1. Field of the Invention
The present disclosure relates to image processing, and more particularly to a system and method for estimating a size of solitary objects in of interest in medical images.
2. Description of Related Art
Measuring the size of pulmonary nodules from X-ray computed tomography (CT) data is an important practice for diagnosis and progression analysis of lung cancer. The nodule size often plays an important role in choosing a proper patient care, and is also an effective feature to separate true nodules form nodule-like spurious findings. Typically, the size is represented by the diameter of the nodule. Automating this task for computer-aided diagnosis (CAD) is, however, a difficult problem due to intensity variations, partial volume effects, attachment to other structures, and noises.
A Ct-based screening protocol specified by the International Early Lung Cancer Action Program (I-ELCAP) details how the diameter of pulmonary nodules should be measured and how the measurements should be used for determining the patient management. According to the protocol, the result of an initial CT screening of lung is considered positive if at least one solid or part-solid nodule with 5.00 mm or more in diameter or at least one non-solid nodule with 8.0 mm or more in diameter is found. Although these 5 mm and 8 mm thresholds are likely to drop as more accurate screening becomes possible with high resolution multi-detector helical CT (MDCT), the importance of module size in cancer diagnosis will stay unchanged.
To this end, an automated size estimation algorithm using a Gaussian Ellipsoid Fit (EF) has been developed. Given a marker positioned near a nodule, the algorithm computes the location, orientation and radii of an ellipsoid that models closely the intensity variation nearby the marker. It employs mean-shift and scale estimator to find the solution. The volume and diameter of the nodule can be estimated from the ellipsoid. EF can be incorporated into a CAD system where markers and provided from either manual markings of a reader or the detection algorithm of the system. The accuracy of the estimates has been verified using a large database with manual size measurements. However, the technique tends to have difficulties with small nodules due mainly to the small sample size problem.