Early diagnosis of lung tumors by genetically optimized 3D-metaball malignancy metric

We present a novel approach to the early diagnosis of lung tumors that considers new malignancy indexes by using a metaball-based representation of this neoplasia. Starting from CT data we extract the suspected tumors represented as approximating metaballs and calculate malignancy indexes based on precise volume and surface irregularity evaluation. The mentioned approximation is performed resolving a constraint problem using a genetic algorithm whose objective function is a mathematical representation of the metaball. Compared to existing art, the metaball approximation provides a consistent reference surface for the evaluation of novel diagnosis parameters borrowed from engineering surface analysis. We have implemented the method in a demonstrator software and analyzed three different test cases.