**4. Discussion**

We discussed in this chapter a new way to observe OCT images and angiographies. Observing the eye circulatory veins colored analogous to SATO2, we give physician and researches new alternatives to look at the inner eye blood system from a numerical perspective. This will let to follow up to people suffering from various diseases not only from the clinic perspective but also from the statistical performance that affects the oxygen saturation of an eye.

*A New Method to Manipulate Conventional OCT Images to Measure Changes in the Relative… DOI: http://dx.doi.org/10.5772/intechopen.110884*

It is the case of a statistical analysis which allows to see how the behavior of the vector of the image is being analyzed, as well as, to separate the values that are believed to be within the curve as shown in **Figure 4** and to give guidelines for future research in this area.

This new method to manipulate conventional OCT images to measure changes in the relative haemoglobin oxygen saturation let the physician to know the SatO2 as a set of numerical data, and then the physician will know the SatO2 at each point of the circulatory system. The fact that the OCT image is transformed into a matrix where each pixel of the circulatory eye system has a specific gray level and then a corresponding SatO2 measurement will let to follow up the performance of oxygen saturation through each branch of the circulatory system. As said above, we have the SatO2 as a set of gray levels perfectly defined into 0 to 255. We showed in our results (**Figures 19** and **20**) a new way to look at the choroid in **Figure 8**, and notice that this new representation gives a bar plot measuring how the oxygen saturation is changing through sets of colors (gray levels). This procedure will let study the patient evolution by comparing the performance of bar plots through time.

The effects of SatO2 in degenerative eye illness like glaucoma, RP, and diabetes among others can be studied statistically following the changes in the SatO2 distribution function, comparing a big set of variables involved in the diseases, finding possible correlation among many variables, for instance, between blood sugar levels and its effects in SatO2 choroids.

#### **Figure 20.**

*A. Choroid levels of light absorbance. B. Shows the distribution of SatO2 from nine different levels of SatO2 concentration presented as color from 0 to 255.*

Our method for measuring oxygen saturation in the eye using OCT imaging represents a significant improvement over current methods in terms of accuracy and traceability. While conventional methods use an average measurement of oxygen saturation in the eye, our method provides a detailed map of oxygen saturation throughout the ocular circulation. This may have significant implications for the diagnosis and monitoring of ocular diseases related to oxygen saturation, such as retinal vascular disease. In addition, our method is based on existing OCT images, making it more accessible and less expensive than invasive and advanced imaging methods currently used in the clinic. Overall, we believe that our work can significantly contribute to the understanding of ocular physiology and improve eye care in the future.

This approach opens great opportunities to make big data OCT sets that can be evaluated even by artificial intelligence and machine learning tools.

### **5. Conclusions and future challenges**

Authors propose a new mathematical expression that allows the calculation of oxygen saturation from conventional OCT images in a computational way. The new results discussed in this chapter shows that pseudocolor method gives better results now than those previously reported in our paper "False Color Method for Retinal Oximetry" [3]. The results obtained need deeper validation by assigning colors to the gray scale depending on the OCT manufacturer. Then for a specific OCT at the same frequency, all gray levels 0 to 255 will be the same trough any study, and then we have the same reference, which means comparison among images from the same OCT is valid. OCT operation frequency plays a significant role in gray levels distribution, as shown in **Figure 19**. This means that for a specific clinic study for illness or patients follow-up, we recommend to use the same equipment and frequency in order to have the same reference through the study. Traditional OCT visual analysis does not let making difference for each of the 0 to 255 gray level in a OCT image. However, it is clear that the SatO2 distribution function let us know clearly how the SatO2 distribution is changing, but we must be careful about different OCT frequencies at time to take the OCT image. Different OCT frequency gives important changes in the OCT gray-level image distribution. This is well known from the spectrographic performance of HBO and HBO2 at different light wavelengths; fortunately, we have now numerically documented those differences with the distribution function of the gray levels of OCT at different frequencies as shown in results (see **Figure 19**).

In future work, we require some preprocessing OCT images to improve the results obtained by the proposed SatO2 procedures. New opportunities are coming from the statistical study of the color levels assigned to the image and thus the evolution of gray levels through illness. The OCT images converted as vectors open new opportunities to study the complex structure of the retina from the SatO2 effects. Then, this new tool provides a numerical approach for a better understanding of degenerative eye illness.

### **Acknowledgements**

The authors thank CONACyT and Instituto Politecnico Nacional for their financial support throughout the work.

*A New Method to Manipulate Conventional OCT Images to Measure Changes in the Relative… DOI: http://dx.doi.org/10.5772/intechopen.110884*
