confocal corneal endothelium dystrophy’s ...jestec.taylors.edu.my/vol 15 issue 5 october...

Journal of Engineering Science and Technology Vol. 15, No. 5 (2020) 3419 - 3432 © School of Engineering, Taylor’s University

3419

CONFOCAL CORNEAL ENDOTHELIUM DYSTROPHY’S ANALYSIS USING A HYBRID ALGORITHM

K. V. CHANDRA1, 2, B. M. MURARI1,*,

1Department of Sensor and Biomedical Technology School of Electronics Engineering, Vellore Institute of Technology, Vellore, India

2Department of Electronics and Instrumentation Engineering, Vallurupalli Nageswara Rao Vignana Jyothi

Institute of Engineering & Technology, Hyderabad, India *Corresponding Author: [email protected]

Abstract

The cornea is the most sensitive biological membrane in the human eye which consists of five layers viz. epithelial, Bowman, stroma and Descemet's and endothelial membrane. Endothelial layer in the cornea is the most vulnerable membrane among all the five membranes. This layer is significant as it allows free flow of aqueous humor fluid inside and outside of the cornea. Fuch’s dystrophy (FD), Advanced Fuch’s dystrophy (AFD), Posterior Polymorphous Corneal Dystrophy (PPCD), and Iridocorneal Dystrophy (ICD) are the major dystrophy’s that affects the endothelium layer directly. The cells are drastically decreased when the dystrophies affect the endothelium layer. Morphological operations (MO1 & MO2) are used to estimate the endothelium cell density. A novel hybrid algorithm (HA) using Confocal Microscopy (CM) images have been proposed to identify the mean value of unique cells, standard deviation, area, cell densities and its quantitative values for the endothelial layer. The median filter is used to eliminate the noise. The estimated MSE (Mean square error) and PSNR (Peak Signal to Noise ratio) represented the filtered image for further processing. Altogether Eight (8) confocal endothelial dystrophic and two (2) normal images were used for analysis. The results harvested with the HA are comparable with standard manual (SM) and conventional cell density approach. The average error differences of MO1 & MO2 and S-PSO (Snake Particle Swarm Optimization) to the HA is 12.14%, 20.46%, 9.73%, and 8.00% respectively. The average inspection time of HA is 61.48 ms with standard deviation (SD) of 1.59 ms. The proposed algorithm showed high accuracy with a very low processing time of 58.57 ms which is suitable for detection of the membrane at an early stage to diagnose any disease related to the endothelial layer.

Keywords: Confocal microscope, Fuch’s dystrophy, Median filter, Morphological.

3420 B. M. Murari and K. V. Chandra

Journal of Engineering Science and Technology October 2020, Vol. 15(5)

1. Introduction

The human cornea is dome structure with approximately 12 mm×11 mm dimensions which has five sophisticated layers of epithelium layer, Bowman's layer, stroma layer, Descemet's layer, endothelium layer. Epithelium layer being outermost part of the eye which is strong enough in the cornea. It is composed of many Basel structural cells and 45 non-keratinized thin layers with an approx. the thickness of 50 µm. These cells tightly form a close junction which acts as an effective barrier of aqueous fluid loss [1]. Bowman’s membrane is the soft layer and has 8-12 µm thick in size situated exactly in the middle of the superficial epithelium layer and posterior stroma layer. It consists of a group of collagen fibrous tissue which protects the subepithelial layer and puts a strong hold of basement and cause of transparency in corneal physiology.

The Wateriest structural layer in the cornea is the stroma layer. In a compositional matter of layers, stroma occupies around 90% in the cornea which is nearly 500 µm thick and has 150 flat lamella tissues. It is perfectly transparent in nature and each lamella is 1.5 µm to 2 µm of thick. Any dystrophy caused in the cornea is usually due to the disarrangement of lamellas tissues during the development of embryology of cornea which leads to keratoconus and macular corneal dystrophy. The thickness of descemet’s membrane is 5 µm in children and 10-14 µm in adults. It forms the junction at peripheral endothelium layer. Endothelium layer is important to transfer the aqueous fluid layer to layer and peripheral and inferior part of the cornea. It is hexagonal in shape and comprised of six-sided cell structure, with a standard density ranging between 2500 cells/mm2 to 3150 cells/mm2. The six-sided hexagonal structure has low energy image features. Genetically dystrophy’s and degenerations are more responsible for reduced endothelium cell density. The corneal Endothelium layer plays a crucial role in maintaining transparency in the cornea. Endothelium dysfunction minimizes the cell density below the standard value. The structural integrity of the endothelium layer is influenced by extrinsic & (age and gene) and intrinsic factors (UV radiation and infection). Endothelium cell density (ECD) is a significant clinical parameter for a monolayer of diagnosis. Polygon shaped cells in the endothelium layer discriminate along with age. The opaque sclera is encompassed by the cornea.

