# Correlation Mapping of Perfusion Patterns in Cutaneous Tissue
## 3 Results  
Four subjects (two females and two males) participated in the measurement, while one subject suffered from DM type II and one from DM type I. None of the participants suffered from limb pain or indicated diabetic foot syndrome. The image and signals shown in Figure 5 are after the KLT tracking. Despite the subjects' efforts not to move the limb, the differences between the first and last images were present in the records. Mostly, there were slight rotations of the foot. This problem was eliminated using the proposed algorithm. Figure 5 shows the signals approximately three and a half minutes along with a zoomed-in period of 20 s. Additionally, the dashed black line highlights the deep breathing test. Furthermore, we can see along with the whole record the low-frequency perfusion oscillations in the frequency range 0.05 Hz-0.4 Hz [15, 35, 36, 37]. For instance, the low-frequency perfusion oscillations in the red signal are highlighted in Figure 5 by the black line. Within this slow perfusion oscillation, there are superimposed changes related to cardiac activity. Moreover, we can observe an increase in the amplitude of the PPG signal in some stages of the record. The change in amplitude of the red-coloured signal strongly  
Figure 4: Left—Different averaging masks and their impact on HR accuracy, right—foot with marked locations for signal extraction. resembles the change in amplitude of the reference PPG signal obtained from the little toe, noting that the PPG signal from BIOPAC is inverted relative to the natural PPG signal. Three correlation maps (Figure 6) showing perfusion changes related to cardiac activity were created from the acquired recordings. The first correlation map corresponds with the stage before DBT is performed. The second depicts perfusion changes during the DBT, and the third correlation map shows the perfusion three minutes after the DBT is completed. All correlation maps are created from a 20-s-long segment of the record. The time courses shown in Figure 6 are associated with the third correlation map, thus with the stage after DBT for each subject. The red signal is from the toe region, the green signal is from the central region of the foot, the blue signal is from the heel region, and the last black signal is the reference signal acquired by the BIOPAC unit. Similar to the signals from Figure 5, we can see the amplitude differences of the signals obtained from different positions of the same subject in these recordings. The differences in light intensity modulation are particularly pronounced across the recordings. While in record one, the signals show a significant modulation of light intensity along the whole foot, the correlation coefficient is significantly lower in record two. In record two, the signal is more affected by noise, but the amplitude of the signal intensity change is not significantly different from the other records. When evaluating the maps, we can notice that the correlation coefficient during DBT decreased for all subjects; for record number two, up to such an extent that the foot contour is hardly visible on the map. After DBT, the correlation coefficient in all cases increased slightly compared to the pre-DBT condition. The correlation maps of healthy subjects are also shown in Figure 6 (3,4). Record four is more significantly affected by noise compared to record three. Similar to records one and two, there is a decrease in the correlation coefficient during DBT. However, unlike records one and two, there was a noticeable decrease in the correlation coefficient after the DBT compared to the pre-test condition. To gain another perspective on the changes in cutaneous tissue perfusion during the different phases of the experiment, we created bar graphs showing the normalized correlation coefficients from the regions marked in Figure 7; see the map on the left.