In this paper we present two methods of binarization of corneal endothelial images. The binarization is a first step of advanced image analysis. Images of corneal endothelial obtained by the specular microscopy have a poor dynamic range and they are usually non-uniformly illuminated. The binarization endothelial images is not trivial. Two binarization algorithms are proposed. The output images are presented. The quality of algorithms is discussed

Gronkowska-Serafin, Jolanta

Piórkowski, Adam

Szostek, Kamil

Portal Czasopism Naukowych (E-Journals)

Schedae Informaticae, vol. 20, pp. 211-2218 
Kraków 2011 
Published online January 23, 2012 
DOI 10.4467/20838476SI.11.012.0298 
 
PROBLEMS OF CORNEAL ENDOTHELIAL 
IMAGE BINARIZATION 
Kamil Szostek, Jolanta Gronkowska-Serafin, Adam Piórkowski 
 
ABSTRACT 
In this paper we present two methods of binarization of corneal endothelial images. The 
binarization is a first step of advanced image analysis. Images of corneal endothelial obtained by 
the specular microscopy have a poor dynamic range and they are usually non-uniformly 
illuminated. The binarization endothelial images is not trivial. Two binarization algorithms are 
proposed. The output images are presented. The quality of algorithms is discussed. 
 
Keywords: binarization, corneal endothelia, image processing 
S C H E D A E I N F O R M A T I C A E
VOLUME 20(2011) DOI 10.4467/20838476SI.11.012.0298
Problems of Corneal Endothelial Image Binarization
Kamil Szostek1, Jolanta Gronkowska-Serafin2, Adam Piórkowski1
1Department of Geoinfomatics and Applied Computer Science,
AGH University of Science and Technology, Cracow, Poland
e-mail: {szostek, pioro}@agh.edu.pl
2Chair and Departmenf of Ophthalmology,
Pomeranian Medical University, Szczecin, Poland
Abstract. In this paper we present two methods of binarization of corneal
endothelial images. The binarization is a first step of advanced image analy-
sis. Images of corneal endothelial obtained by the specular microscopy have
a poor dynamic range and they are usually non-uniformly illuminated. The
binarization endothelial images is not trivial. Two binarization algorithms are
proposed. The output images are presented. The quality of algorithms is
discussed.
Keywords: binarization, corneal endothelia, image processing.
1. Corneal endothelial images
Corneal endothelium is a monolayer of mainly hexagonal cells covering the posterior
corneal surface. These cells are responsible for maintaining the corneal transparency
dependent on the stable hydratation level (78%). Cells are closely joined “membrane
to membrane” by zonulae occludens so there is no intracellular space; it allows to
dehydrate the corneal stroma and epithelium. In human in contrary to many other
species mitotic activity of endothelial cells is clinically insignificant so the cell num-
ber decreases continuously in the lifetime. The empty place left by necrotic cells is
fulfilled by enlarging distorted neighboring cells. Every intervention in the anterior
chamber space (surgery, trauma, etc.) as well as many congenital (PPD) and ac-
quired diseases (e.g. glaucoma, diabetes) result in corneal cell number decrease. In
the case of severe endothelial damage (cell density below 500 cells/mm2) the remain-
ing cells are no more able to enlarge and compensate the loss; water going freely by
212
simple diffusion from the aequs fluid stops to be removed that leads to irreversible
corneal edema with consecutive transparency loss and functional blindness.
Specular microscopy allows to assess the endothelial layer in vivo – traditional
parameters as corneal density (CD), coefficient of cell size variation (CV) and per-
centage of hexagonal cells (H) are not always sufficient in predicting the postsurgical
corneal state, so many studies are conducted in search of additional cell features.
2. Medical image processing
Medical image processing is a wide area of researchers’ interest. Correct image anal-
ysis is important in diagnosis and treatment effectiveness assessment. Endothelial
images may contain important information about the corneal condition, impossible
to achieve in the normal slit lamp examination [2]. The corneal endothelial images
processing, binarization and segmentation were studied long time before [4]. The
problem of processing and analyzing corneal endothelial images has already been
discussed and the non-subsampled wavelet pyramid decomposition of lowpass region
technique was proposed [3]. Also the directional filter bank was presented to isolate
directional features in the endothelial cells image [1] and automatic recognition of
different cell layers was introduced [5].
The problem of corneal endothelial images binarization is the first step to prepare
these images for processing to determine factors that can easily describe an eye
condition. Binarization should be as automatic and as precise as possible to give
the best results and to provide source for factor calculations.
3. Binarization
Image binarization is a process which results in a binary image that consists of only
two possible values, usually 0 (displayed as black) and 1 (white). Any color or
gray images can be processed to obtain a two-level image. For bi-level images it is
easier to write algorithms that find specific image features, like patterns, or to count
interesting elements; usually binarization is a first step in image analysis. What is
more bi-level images occupy the least amount of computer memory [6, 7].
Image binarization is not a trivial operation, because of source image distortions
and noises. Techniques of binarization vary from simple threshold (Fig. 1b) to
statistical algorithms or series of different image processing operations that may
give more accurate results (Fig. 2c).
Quality of image binarization depends on the quality of the source image and its
gray level dynamics that can be easily observed on the image histogram. The most
213
difficult case is when the interesting elements of the image are surrounded by the
similar background and the image is non-uniformly illuminated [6].
(a) (b) (c)
Fig. 1. An example of bad and good binarization. The source image has a low
