A New Geometric Deformable Model for Medical Image Segmentation

Segmentation of medical images is challenging due to poor image contrast and artifacts that result in missing or diffuse organ or tissue boundaries. In thispaper, we proposed a modification to the original level setalgorithm for implementation of deformable models. Themodification is derived from intensity averaging of the image. This new method helps to segment medical imagesaccurately into multi-level images. The level set algorithmhas some advantages over the classical snake deformablemodels but it has diffculties with large gaps in the boundaries of segmented regions. Such boundary gaps may causeinaccurate segmentation that requires manual correctionby users, while our goal was to keep user assistance toa minimum. The proposed method possesses an inherentproperty to detect gaps within the object with a single initial contour and also does not require specific initialization.