A 3D level sets method for segmenting the mouse spleen and follicles in volumetric microCT images.
We present a semi-automatic, 3D approach for segmenting the mouse spleen, and its interior follicles, in volumetric microCT imagery. Based upon previous 2D level sets work, we develop a fully 3D implementation and provide the corresponding finite difference formulas. We incorporate statistical and proximity weighting schemes to improve segmentation performance. We also note an issue with the original algorithm and propose a solution that proves beneficial in our experiments. Experimental results are provided for artificial and real data.
Price
Jeffrey R
JR
Image Science & Machine Vision Group, Oak Ridge National Laboratory, TN, USA. pricejr@ornl.gov
Aykac
Deniz
D
Wall
Jonathan
J
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D021621:Imaging, Three-Dimensional / Q000379:methods* / Q000662:veterinary*; D051379:Mice; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013154:Spleen / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods* / Q000662:veterinary*