Hot spot detection based on feature space representation of visual search.
This paper presents a new framework for capturing intrinsic visual search behavior of different observers in image understanding by analysing saccadic eye movements in feature space. The method is based on the information theory for identifying salient image features based on which visual search is performed. We demonstrate how to obtain feature space fixation density functions that are normalized to the image content along the scan paths. This allows a reliable identification of salient image features that can be mapped back to spatial space for highlighting regions of interest and attention selection. A two-color conjunction search experiment has been implemented to illustrate the theoretical framework of the proposed method including feature selection, hot spot detection, and back-projection. The practical value of the method is demonstrated with computed tomography image of centrilobular emphysema, and we discuss how the proposed framework can be used as a basis for decision support in medical image understanding.
Hu
Xiao-Peng
XP
Royal Society/Wolfson Foundation Medical Image Computing Laboratory, Imperial College London, SW7 2AZ London, UK.
Dempere-Marco
Laura
L
Yang
Guang-Zhong
GZ
D000328:Adult; D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005133:Eye Movements / Q000502:physiology*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging; D008297:Male; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D010364:Pattern Recognition, Visual / Q000502:physiology*; D011656:Pulmonary Emphysema / Q000175:diagnosis / Q000000981:diagnostic imaging*; D011859:Radiography