Here, we proposed three different alternative distance metrics for comparing a brain image pair with increasing complexity, namely naïve pixel-wise distance metric, adjusted pixel-wise distance metric, and a method based on intensity summaries by histogram, row, and column (so-called HRC method).
The threshold is calculated based on the intensity histogram distribution within the region of interest.
Once again, the threshold based on the intensity histogram technique was used for an initial joint effusion segmentation.
The segmentations were performed in each T2 image by thresholding based on the intensity histogram analysis (Support material 1 in Additional file 1).
This automated WMH segmentation method involves 1) automatic identification of WMH seeds based on the intensity histogram of the FLAIR image, 2) using a fuzzy-connected algorithm to segment the WMH clusters, 3) iteratively updating the set of seeds, and 4) combining the WMH clusters into a final WMH segmentation.
During development of the system, various descriptors of texture based on the intensity histogram of a region were calculated for potential use in analysis of PCO images of patients.[ 10] Entropy was found to provide the most useful form of texture measure.
We firstly applied the multi-threshold Ng's method on modulation histogram and then proposed our method for shadow and background detection based on modulation and intensity histogram.
Figure 8e shows the detected object by our proposed method, the background is segmented based on the intensity difference histogram of the coding map shown in Fig. 8b, and threshold t in is utilized.
First-order statistics, based on histogram analysis, include mean intensity, maximum intensity, minimum intensity, uniformity (uniformity of gray-level distribution), entropy (irregularity of gray-level distribution), standard deviation of the gray-level histogram distribution, skewness (asymmetry of the histogram), and kurtosis (flatness of the histogram) (Appendix).
