Modeling Context in Pulmonary CT Images: Applications to Nodule Classification and Segmentation
Date Issued
2015
Date
2015
Author(s)
Tsou, Chi-Hsuan
Abstract
The task of recognizing every object/region in images acquired with different medical imaging modalities is a key problem in medical image analysis (also called image understanding). Most of the earlier object/organ recognition algorithm assigns a single label to an image, e.g. an image of a heart, a lung or a liver. Some go further in detecting and localizing multiple anatomical structures within three-dimensional computed tomography (CT) scans. Instead of applying to a single or multiple object/organ detection, a few concurrent approaches combine segmentation and recognition into one coherent framework. Incorporating contextual information into coherent framework has proven to enhance performance of higher level tasks such as object recognition or detection. In this thesis, we adopt the concept of image understanding to investigate two key components of computer-aided diagnosis (CAD) system for lung cancer, namely nodule classification and nodule segmentation. Specifically, we ask two questions. First: What information from pulmonary CT images can be helpful as context for lung cancer risk prediction? We show that recognition of anatomic patterns of pulmonary nodules can be potentially useful and robust algorithm in predicting the probability of the malignancy of pulmonary nodules. Second: How can recognition of anatomic structures in pulmonary CT images be performed? We show that given an image, semantically meaningful regions each labeled with a specific lung tissue class can be extracted by unifying the techniques of statistical region merging and conditional random field (CRF) with graph cut optimization.
Subjects
Lung CT images
Nodule classification
Ground-Glass nodule segmentation
Statistical region merging
Conditional random field
Hierarchical segmentation tree
SDGs
Type
thesis
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