Single organ segmentation filters for multiple organ segmentation.
In this paper, we propose an approach for automatic organ segmentation in computed tomography (CT) data. The approach consists of applying multiple single organ segmentation filters and resolving conflicts among the single organ segmentations to generate a multiple organ segmentation. Each of the single organ segmentations consists of three stages: first, a probability image of the organ of interest is obtained by applying a binary classification model obtained using pixel-based texture features; second, an adaptive split-and-merge segmentation algorithm is applied on the organ probability image to remove the noise introduced by the misclassified pixels; and third, the segmented organ's boundaries from the previous stage are iteratively refined using a region growing algorithm. The conflict resolution among the single organ segmentations involves comparing region sizes and average probabilities over contested pixels.
Furst
Jacob D
JD
DePaul Univ., Chicago, IL 60604, USA. jfurst@cti.depaul.edu
Susomboom
Ruchaneewan
R
Raicu
Daniela S
DS
D001698:Biomedical Engineering; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data; D009928:Organ Specificity; D010363:Pattern Recognition, Automated / Q000706:statistics & numerical data; D036542:Tomography, Spiral Computed / Q000706:statistics & numerical data; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data*