An integrated approach for surface finding in medical images

The wide availability of three-dimensional medical images has made their direct analysis a necessity. Accurately segmenting and quantifying structures is a key issue for such images. Conventional gradient-based surface finding however often suffers from a variety of limitations. This paper proposes a surface finding approach that uses in addition to gradient information, region information. This makes the resulting procedure more robust to noise and improper initialization. It uses Gauss´s Divergence theorem to find the surface of of a homogeneous region-classified area in the image and integrates this with a gray level gradient-based surface finder. Experimental results show that indeed, as expected, a significant improvement is achieved as a consequence of the use of this extra information. Further these improvements are achieved with little increase in computational overhead, an advantage derived from the application of Gauss´s Divergence theorem