Discrete Fourier Transform (DFT) is applied to the images and Acquired Automatic endothelium cell density. In an image, all spatial frequency components are acquired. the repetitive spatial component structures are retrieved from DFT. In this method, all images are acquired from cornea bank Berlin (www.karger.com). Another approach was neural network segmentation module (NNSM). The skeletonized cell contours were used to process the image. Splitting of the image and merging of the image is performed to get Estimate ECD. This method requires external experts to get correct the data from NNSM. MO1, MO2, S-PSO are also existing methodologies to identify dystrophies. The disadvantage of these methods is high processing time & diagnosis time.

Endothelium cell shape, cell size, cell elongation factor, cell compression factor, cell circularity factor cannot be identified with these techniques, but they are very helpful to identify FD, AFD, PPCD, ICD. When these dystrophies encounter endothelium corneal layer, the cell structure completely gets damage and cause severe visual impairment. Development of an automatic algorithm is

http://www.karger.com/

Confocal Corneal Endothelium Dystrophy’s Analysis Using . . . . 3421


crucially important for clinicians or ophthalmologists to identify different abnormalities in endothelial layer [2].

The proposed algorithm executes automatically in order to harvest the endothelium cell density total number of (10) Fuch’s endothelium dystrophy’s datasets are tested with the proposed algorithm and compared with existing methods. Cubic-Spline interpolation is adopted for re-sampling the images, harvested cell density, and structures of polymegethism and Pleomorphism of the cells. In further processing, endothelium corneal layer images are tested with the hybrid algorithm.

2. Diseases Affecting Corneal Endothelium The most common dystrophy is FD and AFD which frequently attacks endothelial cells. In this work eight (08) images (Ref -http://rod-rep.com -Rotterdam Ophthalmic Data Repository) were processed through the proposed algorithm, the result of this, density loss and visual impairment as shown in Fig. 1(a). The corneal guttate early stage of FD as shown in Fig. 1(b) can be predicted much early before vision gets fuzzy [3, 4]. All diseases/dystrophies severely damage the endothelium cell density and cause fuzzy/hazy/foggy/blur vision in the eye. Monitoring the condition of the patient’s cornea is imperative.

Corneal most susceptible layers are epithelium, stroma, and endothelium which are more prone to the diseases. FED causes severe vision problem and it leads to blind impairment as shown in Fig. 1(c). The characteristic features are edema and apparent endothelial cell loss. The FECD (Fuch’s Endothelium Cell Density) causes progressive loss of endothelial cells and extracellular cells which are deposited in the form of guttae swelling of the cornea due to critical cell density, results poor vision [5, 6].

(a) (b)

(c)

Fig. 1. (a) Fuchs endothelium image (b) Corneal guttae image (c) Advanced Fuch’s image.



Advantages of confocal microscopes in corneal layer scanning Microscopes enlarge the vision of micro-structures/microcells which cannot be seen through the naked eye. Laser scanning confocal microscope, fluorescence microscope, and the phase-contrast microscope are widely used for diagnosing dystrophies caused to five layers in the cornea [7]. Laser scanning confocal microscope is used to acquire 2D endothelium corneal structure with different laser beams to observe the thick structure of a cell with varied focal distant planes.

CM provides high definition and high-resolution endothelium cell images which are non-invasive, non-destructive. The CM is the advanced version of fluorescence microscopy. In fluorescence microscopy light hitting on images leads to foggy visualization. These can be swamped with CM. The high resolution of an endothelium image can be observed in CM due to the pinhole arrangement [8, 9]. Another feature of CM is Raster scanning is done in CM to get detail resolution of an endothelium image. Different wavelengths of light are projected on cell and capturing of cell structure with different focal points are carried out by integrating all scanned points to get a high-resolution image of the endothelium.

