About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Conference paper
Exploring features towards semantic characterization of lung nodules in computed tomography images
Abstract
One of the main challenges in the integration of medical data reports is translating numerical features from different sources into a common abstract vocabulary that support a seamless combination of such data. When it comes to image analysis, a very common pipeline to describe the image involves extracting numerical features from image data and translate them into meaningful pre-defined semantic concepts. In this context, we propose a methodology for selecting numerical features and relating them to semantic features using the publicly available categorization in the lung nodules LIDC NIH database. We present several numerical features joined several classifiers, and a comparison between two feature selection methods and discuss how different features contribute to the discrimination of different semantic characteristics of lung nodules. Our results show the potential of such methodology for translating features into abstract semantic concepts for lung nodules characterization.