Automatic Lung Segmentation and Fissure Detection Based on Anatomical Shape Characteristics in CT Images
Date Issued
2012
Date
2012
Author(s)
Jiang, Jun-Yan
Abstract
Segmentation of the pulmonary lobes is important to localize parenchyma disease inside the lungs and to quantify the distribution of a parenchyma disease. Since the proposed fissure segmentation system can provide a visualization of a patient’s upper and lower lungs, it also could be incorporated in teaching software for medical professionals. Although radiologists might be able to identify lobar boundaries on CT scans, manual delineation of over hundreds CT images is unthinkable in clinical routine. Therefore, computer-aided diagnosis (CAD) is strongly desired to assist radiologists in CT image interpretations.
This work proposed a fissure detection algorithm based on the physiological structure. Before the fissure detection, it is necessary to have a good lung region segmentation. Accordingly, we use 3D region growing to obtain a good lung region in the first step of the proposed algorithm. Next, we separate the lung region to right and left by following the lung wall. Finally, the fissure is segmented by using fissure filter and 3D neutrosophic (NS) filter. The experimental results show that we have proposed algorithm for fissure segmentation has good performance.
Subjects
Lobe segmentation
3D region growing
Right and left lung separation
Fissure Detection
NS filter
Type
thesis
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