Gaze-guided CT image retargeting by multi-attribute binary hashing

Computed tomography (CT) imaging is pervasively utilized for detecting tumors and internal body injuries. CT image retargeting means to horizontally/vertically shrink the semantically non-salient regions (e.g, the normal organs) while preserving the salient ones (e.g., the diseased organs) inside a CT image, as exemplified in Fig. 1. In practice, retargeting can substantially facilitate CT image displaying, which can benefit the subsequent medical treatment. In this work, we propose a bio-inspired CT image retargeting pipeline by mimicking human gaze behavior. More specifically, for each CT image, we extract the gaze shifting path (GSP) to capture human gaze distribution during the visual perception toward each CT image. Afterward, a multi-attribute binary hashing (MABH) is formulated to exploit the semantics of these GSPs. Thereby, each graphlet can be converted into the binary hash codes. Finally, the hash codes corresponding to GSP from each CT image are quantized into a feature vector, which is leveraged to learn a Gaussian mixture model (GMM) that guides CT image shrinking. In the experiments, to evaluate how gaze allocation influencing CT image retargeting, a user study is designed to compare the GSPs produced by normal observers and Alzheimer's patients respectively. Besides, a comparative study has verified the superiority of our method.

Automated summary

In this study, a bio-inspired CT image retargeting pipeline is proposed, which mimics human gaze behavior to achieve CT image retargeting. By extracting gaze shifting paths and using multi-attribute binary hashing to capture the semantics of CT images, a Gaussian mixture model is learned to guide the image shrinking process.