Automated 3D Segmentation of Lung Fields in Thin Slice CT Exploiting Wavelet Preprocessing.
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.
- SourceAvailable from: Henning Müller[Show abstract] [Hide abstract]
ABSTRACT: We propose near-affine-invariant texture descriptors derived from isotropic wavelet frames for the characterization of lung tissue patterns in high-resolution computed tomography (HRCT) imaging. Affine invariance is desirable to enable learning of nondeterministic textures without a priori localizations, orientations, or sizes. When combined with complementary gray-level histograms, the proposed method allows a global classification accuracy of 76.9% with balanced precision among five classes of lung tissue using a leave-one-patient-out cross validation, in accordance with clinical practice.IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society 05/2012; 16(4):665-75. · 1.69 Impact Factor
- MICCAI workshop MCBR-CDS 2012; 01/2013
Conference Paper: Retrieval of 4D Dual Energy CT for Pulmonary Embolism Diagnosis[Show abstract] [Hide abstract]
ABSTRACT: Pulmonary embolism is a common condition with high short---term morbidity. Pulmonary embolism can be treated successfully but diagnosis remains difficult due to the large variability of symptoms, which are often non---specific including breath shortness, chest pain and cough. Dual energy CT produces 4---dimensional data by acquiring variation of attenuation with respect to spatial coordinates and also with respect to the energy level. This additional information opens the possibility of discriminating tissue with specific material content, such as bone and adjacent contrast. Despite having already been available for clinical use for a while, there are few applications where Dual energy CT is currently showing a clear clinical advantage. In this article we propose to use the additional energy---level data in a 4D dataset to quantify texture changes in lung parenchyma as a way of finding parenchyma perfusion deficits characteristic of pulmonary embolism.Medical Content-based Retrieval for Clinical Decision Support; 10/2012