Spatially adaptive active contours: a semi-automatic tumor segmentation framework.
OBJECTIVE
Tumor segmentation constitutes a crucial step in simulating cancer growth and response to therapy. Incorporation of imaging data individualizes the simulation and assists clinical correlation with the predicted outcome. We adapted snakes to improve tumor segmentation including difficult cases with inherently inhomogeneous structure and poorly defined margins.


METHODS
Snakes are flexible curves, based on the parameter-controlled deformation of an initial user-defined contour toward the boundary of the desired object, through the minimization of a suitable energy function. Although parameter-adjustment can yield fairly good results in homogeneous regions, traditional snakes often fail to provide an accurate segmentation result when both rigid and very elastic behavior is needed simultaneously to delineate the true outline of the tumor. We developed and tested a spatially adaptive active contour technique by introducing local snake bending, to improve traditional snakes performance for segmenting tumors. The key point of our method is the use of adaptable snake parameters, instead of constant ones, to adjust the bending of the curve according to the local edge characteristics. Our algorithm discriminates image regions according to underlying image features, such as gradient magnitude and corner strength. More specifically, it assigns each region a different "localized" set of parameters, one corresponding to a very flexible snake, and the other corresponding to a very rigid one, according to the local image characteristics.


RESULTS
Qualitative results on more than 150 real MR images, as well as quantitative validation based on agreement with an expert clinician's annotations of the true tumor boundaries, demonstrate our approach is highly efficient compared to traditional active contours and region growing. Due to the use of adaptable parameters in the snake evolution process, our approach outperforms the other two methods, and consistently follows an expert's annotations. Statistical tests indicated significant difference between the results produced by our approach and two other algorithms traditional snakes and region growing, while multiple comparison showed that our method consistently outperformed those algorithms, with an average overlap of 89%, over the entire data set, while traditional snakes were at 82.5% and region growing at 59.2%. Furthermore, we performed several tests that demonstrate our method's stability to different initial contours, as well as, to lower resolution images.


CONCLUSIONS
Our adaptive snake algorithm can spatially adapt to diverse image characteristics, producing outlines that mimic the true tumor boundaries. Results in MR datasets are very close to an expert clinician's intuition about the tumor boundaries.
Farmaki
Cristina
C
Institute of Computer Science, Hellas, Heraklion, Crete, Greece. xfarmakh@ics.forth.gr
Marias
Konstantinos
K
Sakkalis
Vangelis
V
Graf
Norbert
N
D000465:Algorithms; D016009:Chi-Square Distribution; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted; D007680:Kidney Neoplasms / Q000473:pathology*; D008279:Magnetic Resonance Imaging; D018709:Statistics, Nonparametric; D009396:Wilms Tumor / Q000473:pathology*
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