Computer Aided Diagnosis of Lung Ground Glass Opacity Nodules and Large Lung Cancers in CT.

Computer Aided Diagnosis of Lung Ground Glass Opacity Nodules and Large Lung Cancers in CT.

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Diagnosis of lung nodules and cancers is a critical and urgent problem in clinical diagnosis. This thesis is to design and build a computer aided lung ground glass opacity (GGO) nodules and large lung cancers diagnosis system which aims to quantify the volumetric change of the lung GGO nodules and large lung cancers between the pre-treatment and post-treatment. In order to quantify the volumetric change of the lung nodules and cancers over time, segmentation and registration methods are used to determine the same lung nodule or cancer between the pre-treatment and post-treatment. We first perform a pre-selection method and extract the centerlines of tubular objects by applying intensity ridge tracing method. While tracing tubular objects, bifurcation points are automatically detected from the cross-sectional planes by applying scan-conversion method or Adaboost algorithm. For the registration method, we develop a 3D-3D model based rigid registration method based on bifurcation points. This rigid registration method minimizes the least square error of the corresponding bifurcation points between the planning CT images and the respiration-correlated CT images. For the lung GGO nodules and large lung cancers detection and segmentation, we propose a novel method to automatically detect and segment lung GGO nodules and large lung cancers from chest CT images. For lung GGO nodules detection, we develop a classifier by boosting k-Nearest Neighbor. We then apply a clustering method to detect the regions of the lung GGO nodules. The detected regions of lung GGO nodules are then automatically segmented. We also present the statistical validation of the proposed classifier for lung GGO (10 datasets contains 10 GGO nodules) detection as well as the promising results of automatic lung GGO nodules segmentation. The improvement of the method of large lung cancers is that we propose a robust active shape model method for automatic segmentation of lung areas which can be distorted by large lung cancers. We present the statistical validation of the proposed classifier for large lung cancers (10 datasets contains 16 large lung cancers) detection as well as the very promising results of automatic large lung cancers segmentation.In addition, manual delineation of tumor contours is time-consuming and lacks the reproducibility. ... Unlike small lung nodules, lung cancers to be treated are often large in size, present spiculate edges, and grow against surroundinganbsp;...


Title:Computer Aided Diagnosis of Lung Ground Glass Opacity Nodules and Large Lung Cancers in CT.
Author: Jinghao Zhou
Publisher:ProQuest - 2008
ISBN-13:

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