A New Method to Manipulate Conventional OCT Images to Measure Changes in the Relative Haemoglobin Oxygen Saturation

Abstract

Conventional OCT gray scale images hidden information that do not let the physician to measure the retina oxygen blood saturation. We discuss in this chapter a new approach to extract valuable information from conventional OCT images. The main idea is to convert OCT images to colored images that let the physician to identify more easily the complex structures at the retina circulatory network. A key point in this chapter is not only to identify OCT differences through illness, but also to find a metrics to predict the percent (%) of oxygen saturation in the eye fundus. We will focus on the difficulties to measure oxygen saturation within the ocular vasculature from light reflection. Discussion concerns about a new metric to measure the oxygen saturation within the blood vessels from OCT images. We propose to transmit the lecturer the need to take advantage of the properties within HbO2 and Hb when absorbing light and how that absorption reflected in gray color intensity can be converted as an algorithm to measure the oxygen saturation numerically.

1. Introduction

Optical coherence tomography (OCT) [1, 2, 3, 4] is becoming as a new research field to find new data to study the blood oxygenation from said vessels within inner and outer retina. Noninvasive spectrophotometric retinal oximetry has been a target for over five decades ago. Our work and results reported in this chapter are inspired from a previous experiences reported in Ref. [3]. We invite lectures to study previous reports about OCT image processing [3, 4, 5, 6, 7, 8] to follow the evolution of noninvasive spectrophotometric retinal oximetry efforts. However, image processing from optical coherence tomography (OCT) requires new alternatives to analyze the absorption difference between oxyhemoglobin (HbO₂) and deoxyhemoglobin (Hb) in the retinal vessels.

OCT images show an internal view of the eye. The inside view of the ocular eye is recorded through the pupil, and this let to get an image that shows the vascular system inside the ocular glove [1]. The study is performed by projecting a beam of light into the pupil to facilitate the visualization of the fundus. Fundus images let to know if patients have different eye condition, like retinal detachment, retinal thrombosis, macular degeneration, diabetic retinopathy and glaucoma. However, diagnostic steel depends on the physician experience and not in a set of new parameters that can be extracted from the OCT image that converted in new data that let to know from image gray levels to measure the oxygen saturation in the retina vein system [3, 5, 7]. The oxygen saturation that is intrinsically in the gray levels of the OCT image depends directly on the frequency that the image is obtained, which comprises a range between 570 and 600 nm [9, 10, 11, 12, 13]. This relationship is clearly shown in Figures 1 and 2. Figure 2 shows the absorption spectrum of Hb and HbO₂ [9 at 532 nm].

Figure 1.

Absorption spectrum of haemoglobin (Hb) and oxyhemoglobin (HbO₂) [8].

Figure 2.

Plot of the absorption spectrum of Hb and HbO₂ [8], with the absorption spectrum indicator at 532 nm.

We have now new ideas about vascular retina oximetry (VRO). VRO can be understood as the study of oxygen saturation measurement from OCT conventional images if they are processed properly. We defined the VRO approach as a new method to measure the oxyhemoglobin (HbO₂) and deoxyhemoglobin (Hb) from light reflection at different wavelengths. Light reflection from HbO₂ and Hb is the gray light levels seen from the OCT images. These gray levels are not properly recognized by humans; however, OCT image processing techniques, as discussed in this chapter, are excellent tools converting conventional OCT images in new ones according to light reflectance laws. The calculus of percentage of HbO₂ from OCT images is a big challenge. We present in this chapter a new method to evaluate oxygen saturation at the retina vein system.

The Eyeball structure visualized from a fundus image as shown in Figure 1. Figure 1 shows the main central eye arterial just entering at the optic nerve disk. The main artery divides into the central retina artery and ciliary arteries to supply the inner and outer retina at the back of the eye. However, this image, as shown, is far away to give us information about the oxygen consumption in the vein circulatory system. The human retina is a highly oxygen consuming structure. It happens that the human retina consumes oxygen faster than the brain; consequently, the retina metabolic processes are highly dependent on oxygen saturation SATO₂. The choroid has the responsibility to supply oxygen to the cone and rods at the retina complex system.

