Automatic cumulative sums contour detection of FBP-reconstructed multi-object nuclear medicine images

The problem of determining the contours of objects in nuclear medicine images has been studied extensively in the past, however most of the analysis has focused on a single object as opposed to multiple objects. The aim of this work is to develop an automated method for determining the contour of multiple objects in positron emission tomography (PET) and single photon emission computed tomography (SPECT) filtered backprojection (FBP) reconstructed images. These contours can be used for computing body edges for attenuation correction in PET and SPECT, as well as for eliminating streak artifacts outside the objects, which could be useful in compressive sensing reconstruction. Contour detection has been accomplished by applying a modified cumulative sums (CUSUM) scheme in the sinogram. Our approach automatically detects all objects in the image, without requiring a priori knowledge of the number of distinct objects in the reconstructed image. This method has been tested in simulated phantoms, such as an image-quality (IQ) phantom and two digital multi-object phantoms, as well as a real NEMA phantom and a clinical thoracic study. For this purpose, a GE Discovery PET scanner was employed. The detected contours achieved root mean square accuracy of 1.14 pixels, 1.69 pixels and 3.28 pixels and a Hausdorff distance of 3.13, 3.12 and 4.50 pixels, for the simulated image-quality phantom PET study, the real NEMA phantom and the clinical thoracic study, respectively. These results correspond to a significant improvement over recent results obtained in similar studies. Furthermore, we obtained an optimal sub-pattern assignment (OSPA) localization error of 0.94 and 1.48, for the two-objects and three-objects simulated phantoms, respectively. Our method performs efficiently for sets of convex objects and hence it provides a robust tool for automatic contour determination with precise results.