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
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
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ABSTRACT: Delineation of lung fields in presence of diffuse lung diseases (DLPDs), such as interstitial pneumonias (IP), challenges segmentation algorithms. To deal with IP patterns affecting the lung border an automated image texture classification scheme is proposed. The proposed segmentation scheme is based on supervised texture classification between lung tissue (normal and abnormal) and surrounding tissue (pleura and thoracic wall) in the lung border region. This region is coarsely defined around an initial estimate of lung border, provided by means of Markov Radom Field modeling and morphological operations. Subsequently, a support vector machine classifier was trained to distinguish between the above two classes of tissue, using textural feature of gray scale and wavelet domains. 17 patients diagnosed with IP, secondary to connective tissue diseases were examined. Segmentation performance in terms of overlap was 0.924±0.021, and for shape differentiation mean, rms and maximum distance were 1.663±0.816, 2.334±1.574 and 8.0515±6.549 mm, respectively. An accurate, automated scheme is proposed for segmenting abnormal lung fields in HRC affected by IPJournal of Instrumentation 07/2009; 4(07):P07013. · 1.66 Impact Factor
- MICCAI workshop MCBR-CDS 2012; 01/2013