Mathematical definition of the Placido irregularity indices
The rPACI package can read corneal topography files and then analyze them. This analysis includes the computation of some Placido irregularity indices. The indices computed by rPACI allow to discriminate between normal and irregular corneas. Most of them were introduced and validated with real datasets in the scientific publications Ramos-López et al. (2011) and Ramos-López et al. (2013). An additional index, based on a naive Bayes classifier, was proposed later, and tested with a different database, in Castro-Luna, Martínez-Finkelshtein, and Ramos-López (2020). In these papers, all indices demonstrated a good sensitivity for the detection of keratoconus, a corneal disease.
The purpose of this vignette is to explain the mathematical definition and the meaning of the indices given by rPACI. They can be split into two categories: primary and combined indices. The primary indices are: \(PI_1\), \(PI_2\), \(PI_3\), \(SL\), \(AR_1\), \(AR_2\), \(AR_3\), \(AR_4\), \(AR_5\). They all measure certain geometrical properties of the data distribution. Based on them, other combined indices are computed: \(GLPI\) (a generalized linear model) and \(NBI\) (naive Bayes index).
Data structure
First, we assume a data.frame with corneal data is already available, which may have been loaded from a file using the function readFile (see Workflow of rPACI pacakge), simulated using simulateData (see Simulating corneal datasets), or by other ways (as long as it meets the dataset requirements below).
The dataset consists of three columns: x, y (with the X and Y Cartesian coordinates of data points), and ring index (1, 2, …). The number of rows (number of data points) in the dataset depends on the data availability (readFile by default only reads complete rings, and simulateData also simulates complete rings), and the chosen number of innermost rings to use (an optional parameter for readFile, 15 by default).
dataset = simulateData(rings = 13)
plot(dataset$x,dataset$y)
If \(N\) is the number of complete rings to be used and \(N_A\) is the number of points per ring (in the example, \(N=13\) and \(N_A=256\), its default value), the format in dataset is the following:
| \(x_1\) |
\(y_1\) |
\(1\) |
| \(x_2\) |
\(y_2\) |
\(1\) |
| \(x_3\) |
\(y_3\) |
\(1\) |
| … |
… |
… |
| \(x_{N_A}\) |
\(y_{N_A}\) |
\(1\) |
| \(x_{(N_A+1)}\) |
\(y_{(N_A+1)}\) |
\(2\) |
| \(x_{(N_A+2)}\) |
\(y_{(N_A+2)}\) |
\(2\) |
| … |
… |
… |
| \(x_{(2 N_A)}\) |
\(y_{(2 N_A)}\) |
\(2\) |
| … |
… |
… |
| \(x_{((N-1) \times N_A + 1)}\) |
\(y_{((N-1) \times N_A + 1)}\) |
\(N\) |
| … |
… |
… |
| \(x_{(N \times N_A)}\) |
\(y_{(N \times N_A)}\) |
\(N\) |
There are \(N_A\) rows per ring (\(N\) rings), and thus, a total of \(N \times N_A\) data points (in the example, \(N \times N_A = 13 \times 256 = 3328\)). The dataset shall not contain missing data (this is guaranteed if using readFile with default parameters or simulateData).
Thus, we have \(N \times N_A\) data points corresponding to \(N\) complete rings, given in Cartesian coordinates. Let us define \(R_k\), the set of points belonging to the \(k\)-th ring/mire (from the inside out) and \(k \in \{1, \dots, N\}\) is the ring index. Then, a single data point \(P_j=( x_j, y_j )\) (corresponding to the j-th row of the dataframe) belongs to \(R_k\), i.e., \(P_j \in R_k\) if \(j \in J_k\), \(J_k:=\{ n_k, n_k+1, ..., n_k+(N_A-1) \}\), with \(n_k = 1+N_A(k-1)\) (\(J_k\) is the set of indices \(j\) which correspond to the \(k\)-th mire \(R_k\), and \(n_k\) is the first (smallest) of them).
Primary indices
The first step to calculate the indices is to compute the best-fitting circle (with center \(C_k\) and (average) radius \(AR_k\)) for each ring, with \(k \in \{1, \dots, N\}\). This is done by least-squares fitting of a general circle equation to the Cartesian coordinates of data points in ring \(k\) and then, the geometrical parameters (center and radius) are computed from the coefficients (see Ahn, Rauh, and Warnecke (2001) and Ramos-López et al. (2011) for more details). These centers and radii contain information about the displacement or off-centering of the mires, as well as their size and regularity. A high level of drift or variability in these parameters is a signal of corneal irregularity (Ramos-López et al. (2011)).
From the best-fitting circle centers \(C_k\), \(k \in \{1, \dots, N\}\), a regression line can be adjusted to their coordinates (\(y\) over \(x\)), yielding a slope \(m\) and an intercept (which is not used). This line contains information about the direction of the displacement of the centers, which can be a signal of keratoconus (more specifically, the upper-bottom or north-south asymmetry is a marker for keratoconus, see Ramos-López et al. (2013)).
