Lung fissure detection in CT images using global minimal paths

Vikram Appia; Uday Patil; Bipul Das

doi:10.1117/12.844595

12 March 2010 Lung fissure detection in CT images using global minimal paths

Vikram Appia, Uday Patil, Bipul Das

Proceedings Volume 7623, Medical Imaging 2010: Image Processing; 76231P (2010) https://doi.org/10.1117/12.844595
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

Pulmonary fissures separate human lungs into five distinct regions called lobes. Detection of fissure is essential for localization of the lobar distribution of lung diseases, surgical planning and follow-up. Treatment planning also requires calculation of the lobe volume. This volume estimation mandates accurate segmentation of the fissures. Presence of other structures (like vessels) near the fissure, along with its high variational probability in terms of position, shape etc. makes the lobe segmentation a challenging task. Also, false incomplete fissures and occurrence of diseases add to the complications of fissure detection. In this paper, we propose a semi-automated fissure segmentation algorithm using a minimal path approach on CT images. An energy function is defined such that the path integral over the fissure is the global minimum. Based on a few user defined points on a single slice of the CT image, the proposed algorithm minimizes a 2D energy function on the sagital slice computed using (a) intensity (b) distance of the vasculature, (c) curvature in 2D, (d) continuity in 3D. The fissure is the infimum energy path between a representative point on the fissure and nearest lung boundary point in this energy domain. The algorithm has been tested on 10 CT volume datasets acquired from GE scanners at multiple clinical sites. The datasets span through different pathological conditions and varying imaging artifacts.

Citation Download Citation

Vikram Appia, Uday Patil, and Bipul Das "Lung fissure detection in CT images using global minimal paths", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76231P (12 March 2010); https://doi.org/10.1117/12.844595

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