An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection

Presents a novel approach for segmentation of suspicious mass regions in digitized mammograms using a new adaptive density-weighted contrast enhancement (DWCE) filter in conjunction with Laplacian-Gaussian (LG) edge detection. The DWCE enhances structures within the digitized mammogram so that a sim...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on medical imaging 1996-02, Vol.15 (1), p.59-67
Main Authors: Petrick, N., Heang-Ping Chan, Sahiner, B., Datong Wei
Format: Article
Language:English
Subjects:
Adaptive filters
Biological and medical sciences
Biopsy
Breast cancer
Classification algorithms
Costs
Feature extraction
Gynecology. Andrology. Obstetrics
Image edge detection
Lesions
Linear discriminant analysis
Mammary gland diseases
Medical sciences
Radiology
Tumors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Presents a novel approach for segmentation of suspicious mass regions in digitized mammograms using a new adaptive density-weighted contrast enhancement (DWCE) filter in conjunction with Laplacian-Gaussian (LG) edge detection. The DWCE enhances structures within the digitized mammogram so that a simple edge detection algorithm can be used to define the boundaries of the objects. Once the object boundaries are known, morphological features are extracted and used by a classification algorithm to differentiate regions within the image. This paper introduces the DWCE algorithm and presents results of a preliminary study based on 25 digitized mammograms with biopsy proven masses. It also compares morphological feature classification based on sequential thresholding, linear discriminant analysis, and neural network classifiers for reduction of false-positive detections.
ISSN:0278-0062
1558-254X
DOI:10.1109/42.481441