The transparent structure of the eye fundus offers an extraordinary chance to extract information from the visible vein system about the oxygen going in there. The image spectrometry lets to analyze the oxygen content inside the arterial system if the image is filtered adequately to extract the quantities of oxygen going inside the haemoglobin. This is not an easy task; it is a steel and noncompletely solved problem. The key point is how to measure the absorption of light of haemoglobin and deoxyhemoglobin and from OCT images which vein colors depend on the quantity of oxygen they have in there. The spectral analysis can show both structures as filters that reflect different amounts of light in specific wavelengths depending on its chemical composition; this means more or less oxygen they transport. Table 1 shows the different wavelengths of the laser bands required to distinguished with higher accuracy between haemoglobin (613 nm) and deoxyhemoglobin (486 nm) [14].

Laser Band	Wavelength (ʎ)
Red	$635\mathit{nm}$
Green	$532\mathit{nm}$
Blue	$486\mathit{nm}$
Infrared	$802\mathit{nm}$

Table 1.

Table with the different wavelengths of the laser bands [14].

We show in Figure 3 the relationship between light absorption in Hb and HbO₂ and its relation with the frequency of the light beam. As said above, light absorption and consequently oxygen saturation is a function that depends on the wavelength of the beam light. The optimum frequency to find differences between Hb and HbO₂ are 570 and 600 nm. Jóna Valger›ur Kristjánsdóttir describes important results of light absorption capacity in [9]. Our group and other researchers [3, 5, 6, 7, 9, 15] are interested in applying the light absorption blood properties to find differences between Hb and HbO₂ [7]. The main idea goes converting those properties in new colored OCT images where colors let the physician to see the oxygen saturation performance in the retinal vascular system [3].

Figure 3.

A. Original fundus image with retinitis pigmentosa obtained at $532\mathit{nm}$. B. Image resulting from applying the pseudocolor method to a fundus image with retinitis pigmentosa obtained a $532\mathit{nm}$.

2. Methodology

The oxygen saturation SATO₂ inside the eye circulatory system is highly correlated with the light absorption [16] in each point of the OCT images. Then, the oxygen at the HBO₂ at maximum saturation levels at the corresponding wavelengths shown in Figure 3 will show the maximum absorbance of light. This absorbance is shown in an OCT image as the light intensity output filtered by the veins. The OCT emitter light or input light to the veins has a specific frequency according to spectral answer as shown in Figure 3. Obviously, the Hb has the lower SATO₂ since Hb is the blood returning after delivering the oxygen to the retina. Hb will absorb light according to the blue curve as shown in Figure 3. It is well known that arterial blood is red and venues blood is blue. This color differences and oxygen saturation can be seen too in a black and white OCT image. Now consider the image in Figure 1 as a black and white image. Notice that the black and white image shown in Figure 4 is now the negative version of the black and white image in Figure 1. Then, the light reflected from the Hb system will show the less reflected light in comparison with the light reflected by the HBO₂. This means that in the black and white version of Figure 1, the highest white illumination will correspond to the highest SATO₂, while the darkness points in Figure 4 will show the points with the lower oxygen saturation SATO₂. This difference is clearly observed in the blue and green wavelengths graphs in Figure 3. Then, HbO₂ has the highest absorption points and are at 418, 542, 577, and 925 nm. However, the Hb has highest absorption points located at: 430, 550, 758, and 910 nm.

Figure 4.

Plot of grayscale values of Figure 3A.

We have a big challenge now. This means to implement computational procedures that let estimating the SATO₂ from OCT images. The estimation requires calculating true values of oxygen saturation in the eye circulatory system.

2.1 Oxygen calculus of the blood

The SATO₂ in HbO₂ can be calculated as the relationship between HbO₂ and the HbO₂ + Hb [17]. This easy relation will give us the percentage of HbO₂ compared with the whole oxygen in both HbO₂ + Hb as shown in Eq. (1):

$${\mathit{\text{SatO}}}_2=\frac{{\mathit{HbO}}_2}{\mathit{Hb}+{\mathit{HbO}}_2}\times 100\%$$E1

where HbO₂ is the oxygen combined with haemoglobin and Hb is the deoxygenated haemoglobin. The SatO₂ is multiplied by 100 to express the SatO₂ in percentage. The SatO₂ of blood goes to 97,5–75% at 100 mmHg for the mixed venous blood.

