Helical computed tomography (CT) is the premier modality for chest imaging and is increasingly used to detect lung nodules and quantify their size. Several automated methods have been developed to maximize the precision of volume measurement of a nodule once it is identified. Binary variable threshold (VT) and the partial volume (PV) are two commonly used techniques for estimating nodule volume. We present and validate on a phantom an algorithm that combines the benefits of VT and PV techniques. The process begins by defining an initial binary approximation, Wi, The hybrid algorithm then constructs a hollow shell dW around the edges. The shell dW is designed to maximize the likelihood of containing the lung-nodule interface, while minimizing the containment of both the core of nodule and the surrounding lung. Finally, the PV method is applied to dW and the resulting volume is added to the interior volume to obtain the volume of the entire nodule. We have implemented the hybrid method and validated it on a realistic chest phantom that contained 40 simulated nodules. The absolute error of VT and PV was 2.3 mm3, with a standard deviation of 2.0 mm3. The hybrid method performed better, with an absolute error of 2.2 mm3 and a standard deviation of 1.9 mm3. The algorithm appears to be a promising tool in evaluating the growth of lung nodule.