Biologically inspired XXXX segmentation using a geodesic active contour technique.

@entity272 @entity1489 @entity320 @entity45281
BACKGROUND/PURPOSE: Computer-aided diagnosis of @entity1489 requires accurate lesion segmentation, which must overcome noise such as hair, skin color variations, and ambient light variability. METHODS: A biologically inspired geodesic active contour (GAC) technique is used for lesion segmentation. The algorithm presented here employs automatic contour initialization close to the actual lesion boundary, overcoming the 'sticking' at minimum local energy spots caused by noise artifacts such as hair. The border is significantly smoothed to mimic natural lesions. In addition, features that mimic biological parameters include spectral image subtraction and removal of peninsulas and inlets. Multiple boundary choices borders are created by parameter options used at different steps. These choices can allow future improvement over the basic default border. RESULTS: The basic GAC algorithm was tested on 100 images (30 @entity320 and 70 benign lesions), yielding a median @entity45281 border error of 6.7%, comparable to the median inter-dermatologist @entity45281 border error (7.4%), and lower than the gradient vector flow snake median @entity45281 error of 14.2% on the same image set. On a difficult low-contrast border set of 1238 images, which included 350 non-melanocytic lesions, a median @entity45281 error of 23.9% is obtained. CONCLUSION: GAC techniques show promise in attaining the goal of @entity272 segmentation.
