Rui Li1 , Pelz J1 , Haake AR1
1Golisano College of Computing and Information Science, Rochester Institute of Technology, Rochester, NY.
Corresponding Author: Rui Li, Golisano College of Computing and Information Science, Rochester Institute of Technology, Rochester, NY, Tel: +1 585-475-7203; E-Mail: firstname.lastname@example.org Received Date: 18 Apr 2016
Accepted Date: 17 May 2016
Published Date: 21 Jul 2016
Copyright © 2016 Li R
Citation: Li R, Pelz J and Haake AR. (2016). Using Experts’ Perceptual Skill for Dermatological Image Segmentation. M J Derm. 1(1): 007.
There is a growing reliance on imaging equipment in medical domain, hence medical experts’ specialized visual perceptual capability becomes the key of their superior performance. In this paper, we propose a principled generative model to detect and segment out dermatological lesions by exploiting the experts’ perceptual expertise represented by their patterned eye movement behaviors during examining and diagnosing dermatological images. The image superpixels’ diagnostic significance levels are inferred based on the correlations between their appearances and the spatial structures of the experts’ signature eye movement patterns. In this process, the global relationships between the superpixels are also manifested by the spans of the signature eye movement patterns. Our model takes into account these dependencies between experts’ perceptual skill and image properties to generate a holistic understanding of cluttered dermatological images. A Gibbs sampler is derived to use the generative model’s structure to estimate the diagnostic significance and lesion spatial distributions from superpixel-based representation of dermatological images and experts’ signature eye movement patterns. We demonstrate the effectiveness of our approach on a set of dermatological images on which dermatologists’ eye movements are recorded. It suggests that the integration of experts’ perceptual skill and dermatological images is able to greatly improve medical image understanding and retrieval.
Perceptual Expertise; Dermatological Image Understanding; Probabilistic Modeling; Gibbs Sampling; Eye Tracking Experiments; Superpixels.