3. Methodology Considering Fig. A-1 (Appendix A), the image represented F(x, y) will be re-sampled to the best fit for resolution enhancement. CM dataset images are having variations in the geometric structure for processing and extracting favorable output. In order to maintain all tested images to fit into the standard geometric structure, were re-sampled the image with cubic-spline interpolation (CSI). CSI interpolates the endothelium image pixel values and gradient pixel values. Each pair of co-ordinates interpolates for new pixel value by fitting into the cubic spline curve, this cubic spline curve initiates from known pixel quantities [10]. Interpolated gradient pixels in the cubic form initiates best visual and good accuracy in order to estimate mid-range pixel of an endothelium image.

The following mathematical equations were followed to obtain CSI. The original image is represented as F (x, y) (1) The interpolated image is

𝐹𝐹′(𝑥𝑥, 𝑦𝑦) = 𝑧𝑧 ∑ 𝑓𝑓𝑗𝑗 �𝑚𝑚𝑗𝑗 − 𝑡𝑡�𝑛𝑛𝑗𝑗��2

+ (1 − 𝑞𝑞)∫ 𝜆𝜆(𝑛𝑛)�𝑡𝑡′′(𝑛𝑛)�2𝑑𝑑𝑛𝑛𝑛𝑛𝑛𝑛0

𝑘𝑘−1𝑗𝑗=0 (2)

where z is the balance value, fj is a jth pixel of y co-ordinate function, tn is a gradient parameter of CSI t(n), λ(n) are constants, and λj = jth pixel quantity of smoothness.

Extraction of HSI color feature from CM image and histogram equalization is applied to enhance brightness and contrast of CM images [11]. The logarithmic mapping function is applied to minimize the structural complexity in an endothelium image. Image contrast is a major element considered in the processing of the corneal image. ‘𝛾𝛾 ’ function applied to the CM images, which should obey the boundary condition, i.e., 0 < 𝛾𝛾 < ∞, where ‘𝛾𝛾’ determines the contrast level against image background to track it down the endothelial cell. The 𝛾𝛾 value ‘1.5’ is considered in the proposed algorithm. In the primary process, the endothelial cells are tracked to identify the object boundaries to segregate them from other objects. Following mathematical equations were followed to obtain contrast enhancement

i(u,v) = k R(u,v) + c (3)



where R(u,v) is the input of the image, i(u,v) is the output of the image, k is an operator, R is the neighbourhood pixels, and c is a constant.

Clustering threshold is used to segregate endothelium normal and abnormal cells in terms of pixel intensities. Histogram of each image can show the maximum intensity level [12]. The endothelium layer image is processed in order to identify the bright pixel objects based on subtraction of pixel value from the average intensity of the window. Subsequently, background pixels are corrected. In Eq. (4) ‘j’ value is ‘90’.

𝐺𝐺(𝑢𝑢, 𝑣𝑣) = �1, if 𝑔𝑔(𝑢𝑢, 𝑣𝑣) ≥ 𝑗𝑗0, if𝑔𝑔(𝑢𝑢, 𝑣𝑣) < 𝑗𝑗 (4)

where j = thresholding value.

4. Results and Discussion The total number of ten (10) CM images is taken with z-ring which gives correct dimensions irrespective of manual artifacts generated while scanning. The images inspected field is 460*345μm at high magnification (40X) with the size of 768*576 pixels. All acquired images are re-sampled with cubic-spline interpolation in order to fit in standard dimensions.

