Journal of Current Ophthalmology

LETTER TO EDITOR
Year
: 2022  |  Volume : 34  |  Issue : 2  |  Page : 272-

Reply to Letter to Editor: Water-Drinking Test in Central Serous Chorioretinopathy


Niroj Kumar Sahoo1, Jay Chhablani2,  
1 Anant Bajaj Retina Institute, L V Prasad Eye Institute, Kode Venkatadri Chowdary Campus, Vijayawada, Andhra Pradesh, India
2 UPMC Eye Centre, University of Pittsburgh, Pittsburgh, PA, USA

Correspondence Address:
Jay Chhablani
UPMC Eye Center, University of Pittsburgh, 203 Lothrop Street, Pittsburgh, PA 15213
USA




How to cite this article:
Sahoo NK, Chhablani J. Reply to Letter to Editor: Water-Drinking Test in Central Serous Chorioretinopathy.J Curr Ophthalmol 2022;34:272-272


How to cite this URL:
Sahoo NK, Chhablani J. Reply to Letter to Editor: Water-Drinking Test in Central Serous Chorioretinopathy. J Curr Ophthalmol [serial online] 2022 [cited 2022 Oct 6 ];34:272-272
Available from: http://www.jcurrophthalmol.org/text.asp?2022/34/2/272/352473


Full Text



Dear Sir,

We thank the authors for their comments regarding our article entitled, “Water-Drinking Test in Central Serous Chorioretinopathy”.[1]

The issue of “blooming effect” is one of the major concerns in the analyses of images having several shades of the grayscale. Moreover, as pointed out by the authors, the issue can appear alike in ultrasound and optical coherence tomography B-scan images. However, the choroidal vascularity index calculation has undergone many modifications since its inception, from segmenting and binarizing it manually using ImageJ software (ImageJ version 1.53, National Institutes of Health, Bethesda, Maryland, USA) to using automated algorithms,[3] which is faster and more accurate.[4] The automated process involves several steps that refine the image before the actual process of binarization. This includes a preprocessing of the image by denoising using block-matching and three-dimensional filtering, followed by adaptive histogram equalization to improve image contrast.[3] The next step is usually an exponential and nonlinear enhancement, which ensures uniform distribution of pixel intensities among stroma and vessel regions.[3] These steps minimize the blooming effect to some extent. Furthermore, the application of an automated algorithm allows for the implementation of similar steps for each image every time the algorithm is run. This makes the amplification or error due to the blooming effect similar for each image and makes pre- and post-measurements more comparable.

While we understand the authors' concerns regarding the shortcomings of the overall process of binarization in biological tissues with small dimensions, the index was devised to understand the choroidal dynamics better and should be taken as a steppingstone in the evolution of choroidal imaging, rather than a definitive final checkpoint.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

References

1Goud A, Sahoo NK, Rasheed MA, Singh SR, Ankireddy S, Vupparaboina KK, et al. Water-drinking test in central serous chorioretinopathy. J Curr Ophthalmol 2021;33:62-7.
2Sonoda S, Sakamoto T, Yamashita T, Shirasawa M, Uchino E, Terasaki H, et al. Choroidal structure in normal eyes and after photodynamic therapy determined by binarization of optical coherence tomographic images. Invest Ophthalmol Vis Sci 2014;55:3893-9.
3Agrawal R, Wei X, Goud A, Vupparaboina KK, Jana S, Chhablani J. Influence of scanning area on choroidal vascularity index measurement using optical coherence tomography. Acta Ophthalmol 2017;95:e770-5.
4Rasheed MA, Sahoo NK, Goud A, Vupparaboina KK, Chhablani J. Qualitative comparison of choroidal vascularity measurement algorithms. Indian J Ophthalmol 2018;66:1785-9.