OCT black and white images as shown in Figure 4 are normalized to get a set of levels going from 0 to 255. The 0 represents the highest level of darkness or lowest level of illumination. The 255 levels represent the maximum white intensity. The HbO₂ with the highest SatO₂ will be represented with the 255 gray level. Then, we have a new easy way to evaluate the SatO₂ in the circulatory ocular globe system (COGS). This new approach considers as a vector only the pixels in the veins shown in the black and white OCT. The veins are filtered by a segmentation algorithm that converts the circulatory eye system as a numerical matrix. This matrix depending on their values between o and 255 let identify the differences in SatO₂ in veins and consequently will implement new alternatives to show the physician the performance of the circulatory eye system in normal and illness conditions:

$${\mathit{\text{SatO}}}_2=\frac{{\sum }_{n=96}^{N-1}\frac{P_{n_{\mathit{\text{total}}}}}{255}}{{\sum }_{n=1}^{N-1}\frac{P_k}{95}+{\sum }_{n=96}^{N-1}\frac{P_{n_{\mathit{\text{total}}}}}{255}}\times 100\%$$E2

where $P_{n_{\mathit{\text{total}}}}$is

$$P_{n_{\mathit{\text{total}}}}=\sum _{n=96}^{N-1}\frac{P_{n_1}}{199}+\sum _{n=200}^{N-1}\frac{P_{n_2}}{255}$$E3

Eq. 2 let calculate SATO₂ from an OCT gray levels image analogously as Eq. (1) does. The OCT image converted as a vector is a matrix, where each number in there represents the proportion of light absorbance by HBO₂; consequently, it is a measure of the oxygen in an specific pixel depending on its value, the 255 value, and the highest gray level has the highest oxygen absorption in that pixel and so on for lower values in the 1 to 255 gray levels scale.

$P_k$ in Eq. (2) is the value of the vector in pixel k. In Eq. 2, we consider that the vector $P_k$ belonging to venous blood pixels going from 1 to 95.

P_{n total} in eq. (3) with two variables $P_{n_1}$ and $P_{n_2}$ are the pixel values of each vector to be analyzed. In this case, $P_{n_1}$ takes the values that are delimited by the n and the dividend, and these values will vary between 96 and 199, which are the values proposed in this work for the representation of venous haemoglobin. On the other hand, $P_{n_2}$ will take the values from 200 to 255 which are the values proposed to identify the arterial haemoglobin or oxyhemoglobin. Thus, when the substitution of the variables is made, the result is $P_{n_{\mathit{\text{total}}}}$ which is the whole amount of gray levels in the vector. This includes all the pixels representing the arterial and venous system. The arterial and venous systems are normalized to their maximum values. Hb has values from 1 to 95, HbO₂ venous goes from 96 to 199, and HbO₂ arterial goes from 200 to 255.

We finally arrive to a final SatO₂ equation as shown below:

$${\mathit{\text{SatO}}}_2=\frac{{\sum }_{n=96}^{N-1}\frac{P_{n_1}}{199}+{\sum }_{n=200}^{N-1}\frac{P_{n_2}}{255}}{{\sum }_{n=1}^{N-1}\frac{P_k}{95}+\left({\sum }_{n=96}^{N-1}\frac{P_{n_1}}{199}+{\sum }_{n=200}^{N-1}\frac{P_{n_2}}{255}\right)}\times 100\%$$E4

As a matter of fact of Eq. 4 with the following values, $P_{n_1}=138$, $P_{n_2}=225$, and $P_k=80$shows SAtO₂ calculation for pixel in k = 80:

$${\mathit{\text{SatO}}}_2=\frac{\raisebox{1ex}{$138$}\!\left/ \!\raisebox{-1ex}{$199$}\right.+\raisebox{1ex}{$225$}\!\left/ \!\raisebox{-1ex}{$255$}\right.}{\raisebox{1ex}{$80$}\!\left/ \!\raisebox{-1ex}{$95$}\right.+\left(\raisebox{1ex}{$138$}\!\left/ \!\raisebox{-1ex}{$199$}\right.+\raisebox{1ex}{$225$}\!\left/ \!\raisebox{-1ex}{$255$}\right.\right)}\times 100\%$$E5

$${\mathit{\text{SatO}}}_2=\frac{1.5758}{2.4179}\times 100\%$$E6

$${\mathit{\text{SatO}}}_2=0.6517\times 100\%=65.17\%$$E7

Then in (7), 65.17% is the SATO₂ for pixel in k = 80.

