3-D liver segmentation from CTA images with patient adaptive Bayesian model
by Maya Eapen; Reeba Korah; G. Geetha
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 19, No. 1, 2015

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.

Online publication date: Tue, 25-Aug-2015

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biomedical Engineering and Technology (IJBET):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com