We propose a computerized framework that, given a region of interest (ROI) circumscribing a lesion, not only predicts radiological observations related to the lesion characteristics with 83.2% average prediction accuracy but also derives explicit association between low-level imaging features and high-level semantic terms by exploiting their statistical correlation. Such direct association between semantic concepts and low-level imaging features can be leveraged to build a powerful annotation system for radiological images that not only allows the computer to infer the semantics from diverse medical images and run automatic reasoning for making diagnostic decision but also provides “human-interpretable explanation” of the system output to facilitate better end user understanding of computer-based diagnostic decisions. The core component of our framework is a radiological observation detection algorithm that maximizes the low-level imaging feature relevancy for each high-level semantic term. On a liver lesion CT dataset, we have implemented our framework by incorporating a large set of state-of-the-art low-level imaging features. Additionally, we included a novel feature that quantifies lesion(s) present within the liver that have a similar appearance as the primary lesion identified by the radiologist. Our framework achieved a high prediction accuracy (83.2%), and the derived association between semantic concepts and imaging features closely correlates with human expectation. The framework has been only tested on liver lesion CT images, but it is capable of being applied to other imaging domains.
Journal of Digital Imaging – Springer Journals
Published: Jun 21, 2017
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