One can find also the best-fitting ellipses (with centers \(\tilde{C}_k\) and axis ratio \(c_k\), i.e., ratio or quotient between the major and the minor axis) for each ring (\(k \in \{1, \dots, N\}\)). This process is also based on a least-squares fit of the general ellipse equation to the Cartesian coordinates of data points of mire \(k\) (see Ahn, Rauh, and Warnecke (2001) and Ramos-López et al. (2011) for more details). A high level of variability in the axis ratio is also a signal of corneal irregularity (Ramos-López et al. (2011)).
Using the quantities, we can define four corneal irregularity indices, labeled as \(PI_1, PI_2, PI_3\) (from Placido Irregularity indices) and \(SL\):
\(PI_1\) \[ PI_1 = \frac{1}{N} \max_{1 \leq n,m \leq N}{ \|C_n - C_m \| } \] This index measures the diameter of the set of centers \(C_k\) (normalized by the total number of rings \(N\)), where \(\| \cdot \|\) is the standard Euclidean norm in \(\mathbb R^2\). A high value of this index shows a large displacement or off-centering between the mires.
\(PI_2\) \[ PI_2 = \frac{1}{N-1} \sum_{1 \leq n \leq N-1}{ \|C_{n+1} - C_{n} \| } \] corresponds to the total length of the path connecting consecutive centers. \(PI_2\) measures the accumulated drift between each pair of mires. In some cases, \(PI_1\) (diameter) can be relatively small but the centers drift a lot from a mire to the next. \(PI_2\) is able to capture this kind of irregularity, which is an abnormal sign.
\(PI_3\) \[ PI_3 = \sqrt{ \frac{1}{N} \sum_{1 \leq k \leq N} { (c_k - \overline{c})^2 } }, \quad \text{where} \quad \overline{c}=\frac{1}{N} \sum_{1 \leq k \leq N} c_k \] is the standard deviation of the distribution of axis ratios, so that it measures the variability of the axis ratios or eccentricities of the individual rings. A large value of \(PI_3\) indicates a high variability in the shapes of the rings, and thus it is a signal of irregularity.
\(SL\) \[ SL = |m| \] is the absolute value of the slope of the best-fitting line to the coordinates of the centers \(C_k\). High values of this index correspond to vertical mire displacements whereas low values correspond to horizontal displacements.
Apart from these 4 indices, we also take as irregularity indices the values of \(AR_k\) (average radius of the \(k\)-th ring) corresponding to the first (innermost) 5 rings. These values also showed a certain sensitivity for detecting keratoconus (Ramos-López et al. (2011)).
Normalization of the primary indices
In order to obtain indices with values in the same range (more easily comparable and to prevent scale problems when combining them), a normalization of each primary index was performed (Ramos-López et al. (2011) and Ramos-López et al. (2013)). To do it, these indices were tested on a real dataset of corneas of different conditions. Then, their values were linearly translated to \([0,100]\), so that the value corresponding to the best possible condition (less irregularity) was transformed to \(0\), and the worst (more irregularity) to \(100\). Therefore, in the test dataset, the values of all primary indices were between \(0\) (best-case) and \(100\) (worst-case).
In practice, after the transformation, most index values for any cornea will be in \([0,100]\), but in some particular cases, they may be outside that interval. To facilitate their interpretation, we can assume that values below zero correspond to zero, and the maximum irregularity is attained at a certain value (e.g. 150, this value is discussed in Ramos-López et al. (2011)).
The normalization coefficients used in the previous studies (Ramos-López et al. (2011) and Ramos-López et al. (2013)) were the following (for notation simplicity, values on the left-hand side of the expressions represent normalized values whereas values on the right-hand side are values before the normalization (as computed by their definitions above)):
\[ PI_1 = 12368.3980719706 PI_1 - 12.5200561911951 \] \[ PI_2 = 9699.23915314471 PI_2 - 18.2916611541283\] \[ PI_3 = 5233.70399537826 PI_3 - 13.7375152045819\] \[ SL = 50 SL\]
\[ AR_1 = -1961.92346976023 AR_1 + 444.770836424576 \] \[ AR_2 = -562.8465909984122 AR_2 + 279.7430721547980 \] \[ AR_3 = -486.9218093835968 AR_3 + 331.3137688964757 \] \[ AR_4 = -318.192614292727 AR_4 + 298.064078007947 \] \[ AR_5 = -288.4037960143595 AR_5 + 321.4180137915255 \]
Thus, after the normalization (and possible truncation of extremely high or low values), all indices values lay in \([0,150]\), with \(0\) corresponding to the most normal case and \(150\) to the most irregular case. This value of \(150\) is arbitrary and could be changed (increased or lowered), but we found it performed well in practice (Ramos-López et al. (2011) and Ramos-López et al. (2013)).