The assignment of colors to the different gray level values was as follows: 0–black, 1–32 equals purple, 33–64 violet, 65–95 blue, 96–127 cyan, 128–159 green, 160–191 yellow, 192–223 orange, and 224–255 red. Then Eq. (3) can evaluate SatO₂ by regions or specific borders inside the OCT image.

We can see from the above discussion that we have a new numerical method to calculate SatO₂. This calculus let describes all the pixels at the circulatory eye system as numbers that represents each one the SatO₂ by pixel. The SatO₂ can be expressed too as a color vein map for an easy recognition of the physician about the state of the circulatory system and its relation with oxygen performance.

3. Results

Figure 3 shows the result of applying the pseudocolor method to an image that was obtained at $532\mathit{nm}$ with the OPTOS California retinograph (this retinograph works with different wavelengths as shown in Table 1), according to gray levels shown in Figure 4.

Notice that in Figure 2 at 532 nm, there is no difference between the absorption spectrum of Hb and HbO₂. However, our results show that differences in gray levels (128–255) let find SatO₂ differences, shown as red and green SatO₂ levels in Figure 3. This is due to the good pseudocolor sensitivity segmentation method discussed above. The same can be said with the red and blue (HbO₂ and Hb) intersections points in Figure 2.

We find in Figure 4 the OCT image vector shown as the blue curve. The blue vector has seven changes we recognize as slopes, and each slope was associated with the SatO₂ variation in a specific region of set of gray levels [18] according to the absorbance properties of Hb and HbO₂, as shown in Figure 2.

We show in Figures 5–8 the SatO₂ absorbance results from images obtained with wavelengths: $486\mathit{nm}$, $635\mathit{nm}$, $802\mathit{nm}$, and the combination of the wavelengths $532\mathit{nm}$ and $635\mathit{nm}$.

Figure 5.

A. Original healthy fundus image obtained at $635\mathit{nm}$. B. Image resulting from applying the pseudocolor method to a healthy fundus image obtained at $635\mathit{nm}$.

Figure 6.

A. Original ocular fundus image with ocular ischemia obtained at $486\mathit{nm}$. B. Image resulting from applying the pseudocolor method to a fundus image with ocular ischemia obtained a $486\mathit{nm}$.

Figure 7.

A. Original fundus image with retinitis pigmentosa with the combination of wavelengths. $532\mathit{nm}$ and $635\mathit{nm}$. B. Image resulting from applying the pseudocolor method to a fundus image with the combination of wavelengths. $532\mathit{nm}$ and $635\mathit{nm}$.

Figure 8.

A. Original fundus image of the choroid obtained with a wavelength of $802\mathit{nm}$. B. Image resulting from applying the pseudocolor method to a fundus image of the choroid obtained with a wavelength at $802\mathit{nm}$.

We show in Figures 5–8 the results obtained with the pseudocolor method. Color levels show differences in SatO₂ in the ocular microvasculature system. This method as shown can be applied to both fundus images and angiographies. It is important to say that Figure 8 shows the SatO₂ of the choroid, making a novel method to analyze the vasculature of the choroid from the vector obtained by the pseudocolor method.

The pseudocolor method allows to differentiate clearly the microvasculature of the fundus and then to analyze the vasculature performance from new tools.

We show in Figure 9A, with author permission, the SatO₂ reported by Kristjánsdóttir et al. [4]. As a matter of comparison, we show the same image obtained at 570 nm (Figure 9B) and 600 nm (Figure 9C) applying our method with results shown in Figures 10 and 11.

Figure 9.

Oximetry of the eye obtained at 570 nm (Figure 9B), and 600 nm (Figure 9C).

Figure 10.

False color method applied to Figure 9C.

Figure 11.

False color method applied to Figure 9B.

The resulting images in Figures 10 and 11 obtained with the method presented in this chapter show notable differences in the color assignment among Kristjánsdóttir et al. and ours; as mentioned in the methodology, the oxygen saturation formula proposed in here was applied in this assignment, and this explains the difference between results reported in [4] and ours; however, color standardization requires deeper work. It is clear that wavelength has an important role to find best results when looking HbO₂ or Hb. Figure 11 shows results with 570 nm image, and Figure 10 shows results with 600 nm.

Figure 12A–C shows results published by Geirsdottir et al. [3], which is similar to the one obtained by Kristjánsdóttir et al. [4].

Figure 12.

Eye oximetry obtained at 570 nm (Figure 12A), and 600 nm (Figure 12B).

