The diagnostic potential of various VFI image features was explored (Table 1). In total, 49 features were computed for each ROI in the VFI images (MATLAB R2011b, Mathworks, Natick, Massachusetts).38–44 Versions of these features have been utilized in the past to quantify image features of non-neoplastic and neoplastic tissue from various anatomical sites; in this study, they are used to quantify proflavine labeling in BE and associated neoplasia. To explore the relative intensity of proflavine uptake, first-order statistical features (variance, standard deviation, etc.) were computed based on individual pixel values.37,45 To explore textural image features which help identify glandular epithelium, a gray-level co-occurrence matrix (GLCM) with pixel offsets (1 to 6) was first computed for each ROI. Then features such as correlation, contrast, energy, and homogeneity were computed from each GLCM. This method has been described in detail;46 and variations have been used previously.37,39,42 To explore spatial frequency features which also help identify glandular features, a two-dimensional Fourier transform was used to calculate the power spectrum of each ROI. The resulting power spectrum was divided into 10 individual frequency ranges, where the frequency content in each of the 10 components corresponded to a particular fixed spatial frequency range. This method has been described in detail;46 and variations have been used previously.37,42,47 To characterize epithelial thickness which changes with the progression of neoplasia, granulometry metrics, which assess the size distribution of disk-shaped elements in an image, were computed for each ROI.41,44 The resulting plot of the total disk surface area as a function of disk size characterizes the relative distribution of various sized disks within each ROI; the disk size range was chosen to be 1 to 100 pixels to accommodate the various sized image features that are seen in the ROIs. Statistical features (skewness, kurtosis, etc.) of this distribution were computed.