Segmentation of Pulmonary Nodules Based on Statistic Features of Wavelet Coefficients and Dual Level Sets

A major problem of pulmonary nodules segmentation can't be solved well by conventional methods, which is other tissue in chest CT image slices, such as blood vessels and bronchi, often overlap with the nodules and they also have the same gray scale intensity approximately, for big size (>40p...

Full description

Saved in:
Bibliographic Details
Main Authors: Hui-yan Jiang, Zhen-yu Cheng
Format: Conference Proceeding
Language:English
Subjects:
Biomedical imaging
Blood vessels
Cancer
chest CT image
Computed tomography
dual level sets
Image segmentation
Level set
Lungs
pulmonary nodules
Respiratory system
Statistics
Wavelet coefficients
wavelet transform
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A major problem of pulmonary nodules segmentation can't be solved well by conventional methods, which is other tissue in chest CT image slices, such as blood vessels and bronchi, often overlap with the nodules and they also have the same gray scale intensity approximately, for big size (>40pixels) nodules especially. This paper presents a novel approach to solve above problem, which works in two main steps: (1) transition region (TR), which is defined as the ambiguous region between nodule and background, is ascertained depending on statistic features of wavelet coefficients. (2) Precise boundaries of the nodule is segmented based on an improvement of dual level sets method. The validity of the proposed approach is demonstrated in the chest CT images. Experiments with real chest CT images confirm the high accuracy of our approach.
DOI:10.1109/ICCME.2007.4381817