alcorn State, MS 39096, USA
Received: 28 October 2009 / Revised: 14 January 2010 / Accepted: 14 January 2010 / Published: 19 January 2010
(This article belongs to the Special Issue Machine Learning for Medical Imaging)
View Full-Text   |   Download PDF [831 KB, uploaded 19 January 2010]   |  


A new breast cancer detection algorithm, named the “Gabor Cancer Detection” (GCD) algorithm, utilizing Gabor features is proposed. Three major steps are involved in the GCD algorithm, preprocessing, segmentation (generating alarm segments), and classification (reducing false alarms). In preprocessing, a digital mammogram is down-sampled, quantized, denoised and enhanced. Nonlinear diffusion is used for noise suppression. In segmentation, a band-pass filter is formed by rotating a 1-D Gaussian filter (off center) in frequency space, termed as “Circular Gaussian Filter” (CGF). A CGF can be uniquely characterized by specifying a central frequency and a frequency band. A mass or calcification is a space-occupying lesion and usually appears as a bright region on a mammogram. The alarm segments (suspicious to be masses/calcifications) can be extracted out using a threshold that is adaptively decided upon the histogram analysis of the CGF-filtered mammogram. In classification, a Gabor filter bank is formed with five bands by four orientations (horizontal, vertical, 45 and 135 degree) in Fourier frequency domain. For each mammographic image, twenty Gabor-filtered images are produced. A set of edge histogram descriptors (EHD) are then extracted from 20 Gabor images for classification. An EHD signature is computed with four orientations of Gabor images along each band and five EHD signatures are then joined together to form an EHD feature vector of 20 dimensions. With the EHD features, the fuzzy C-means clustering technique and k-nearest neighbor (KNN) classifier are used to reduce the number of false alarms. The experimental results tested on the DDSM database (University of South Florida) show the promises of GCD algorithm in breast cancer detection, which achieved TP (true positive rate) = 90% at FPI (false positives per image) = 1.21 in mass detection; and TP = 93% at FPI = 1.19 in calcification detection. View Full-Text
Keywords: breast cancer; computer-aided detection (CAD); Gabor filter; edge histogram descriptor (EHD); mammography screening; mass detection; calcification detection breast cancer; computer-aided detection (CAD); Gabor filter; edge histogram descriptor (EHD); mammography screening; mass detection; calcification detection

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zheng, Y. Breast Cancer Detection with Gabor Features from Digital Mammograms. Algorithms 2010, 3, 44-62.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top