A method of tumors detection in digital mammography

Author

Xie, Mei

Author_Institution

Coll. of Electron. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China

Volume

2

fYear

2002

fDate

29 June-1 July 2002

Firstpage

1007

Abstract

We develop a general method for the detection and segmentation of tumors with an analytical model. It uses a multiresolution wavelet analysis in concert with a Bayesian classifier to identify the possible tumors. The method adaptively chooses thresholds to segment tumors from the background by using a multiscale analysis of the image probability density function. A performance analysis based on a Gaussian distribution model is used to show that the proposed adaptive threshold method is effective in segmenting tumors in mammograms. Examples are presented to demonstrate the efficiency of the technique on a variety of targets.

Keywords

Bayes methods; Gaussian distribution; cancer; diagnostic radiography; image classification; image segmentation; mammography; medical image processing; object detection; statistical analysis; tumours; wavelet transforms; Bayesian classifier; Gaussian distribution model; X-rays; digital mammography; image probability density function; multiresolution analysis; multiscale analysis; segmentation; tumor detection; wavelet analysis; Analytical models; Bayesian methods; Image analysis; Image segmentation; Mammography; Neoplasms; Performance analysis; Probability density function; Tumors; Wavelet analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications, Circuits and Systems and West Sino Expositions, IEEE 2002 International Conference on

Print_ISBN

0-7803-7547-5

Type

conf

DOI

10.1109/ICCCAS.2002.1178957

Filename

1178957