An Innovative Soft Computing Framework to Measure and Classify Solid Pulmonary Tumors from CT Images

Vitoantonio Bevilacqua; Daniele Ranieri; Gaetano Nacci; Gioacchino Brunetti; Piero Larizza; Francescomaria Marino
Assessing the shrinking of lung nodules in response to the treatment is an important task to evaluate the effectiveness of the therapy, so guidelines to minimize variability and make this task objective and observer-independent are strongly needed. The Response Evaluation Criteria in Solid Tumors (RECIST) is a set of standardized rules that defines when cancer patients improve, stay the same, or deteriorate during the treatments. An accurate evaluation of the growth rate can be performed only by means of a high resolute segmentation, followed by volumetric techniques, although the higher the resolution, the more time is consumed in the process. In this work we present a framework that carries out a fully automatic 3D segmentation of the human respiratory system in Computer Tomography images. Usually, an analysis of the lungs requires manual segmentation of nodules that is always subjective and often error-prone. So the framework encompasses a second step, which semi-automatically segments the lung nodules, extracting them from the surrounding tissues and providing an accurate data set for estimating several volumetric features. These features can be used for the classification of the malignancy of the nodule.
3D segmentation; CT Images; Lung Nodules; Region Growing; Level Set; Metaball; GENOCOP; Neural Network
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