The HA is tested on two data sets. Each data set is constructed with five (05) endothelium images. In 1st dataset five (05) abnormal endothelium images are tested, in 2nd dataset two (02) normal endothelium and three (3) abnormal endothelium images have been tested to estimate the cell density. Table 1 represents the segmented image used for pre-processing in order to obtain the histogram equivalent image. The image is further pre-processed to eliminate the high-frequency components (noise) using a median filter. Normal endothelium cells for adult’s ranges from 2400 cells/mm2-3200 cells/mm2 [13]. Below this range, cells are categorized as abnormal. For normal endothelium images, the observed mean values (MV) are at ‘2.63 mm2’ and SD is above ‘10.52 mm2. Normal image cells are hexagonal structure; six side contours are equal which gives mean variations in between 2.60 mm2 to 2.70 mm2 and SD above 10.50 mm2. Abnormal endothelial cells are usually in polygon shape or trapezoidal shape. The calculated cell densities for abnormal images are I1(1531), I2(1373), I3(1873), I4(1801), I5(2162) below normal cell density range.

In HA images are acquired from CM with clear boundaries of cell contours obtained with segmentation. The contrast level is improved with histogram equalization. The bright objects above the threshold value (>90), has been clustered. Binary image inversion flipped high pixel intensity to low and vice-versa. Particle cell boundaries are clearly tracked and identified with pixel variation from binary image inversion, which results in clear particle cell density. At every stage, the image output is clearly tabulated as shown in Table 1. Cell count (CC) is also estimated at distinctive areas Region of Interest (ROI) of an image. Error deviation percentage is also estimated using the following Eq. (5) to (7)

cell density (CD) = cell count in ROI (CC)Total area of image(A)

(5)

where A= Height of the image × Length of the imag (6) The error is calculated with Eq. (7)

𝐸𝐸% = �Manual cell density(standard measurement)-HA cell densityManual cell density

�× 100 (7)



Table 1. Processing of images with different segments (data set 1).

Images Segmented Image

Histogram of the Image

Median Filtered image

Auto clustering Threshold

Binary Image Inversion

CD cells/ mm2

I1

1531

I2

1373

I3

1873

I4

1801

I5

2162

The mean value for I1 (0.28 cm), I2 (0.27 cm), I3 (0.31 cm), I4 (0.31 cm) and I5 (0.30 cm) are approximately close to the (0.30 cm), for abnormal images. The SD for I1 (1.10 cm), I2 (1.08 cm), I3 (1.15 cm), I4 (1.15 cm), and I5 (1.19 cm) are



approximately close to the (1.20 cm), for abnormal images as shown in Fig. 2. As all the images are extracted from images with a visual abnormality. The area estimated for all the images after processing is constant (0.116342×106) because of the images are interpolated with Cubic Spline approach and the size of the image is not deferred in all the images (I1 to I5) as shown in Fig. 3.

The results harvested from auto-clustering threshold is also represented in Fig. 3. The Lower and upper limit values are 10 and 245. The resultant value for the images I1-I5 is above 90, concludes that the images are bright images. After a repetitive investigation, the images did not attain a value of less than 90, which represents dark images.

Fig. 2. Variation of Mean, SD, and Area I1-I5.

Fig. 3. Auto clustering threshold (I1-I5).

Confocal images consist of both low frequency as well as high-frequency noise components. The PSNR values represented in I1(45.8dB), I2(55.2dB), I3(57.2dB), I4(59.1dB), I5(57.9dB) as shown in Fig 4. this information is safe guarded in endothelium images. Mean square error and Peak signal to noise ratio is estimated using the following Eq. (8) and (9)

MSE = ∑ ∑ [𝐼𝐼(𝑖𝑖, 𝑗𝑗) − 𝐼𝐼/(𝑖𝑖, 𝑗𝑗)]𝑧𝑧=1𝑗𝑗=0

𝑦𝑦=1𝑖𝑖=0 (8)

10 10 10 10 10

245 245 245 245 245

94 94 96 94 95

0

100

200

300

I1 I2 I3 I4 I5Lower LimitUpper Limit Upper Limit Result



PSNR db = 10 log 10 2552

𝑀𝑀𝑀𝑀𝑀𝑀 (9)

No. of rows (y), No. of columns (z) the input of the image I (i,j). the output of the image I|(i,j).

Fig. 4. Peak signal to noise ratio vs. mean square error.

Considering Table 2,The calculated cell densities for abnormal images are I6(2382), I8(1012), I9(943), below the normal range, and normal images are I7(2455), I10(3069) under normal cell density range.