dynamic range (a) and auto threshold binarization gives poor results (b). The
required effect is presented in the image (c)
4. Characteristics of the corneal endothelial images
Images of corneal endothelial obtained by the specular microscopy are 8 bit grayscale
images (Fig. 2). The dynamic range is poor: only about 20–30% – between the most
dark and the most bright data pixel are about 40–60 gray levels. What is more,
images are usually non-uniformly illuminated (one side is noticeably brighter than
the other), what has negative influence on the automatic binarization process (Fig.
3). Some other artifacts may appear near the image border that decrease usability
of the parts of the image.
The cellular structures are usually well noticeable: brighter cells and darker
boundaries. The sizes of the cells may vary (Fig. 2a and Fig. 2b). Some images also
contain dark spots that indicate cell loss (Fig. 2c) and are important for diagnosis of
corneal guttata connected with the higher risk of Fuchs dystrophy and postsurgical
corneal decompensation.
214
(a) (b) (c)
Fig. 2. An example of images of corneal endothelial before any processing
Fig. 3. Heterogeneous illumination of the corneal endothelial image with brightness
graphs of maximum (red), mean (green) and minimum (blue) values for each column
215
5. Binarization of corneal endothelial images
The binarization is the first step of the corneal endothelial image analysis. It is
important for further analysis of cells size, shape and density. Two binarization
algorithms are going to be presented.
5.1. The adaptive binarization algorithm
The first binarization method – the adaptive algorithm – processes the image in
fragments. The size of the fragment – the square box – is about 150% of the cell size
and has to be chosen arbitrary. In every fragment maximum, minimum and average
of the brightness is calculated. Then binarization is performed for each square,
according to the selected threshold, which is equal to the calculated average or is
a value between minimum and maximum brightness of the fragment as a selected
percentage part of that distance. The last step is the median filter [6] done with the
3× 3 square element. The median filter removes small artifacts and smoothes cells.
The results of the binarization are presented in Fig. 4.
(a) (b) (c) (d)
Fig. 4. Adaptive binarization results. The source image (a) processed without the
median filter (b), with the median filter and the 55% (c) and 60% threshold
The presented binarization algorithm is not satisfying. The threshold selected
as an average brightness of the fragment gives very poor results. Calculating the
distance between the minimum and maximum value has also failed. The boundaries
are misplaced as well as cells are not clearly extracted. The final bi-level image
consists of significant artifacts that may have the negative influence on the further
calculation.
216
5.2. The filter based binarization algorithm
The algorithm begins with histogram equalization [6]: the darkest source pixels
becomes black and the brightest – white (Fig. 5). Then the high pass filter [6]
is applied to remove low frequency, just like the heterogamous illumination. The
threshold for this filter should be selected experimentally. Then binarization may
be performed globally with the threshold equal to 128. The median filter is applied
to reduce noise and artifacts. The final step is to remove elements that consist of
the small number of pixels. This may be done by counting the pixels in consistent
groups or by performing another median filter and calculating its intersection with
the image before that median filter operation.
The results of this algorithm are better than the adaptive filter, but still include
some artifacts. Their influence may also cause problems in further calculations.
(a)
(b)
Fig. 5. Histogram equalization results (b) of the example image (a) with corre-
sponding histograms
6. Results and conclusion
In this paper binarization of corneal endothelial images was discussed. It is the first
step in creating software to perform analysis of corneal endothelial and to determine
new parameters that will help in diagnosis and treatment effectiveness assessment.
217
Fig. 6. Filter based binarization algorithm results. The source image on the left
Two algorithms and their results were presented: the first adaptive algorithm, sug-
gested by non-uniform illumination, gives poor results, which is contrary to the
expectations. The second filter based algorithm provides better binarization. In
spite of its complicity, the filter approach may be a good solution for further anal-
ysis. Further work is to find new useful for ophthalmology parameters of corneal
endothelial images.
7. References
[1] Khan M.A., Khan M.K., Khan M.A.U., Lee S.; Endothelial Cell Image Enhancement
using Decimation-Free Directional Filter Banks, IEEE Asia Pacific Conference on
218
Circuits and Systems, 2006, pp. 884–887.
[2] Gronkowska J., Piórkowski A., Palacz O.; Ocena różnic stopni szarości fotografii śród-
błonka rogówki w relacji do innych parametrów tej warstwy oraz grubości rogówki u pac-
jentów przed i po operacji zaćmy, V Jubileuszowe Sympozjum Sekcji Informatyki Me-
dycznej, Polskie Towarzystwo Okulistyczne, 2003, pp. 40–42.
[3] Khan M.A.U., Khan M.K., Khan M.A., Ibrahim M.T.; Endothelial cell image en-
hancement using pyramid, Inform. Technol. J., 6, 2007, pp. 1057–1062.
[4] Mahzoun M.R., Okazaki K., Mitsumoto H., Kawai H., Sato Y., Tamura S., Kani K.;
Detection and Complement of Hexagonal Borders in Corneal Endothelial Cell Image,
Medical Imaging Technology, 14(1), 1996.
[5] Ruggeri A., Pajaro S.; Automatic recognition of cell layers in corneal confocal mi-
croscopy images, Computer Methods and Programs in Biomedicine, 2002, 68(1), pp.
25–35.
[6] Tadeusiewicz R., Korohoda P.; Komputerowa analiza i przetwarzanie obrazów,
Wydawnictwo Fundacji Postępu Telekomunikacji, Cracow 1997.
[7] Wojnar L., Kurzydłowski K.J., Szala J.; Praktyka analizy obrazu, Polskie Towarzystwo
Stereologiczne, Cracow 2002.
Received September 23, 2010


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