Liver segmentation by intensity analysis and anatomical information in multi-slice CT images.
OBJECTIVE
Quantitative assessment and essentially segmentation of liver and its tumours are of clinical importance in various procedures such as diagnosis, treatment planning, and monitoring. Moreover, segmentation of liver is the basis of further processing such as visualization, liver resection planning, and liver shape analysis. In this paper, we propose an algorithm to estimate an initial liver boundary.


METHODS
The proposed method consists of four steps as follows: first, we compute statistical parameters of liver's intensity range, associated with a large cross-section of liver CT image, utilizing expectation maximization (EM) algorithm. Second, by automatic extraction of ribs and segmentation of the heart, we define a ROI to confine the liver region for the next operations. Third, we propose a double thresholding approach to divide the liver intensity range into two overlapping ranges. In this case, based on a decision table, we label an object as a liver candidate or disregard it from the rest of the procedures. Finally, we employ an anatomical based rule to finalize a candidate as a liver tissue. In this case, we propose a color-map transformation scheme to convert the liver gray images into color images. In this way, we attempt to visually differentiate the liver from its surrounding tissues.


RESULTS
We have evaluated the techniques in the presence of 14 randomly selected local datasets as well as all datasets from the MICCAI 2007 Grand Challenge workshop database. For the local datasets, the average overlap error and average volume difference were of values of 15.3 and 2.8%, respectively. In the case of the MICCAI datasets, the above values were estimated as 20.3 and -4.5%, respectively.


CONCLUSIONS
The results reveal that the proposed technique is feasible to perform consistent initial liver borders. The boundary might be then employed in an 'Active Contour' algorithm to finalize the liver mask.
Foruzan
Amir H
AH
Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Aghaeizadeh Zoroofi
Reza
R
Hori
Masatoshi
M
Sato
Yoshinobu
Y
D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000000981:diagnostic imaging*; D019047:Phantoms, Imaging; D014057:Tomography, X-Ray Computed / Q000379:methods*