The mean value for I6 (0.23 cm), I7 (0.23 cm), I10 (0.22 cm) are approximately close to the (0.20 cm), for normal images. I8 (0.34 cm), I9 (0.35 cm) for abnormal images. The SD for I6 (1.00 cm), I7 (1.00 cm), I10 (0.9 8 cm) are close to the (0.9 cm), for normal images. I8 (1.20 cm), I9 (1.22 cm) for abnormal images as shown in Fig 5.

As all the images are extracted with visual normality and abnormality. The area estimated for all the images after processing is constant (0.116342×106) because of the images are interpolated with cubic spline approach and the size of the image is not deferred from I6 to I10.

The results harvested from auto-clustering threshold represented in Fig. 6. The lower and upper limit values are 10 and 245. The resultant value for the images I6 - I10 is above 90, concludes the bright images. After a repetitive investigation, the images did not attain a value of less than 90, which represents dark images.

Three images are processed with MO1, MO2, S-PSO algorithms and obtained maximum error as shown in Table 3. The error is minimalistic When compared with existed algorithms, and improves accuracy level by 2%, as well as various statistical parameters, have been estimated. With the proposed HA, the inspection time is negligible when compared with other existing techniques as explained earlier. Therefore, the HA method can be a useful tool for endothelium examination.

45.8

55.2 57.2 59.1 57.9

3.5 5.5 6.1 6.2 5.9

0

10

20

30

40

50

60

70

I1 I2 I3 I4 I5

PSNR MSE



Table 2. Processing of images with different segments (Data set 2). S.

No. Segmented

Image Histogram of the Image Median Filtered image

Auto clustering Threshold

Binary Image Inversion CD

I6

2382

I7

2455

I8

1012

I9

943

I10

3069



Fig. 5. Variation of mean, SD, and area for I6-I10.

Fig. 6. Auto clustering Threshold Result for I6-I10.

Table 3. Comparison of Errors with MO1 vs. MO2 vs S-PSO (Sharifa et al. [1]) vs. HA

S.no MO1 MO2 S-PSO Approach Hybrid Algorithm (HA)

1 13.19 22.24 12.20 10.02 2 12.28 20.93 9.55 7.5 3 10.96 18.23 7.45 6.7

5. Conclusion A novel hybrid algorithm has been developed to examine the corneal endothelial layer. In this study, HA was used on five abnormal Fuch’s dystrophy images and three abnormal and two normal images. The HA investigated for the analysis of the condition of the corneal endothelium layer with minimal diagnostic time along with cell density. The image data sets are processed for segmentation, auto-clustering and binary image inversion for meticulous estimation of the cell density.

The estimated results are correlated with the existing approaches like MO1, MO2, S-PSO. The results obtained with the proposed algorithm comparable to the



MCD, which is considered as a reference standard. With MO1, MO2, S-PSO approaches the deviation from MCD is significantly high. Further, the estimation of cell density, size, and shape produced showed more favorable results. The calculated cell densities for abnormal images are I1(1531), I2(1373), I3(1873), I4(1801), I5(2162), I6(2382), I8(1012), I9(943) below normal cell density range. and normal images are I7(2455), I10(3069) under normal cell density range.

The processing time interval is also minimized with the proposed approach. This approach improves for identification of cornea health condition, intensity, and level of damage. The existing approaches produced considerable average error values when compared with the proposed HA Approach.

Further research to harvest the statistical parameters in order to investigate the pleomorphism, polymegethism of each cell of the Fuch’s dystrophy image can be undertaken to make it a more robust technique for better analysis of endothelium layer as well other layers of the cornea.

Nomenclatures FJ Jth Pixel of y- Coordinate function J Threshold value t Gradient parameter u X-coordinate pixel intensity v Y-coordinate pixel intensity z Balance value Greek Symbols 𝜆𝜆 Pixel quantity of smoothness ℽ Image contrast/brightness Abbreviations

AFD Advanced Fuch’s Dystrophy CD Cell Density CM Confocal microscope CSI Cubic-spline interpolation ECD Endothelium cell density FD Fuch’s dystrophy FECD Fuch’s endothelium cell density HA Hybrid Algorithm HSI Hue, Saturation Intensity I Image IBO Identified the Bright Object ICD Irido Corneal Dystrophy MO1&MO2 Morphological Operations 1&2 PACAP Pituitary Adenylate Cyclasea Activating Endothelial Cells PPCD Posterior Polymorphous Corneal Dystrophy ROI Region Of Interest



References

1. Sharif, M.S.; Qahwaji, R.; Shahamatnia, E.; Alzubaidi, R.; Ipson, S.; and Brahma, A. (2015). An efficient intelligent analysis system for confocal corneal endothelium images. Computer Methods Programs in Biomedicine, 122(3), 421-436.