Combined indices
The primary Placido indices described above, \(PI_1\), \(PI_2\), \(PI_3\), \(SL\), and \(AR_k\), show sensitivity to detecting various irregularities in the cornea, such as keratoconus (Ramos-López et al. (2011), Ramos-López et al. (2013)). However, a single index does not reach sufficient accuracy in the task, and compound indices have been proposed and tested (Ramos-López et al. (2013), Castro-Luna, Martínez-Finkelshtein, and Ramos-López (2020)) in order to improve the performance and precision of the individual indices. These compound indices showed a significant improvement in accuracy when predicting keratoconus.
A brief description of them is given as follows (see the aforementioned references for more details):
GLPI index
The \(GLPI\) index (from Generalized Linear Placido Index) (Ramos-López et al. (2013)) is a generalized linear model computed using some of the primary indices, in the following way: \[ GLPI = 100 \Phi \left( \left(-224.90 + 1.69 PI_1 + 1.28 PI_3 + 1.89 AR(4) + 0.19 SL \right)/20 \right) \]
where \(\Phi\) stands for the cumulative distribution function of the standard normal distribution. This corresponds to a generalized linear regression model with the probit (\(\Phi^{-1}\)) link function. Therefore, \(GLPI\) has values between \(0\) and \(100\). The coefficients in the formula above were obtained by fitting a real-world dataset of normal and keratoconic corneas (Ramos-López et al. (2013)). The index \(GLPI\) was validated giving an excellent discrimination capability of irregular corneas.
NBI index
The compound index \(NBI\) (from Naive Bayes Index), introduced in Castro-Luna, Martínez-Finkelshtein, and Ramos-López (2020), is a Bayesian classifier. More precisely, it is a Bayesian network with the naïve Bayes structure depicted below.
This Bayesian network is a conditional linear Gaussian (CLG) network, with its root node (\(KC\)) being a discrete binary variable and the rest of the nodes (which are some of the primary indices introduced above) being continuous. The parameters of the model (their conditional probability distributions) were reported in Castro-Luna, Martínez-Finkelshtein, and Ramos-López (2020). This model can be used for predicting whether a specific cornea (whose values of the primary indices are known) is a normal cornea or a keratoconic cornea.
Moreover, the probability of being one type or another can be computed as well, either using exact inference or approximate inference with algorithms such as evidence weighting, likelihood weighting, or more generally, importance sampling (Cheng and Druzdel (2000)). Even though exact inference is feasible in this case, approximate inference is easier to implement and to generalize to more complex network structures. In a nutshell, these algorithms consist of simulating a large number of samples from the network, according to the evidence (i.e., variable values that are known a priori), and averaging their likelihoods to estimate the probability of each state of the target variable (in this case, \(KC\)). For a deeper explanation, we refer the readers to Castro-Luna, Martínez-Finkelshtein, and Ramos-López (2020) and its references.
Thus, the posterior probability estimation can be computed efficiently with several approaches. In rPACI, we make use of the R package bnlearn (Scutari (2010)), which includes an implementation of the likelihood weighting algorithm, for computing the index NBI from the primary indices values of the bottom nodes.
References
Ahn, Sung Joon, Wolfgang Rauh, and Hans-Jürgen Warnecke. 2001.
“Least-Squares Orthogonal Distances Fitting of Circle, Sphere, Ellipse, Hyperbola, and Parabola.” Pattern Recognition 34 (12): 2283–303.
https://doi.org/10.1016/S0031-3203(00)00152-7.
Castro-Luna, Gracia M., Andrei Martínez-Finkelshtein, and Darío Ramos-López. 2020.
“Robust Keratoconus Detection with Bayesian Network Classifier for Placido Based Corneal Indices.” Contact Lens and Anterior Eye 43 (4): 366–72.
https://doi.org/10.1016/j.clae.2019.12.006.
Cheng, Jian, and Marek J. Druzdel. 2000.
“AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks.” Journal of Artificial Intelligence Research 13 (1): 155–88.
https://doi.org/10.1613/jair.764.
Ramos-López, Darío, Andrei Martínez-Finkelshtein, Gracia M. Castro-Luna, Neus Burguera-Giménez, Alfredo Vega-Estrada, David Piñero, and Jorge L. Alió. 2013.
“Screening Subclinical Keratoconus with Placido-Based Corneal Indices.” Optometry and Vision Science 90 (4): 335–43.
https://doi.org/10.1097/opx.0b013e3182843f2a.
Ramos-López, Darío, Andrei Martínez-Finkelshtein, Gracia M. Castro-Luna, David Piñero, and Jorge L. Alió. 2011.
“Placido-Based Indices of Corneal Irregularity.” Optometry and Vision Science 88 (10): 1220–31.
https://doi.org/10.1097/opx.0b013e3182279ff8.
Scutari, Marco. 2010.
“Learning Bayesian Networks with the Bnlearn R Package.” Journal of Statistical Software 35 (3): 1–22.
https://doi.org/10.18637/jss.v035.i03.