Title: 3-D liver segmentation from CTA images with patient adaptive Bayesian model

Authors: Maya Eapen; Reeba Korah; G. Geetha

Addresses: Department of Computer Science and Engineering, Jerusalem College of Engineering, Chennai, Tamil Nadu, India ' Department of Electronics and Communication Engineering, Alliance University, Bangalore, Karnataka, India ' Department of Computer Science and Engineering, Jerusalem College of Engineering, Chennai, Tamil Nadu, India

Abstract: Precise identification of liver region from abdominal Computed Tomography-Angiography (CTA) plays an important role in the evaluation of donor for liver transplantation surgery. Nevertheless, the issues like intensity similarity of liver with neighbouring tissues and inter-intra patient liver shape variability; left the task of liver segmentation challenging. Here, we focus on improving the accuracy and reliability of liver donor evaluation system by customising its crucial step - liver segmentation and volume measurement. For achieving this, a Bayesian classifier is iteratively trained with salient features of liver, namely Haralick texture features and spatial information computed from the individual patient dataset. The proposed method is a combination of two techniques namely, advanced region growing and Bayesian classification. The agreement between the proposed method with the manual segmentation was satisfactory with Relative Volume Difference (RVD), Dice Similarity Coefficient (DSC), False-Positive Ratio (FPR), False-Negative Ratio (FNR) with values 8.98, 94.8 ± 1.5, 3.1 ± 2.8 and 5.67 ± 1.8, respectively.

Keywords: medical images; image segmentation; liver segmentation; Bayesian classifier; texture features; liver transplantation; spatial information; computed tomography angiography CTA; liver donor evaluation; volume measurement.

DOI: 10.1504/IJBET.2015.071409

International Journal of Biomedical Engineering and Technology, 2015 Vol.19 No.1, pp.53 - 69

Received: 02 Jan 2015
Accepted: 02 Mar 2015
Published online: 25 Aug 2015 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article