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

Author

Petrick, Nicholas ; Chan, Heang-Ping ; Sahiner, Berkman ; Wei, Datong

Author_Institution

Dept. of Radiol., Michigan Univ., Ann Arbor, MI, USA

Volume

15

Issue

1

fYear

1996

fDate

2/1/1996 12:00:00 AM

Firstpage

59

Lastpage

67

Abstract

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

Keywords

adaptive signal processing; diagnostic radiography; edge detection; image segmentation; medical image processing; DWCE algorithm; Laplacian-Gaussian edge detection; adaptive density-weighted contrast enhancement filter; biopsy proven masses; digitized mammograms; false-positive detections reduction; linear discriminant analysis; mammographic breast mass detection; medical diagnostic imaging; morphological feature classification; neural network classifiers; sequential thresholding; suspicious mass regions segmentation; Adaptive filters; Biopsy; Breast cancer; Classification algorithms; Costs; Feature extraction; Image edge detection; Lesions; Linear discriminant analysis; Radiology;

fLanguage

English

Journal_Title

Medical Imaging, IEEE Transactions on

Publisher

ieee

ISSN

0278-0062

Type

jour

DOI

10.1109/42.481441

Filename

481441