2. Amin, S.R.; Baratz, K.H.; McLaren, J.W.; and Patel, S.V. (2014). Corneal abnormalities early in the course of Fuchs endothelial dystrophy. journal of Ophthalmology, 121(12), 2325-2333.

3. Patel, S.V.; and McLaren, J.W. (2013). In vivo confocal microscopy of Fuchs endothelial dystrophy before and after endothelial keratoplasty. journal of JAMA Ophthalmology, 131(5), 611-618.

4. Agarwal, M.; and Mahajan, R. (2018). Medical image contrast enhancement using range limited weighted histogram equalization. procedia computer science, International Conference on Smart Computing and Communications, Kurukshetra, India. 149-156.

5. Grupcheva, C.N.; Craig, J.P.; Sherwin, T.; and McGhee. C.N.(2001). Differential diagnosis of corneal edema assisted by in vivo confocal microscopy. Clinical Experimental Ophthalmology, 29(3), 133-137.

6. Eghrari, A.O.; Riazuddin, S.A.; and Gottsch, J.D. (2015). Fuchs Corneal Dystrophy. Progress in Molecular Biology and Translational Science, 134(2), 79-97.

7. Batista, A.; Silva, S.F.; Domingues, J.P.; and Morgado, A.M. (2014). Fluorescence lifetime based corneal metabolic imaging. procedia technology, Conference on Electronics, Telecommunications and Computers, 17, 281-288.

8. Niederer, R.L.; and McGhee, C.N.J. (2010). Clinical in vivo confocal microscopy of the human cornea in health and disease. Progress in Retinal and Eye Research, 29(1), 30-58.

9. Klais, C.M.; Bühren, J.; and Kohnen, T. (2003). Comparison of endothelial cell count using confocal and contact specular microscopy.Ophthalmologica, 217(2), 99-103.

10. Dyer, S.A.; and Dyer, J.S. (2001). Cubic-spline interpolation part 1. IEEE Instrumentation Measurement Magazine, 4(1), 44-46.

11. Michael, K.; Andrew, W.; and Demetri Tterzopoulos. (1988). Snakes active contour models. International Journal of Computational Vision, 1(4), 321-331.

12. Naidu, M.S.R.; Rajesh Kumar, P.; and Chiranjeevi, K. (2018). Shannon and Fuzzy entropy based evolutionary image thresholding for image segmentation. Alexandria Engineering Journal, 57(3), 1643-1655.

13. Wilson, R.S.; and Roper Hall, M.J. (1982). Effect of age on the endothelial cell count in the normal eye. British Journal of Ophthalmology, 66(8), 513-515.



Appendix A

Computer Programme

A.1. Programme Flow The proposed Hybrid Algorithm (HA) flow chart is shown in A-1. The three different components are 1) Image Acquisition 2) Pre-processing 3) Processing. The acquired endothelium image is pre-processed with cubic-spline interpolation for smoothing the images and to fit into the standard geometric structure. To remove noises present in the image, the Median filter with the 2*2 size is applied. Histogram equalization HSV (Hue, Saturation Value) is applied to separate the color information. For transforming the lower intensity pixels values into higher intensity logarithmic mapping is applied.

A-2. Auto Clustering Threshold

The clustering threshold technique has been used to further enhance the contrast levels of the image and to identify the bright object ‘𝛾𝛾’ and is in interval between 0 < 𝛾𝛾 < ∞, where 𝛾𝛾 =1.5. The backdrop noise is corrected before performing the clustering operation as shown in flowchart Fig. A-2.

Fig. A-1. Main flow chart of the hybrid algorithm used in this study.



Fig. A-2. A flow chart for auto clustering threshold.

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