Conference Paper

An innovative lung segmentation algorithm in CT images with accurate delimitation of the hilus pulmonis

Dipartimento di Scienza dei Materiali, Università del Salento (Lecce, Italy)
DOI: 10.1109/NSSMIC.2008.4774443 Conference: Nuclear Science Symposium Conference Record, 2008. NSS '08. IEEE
Source: IEEE Xplore


This paper proposes a new segmentation method for the delimitation of the lung parenchyma in thorax Computed-Tomography (CT) datasets, which will be used as pre-processing step in the CAD (Computer Assisted Detection) system for lung nodule detection that is being developed by the MAGIC-5 (Medical Applications in a Grid Infrastructure Connection) Collaboration. Once finished, the CAD software will run in an integrated “grid” environment, where the potentiality of distributed resources for both data and computation will be exploited. The algorithm is fully automated and three-dimensional (3D). Its most innovative part - to the best of our knowledge - is the segmentation of the external airways (trachea and bronchi), obtained by 3D region growing with wavefront simulation and suitable stop conditions. Another original element is the technique used to check and solve the problem of the apparent ‘fusion’ between the lungs, caused by partial volume effects. A general overview of the algorithm is given, with some details of the innovative parts. The results of its application to a database of about 130 high-resolution low-dose images are discussed.

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Available from: Giorgio De Nunzio, Jan 30, 2014
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    • "They focused on demarcating the overlapped left and right lungs. A completely automated algorithm for lung segmentation operating in three dimensions was presented [13] that use region growing algorithm and wavefront simulation. The technique did not work well when lungs are not contained exactly in the middle of slice. "
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    ABSTRACT: Lung segmentation is regarded as foundation for Computer Aided Diagnosis (CAD) of lung diseases. Computed Tomography scan and CAD facilitate researchers to implement cutting-edge image processing techniques for identifying lung cancer and likelihood of other lung diseases including bronchitis, and emphysema. An automated lung segmentation scheme is proposed in this paper that segments lungs from other body organs contained in CT scan, with special emphasis on disjoint in lungs which have not been attempted before. The scheme uses Otsu algorithm and Connected Component Analysis algorithm for initial segmentation. To refine the initial results and avoid under-segmentation and over-segmentation, morphological operations along with bitwise logical AND, OR operations are applied. One of the challenges for lung segmentation algorithms is the overlap of left and right lungs. The algorithm uses Otsu thresholding for segmenting conjoint of right and left lungs. Solution to this exceptional case is also proposed in which there is disjoint in a particular lung due to overlap of anatomical structures. To segment this type of lungs, the method computes centroid of objects. The proposed methodology is tested on the database of Cornell University, USA, which includes 15 test scans containing 2920 slices. The results indicate a successful segmentation of 2757 slices including left and right lung overlap case and the case in which lung is contained in parts.
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