The algorithm developed in this work was applied in Figure 12A to see how our method compares with Geirsdottir et al. and our results are shown in Figure 13.

Figure 13.

Pseudocolor method applied to Figure 12A.

We show in Figure 14 the pseudocolor method applied in the original Figure 12B.

Figure 14.

False color method applied to Figure 12B.

The first results obtained by Davila-Iniesta et al. [3] will be compared with the pseudocolor method as shown in Figures 15–18. We find an improvement in the segmentation procedure. We find that pseudocolor method provides a better resolution with vein network and gives a whole scheme about the distribution of the SatO₂ at the vein system.

Figure 15.

A. Fundus image of healthy eye. B. False color method applied to the fundus image [3].

Figure 16.

A. Fundus image of healthy eye. B. Pseudocolor method applied to Figure 15A.

Figure 17.

A. Fundus image with macular edema. B. False color method applied to Figure 17A.

Figure 18.

A. Fundus image with macular edema. B. Pseudocolor method applied to Figure 17A.

Figure 16B shows a healthy eye, and using the pseudocolor proposed method we noticed an improvement in the vein network sight in relation to the original fundus image [3, 4]. We show too that it is possible to see the SatO₂ levels converted as a color set. This will help the physician to a better understanding of the performance of the vasculature system.

Figure 17A shows the fundus image with macular edema. After applying the false color method, the resulting image shown in Figure 17B was obtained. We find that the relationship between macular edema and SatO₂ plays an important role. However, this statement requires statistical analysis from enough of this illness OCT images, and statically analysis can be applied since we have converted every images in a numerical vector.

Figure 18B shows the fundus image with macular edema after applying the pseudocolor method. The SatO₂ ratio in this image is seen to be clearer than the previous image. Also, the microvascular system and how it is affected due to this disease can be seen.

We show in Table 2 how the eye circulatory system transformed as vector can be analyzed from excel tools an even by more advanced ones like Jupiter Notebook tools.

Choroid (802 nm) – vector	Choroid (802 nm) – pur&$$$;	Choroid (802 nm) – Gr&$$$;	Choroid (802 nm) – Bl&$$$;	Choroid (802 nm) – Cy&$$$;	Choroid (802 nm) – Gre&$$$;	Choroid (802 nm) – Yellow&$$$;	Choroid (802 nm) – Ora&$$$;	Choroid (802 nm) – &$$$;
31	31	34	65	96	128	160	192	224
32	32	34	65	96	128	160	192	224
34		35	65	96	128	160	192	224
34		36	65	96	128	160	192	224
35		37	65	96	128	160	192	224
36		37	65	96	128	160	192	224
37		37	65	96	128	160	192	224
37		38	65	96	128	160	192	224
37		38	65	96	128	160	192	224
38		39	65	96	128	160	192	224
38		39	65	96	128	160	192	224
39		40	65	96	128	160	192	224
39		40	65	96	128	160	192	224
40		40	65	96	128	160	192	224
40		41	65	96	128	160	192	224
40		42	65	96	128	160	192	224
41		42	65	96	128	160	192	224
42		42	65	96	128	160	192	224
42		43	65	96	128	160	192	224

Table 2.

Shows only 20 rows from 40,302 of the real data set for an OCT image.

We show in Figure 19 an important result. The vector means that the OCT image at different frequencies is now represented as a distribution function. We find at the X axis the 0 to 255 gray levels and at the Y axis the count of pixels belonging to that level. This new representation of SatO₂ gives an exact picture about the performance of the vein eye system. Taking a look at Figure 19 Healthy eye-1, we notice clearly the distribution of SatO₂ in the fondues vein system. The maximum SatO₂ values are in the range between 190 and 240 gray levels, just inside the HBO₂. The lowest SatO₂ levels are between 0 and 95 gray levels, belonging to the HB. Figure 19 shows that we can find a set of clearly different SatO₂ distributions, and this is an important result since we can find detailed differences numerically represented that cannot be distinguished by traditional human visual OCT analysis. This of course is not a conclusive statement; however, first results go to that direction.

Figure 19.

Histogram of different OCT images. Horizontal axis represent the 0 to 255 gray levels and vertical axis represent the count per level.

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.

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.

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.

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.

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.

Acknowledgments

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

Conflict of interest

The authors declare that they have no conflicts of interest.