Automated 3D Segmentation of Lung Fields in Thin Slice CT Exploiting Wavelet Preprocessing.

Conference Paper · January 2007with7 Reads
DOI: 10.1007/978-3-540-74272-2_30 · Source: DBLP
Conference: Computer Analysis of Images and Patterns, 12th International Conference, CAIP 2007, Vienna, Austria, August 27-29, 2007, Proceedings

    Abstract

    Lung segmentation is a necessary first step to computer analysis in lung CT. It is crucial to develop automated segmentation
    algorithms capable of dealing with the amount of data produced in thin slice multidetector CT and also to produce accurate
    border delineation in cases of high density pathologies affecting the lung border. In this study an automated method for lung
    segmentation of thin slice CT data is proposed. The method exploits the advantage of a wavelet preprocessing step in combination
    with the minimum error thresholding technique applied on volume histogram. Performance averaged over left and right lung volumes
    is in terms of: lung volume overlap 0.983 ± 0.008, mean distance 0.770 ± 0.251 mm, rms distance 0.520 ± 0.008 mm and maximum
    distance differentiation 3.327 ± 1.637 mm. Results demonstrate an accurate method that could be used as a first step in computer
    lung analysis in CT.