In this paper an automatic texture based volumetric region growing method for liver segmentation is proposed. 3D seeded region growing is based on texture features with the automatic selection of the seed voxel inside the liver organ and the automatic threshold value computation for the region growing stop condition. Co-occurrence 3D texture features are extracted from CT abdominal volumes and the seeded region growing algorithm is based on statistics in the features space. Each CT volume is composed by 230 slices, having 512 x 512 pixels as spatial resolution, and 12-bit gray level resolution. In this initial feasible study, 5 healthy volunteer acquisitions has been used. Tests have been performed on both basal phase and arterial phase images. Segmentation result shows the effectiveness of the proposed method: liver organ is correctly recognized and segmented, leaving out liver vessels form the segmented area and overcoming the “organ-splitting” problem. The goodness of the proposed method has been confirmed by manual liver segmentation results, having analogous and superimposable behavior.

Gambino, O., Vitabile, S., Lo Re, G., La Tona, G., Librizzi, S., Pirrone, R., et al. (2010). Automatic Volumetric Liver Segmentation Using Texture Based Region Growing. In International Conference on Complex, Intelligent and Software Intensive Systems 2010 (pp.146-152) [10.1109/CISIS.2010.118].

Automatic Volumetric Liver Segmentation Using Texture Based Region Growing

GAMBINO, Orazio;VITABILE, Salvatore;Lo Re, G;LA TONA, Giuseppe;PIRRONE, Roberto;ARDIZZONE, Edoardo;MIDIRI, Massimo
2010-01-01

Abstract

In this paper an automatic texture based volumetric region growing method for liver segmentation is proposed. 3D seeded region growing is based on texture features with the automatic selection of the seed voxel inside the liver organ and the automatic threshold value computation for the region growing stop condition. Co-occurrence 3D texture features are extracted from CT abdominal volumes and the seeded region growing algorithm is based on statistics in the features space. Each CT volume is composed by 230 slices, having 512 x 512 pixels as spatial resolution, and 12-bit gray level resolution. In this initial feasible study, 5 healthy volunteer acquisitions has been used. Tests have been performed on both basal phase and arterial phase images. Segmentation result shows the effectiveness of the proposed method: liver organ is correctly recognized and segmented, leaving out liver vessels form the segmented area and overcoming the “organ-splitting” problem. The goodness of the proposed method has been confirmed by manual liver segmentation results, having analogous and superimposable behavior.
Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni
Settore MED/36 - Diagnostica Per Immagini E Radioterapia
International Conference on Complex, Intelligent and Software Intensive Systems
7
Gambino, O., Vitabile, S., Lo Re, G., La Tona, G., Librizzi, S., Pirrone, R., et al. (2010). Automatic Volumetric Liver Segmentation Using Texture Based Region Growing. In International Conference on Complex, Intelligent and Software Intensive Systems 2010 (pp.146-152) [10.1109/CISIS.2010.118].
Proceedings (atti dei congressi)
Gambino, O; Vitabile, S; Lo Re, G; La Tona, G; Librizzi, S; Pirrone, R; Ardizzone, E; Midiri, M
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/59358
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