Mr. Janarthanam Jothi Balaji Profile

Janarthanam Jothi Balaji

at Sankara Nethralaya, Medical Research Foundation

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Author

Publications (17)

Proceedings Article | 13 March 2024 Poster + Paper

A new automated method for grading retinal tessellation in myopic eyes using fundus images

Shriharshinii Ragotham, J. Jothi Balaji, Rajiv Raman, Vasudevan Lakshminarayanan

Proceedings Volume 12824, 128240R (2024) https://doi.org/10.1117/12.3004783

KEYWORDS: Image processing, Eye, Tunable filters, Retina, RGB color model, Linear filtering, Image filtering, Histograms, Blood vessels, Windows, Retinal diseases

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Proceedings Article | 4 October 2023 Paper

OW-SLR: overlapping windows on semi-local region for image super-resolution

Rishav Bhardwaj, Janarthanam Jothi Balaji, Vasudevan Lakshminarayanan

Proceedings Volume 12674, 1267416 (2023) https://doi.org/10.1117/12.2680629

KEYWORDS: RGB color model, Windows, Lawrencium, Feature extraction, Super resolution, Image resolution, Image processing, Image enhancement, 3D image reconstruction, Optometry

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Proceedings Article | 15 March 2023 Poster + Paper

Measurement of sub-foveal choroidal thickness from optical low coherence reflectometry biometry: a new method

Sivvani Muthusamy, Janarthanam Jothi Balaji, Anusha Paritala, Vasudevan Lakshminarayanan

Proceedings Volume 12360, 123600P (2023) https://doi.org/10.1117/12.2651145

KEYWORDS: Optical coherence tomography, Reflectometry, Optical coherence, Image processing, Eye, Biometrics, Vitreous, Ophthalmology, Optometry, Vision

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Proceedings Article | 3 October 2022 Poster + Paper

Quantum machine learning prediction model for retinal conditions: performance analysis

Manoj Naick, Mohammed Abdul Rasheed, Varadharajan Jayakumar, Janarthanam Jothi Balaji, Vasudevan Lakshminarayanan

Proceedings Volume 12227, 122270V (2022) https://doi.org/10.1117/12.2633672

KEYWORDS: Machine learning, Quantum computing, Optical coherence tomography, Image classification, Image processing

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Proceedings Article | 30 June 2022 Open Access

Should optics be taught as continuing medical education to optometrists?

Janarthanam Jothi Balaji, Vasudevan Lakshminarayanan

Proceedings Volume 12297, 1229710 (2022) https://doi.org/10.1117/12.2635537

KEYWORDS: Biomedical optics, Beryllium, Photonics, Geometrical optics, Visual optics, Neodymium, Eye, Education and training, Active optics, Scattering

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Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Annotation and segmentation of diabetic retinopathy lesions: an explainable AI application

Hoda Kheradfallah, Janarthanam Jothi Balaji, Varadharajan Jayakumar, Mohammed Abdul Rasheed, Vasudevan Lakshminarayanan

Proceedings Volume 12033, 120331X (2022) https://doi.org/10.1117/12.2612576

KEYWORDS: Image segmentation, Performance modeling, Databases, Artificial intelligence, Data modeling, Visual process modeling, Statistical modeling, Visualization, Vitreous, Sensors

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Proceedings Article | 4 March 2022 Poster + Presentation + Paper

Is there a relationship between optic disc ovality index and positional changes in myopic eyes?

S. Rohit, Janarthanam Jothi Balaji, Vasudevan Lakshminarayanan

Proceedings Volume 11941, 119410M (2022) https://doi.org/10.1117/12.2610714

KEYWORDS: Optical coherence tomography, Image processing, Eye, Image segmentation, Visualization, Optic nerve

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Proceedings Article | 4 March 2022 Poster + Presentation + Paper

What is the role of magnification correction in the measurement of macular microvascular dimensions in emmetropic eyes?

V. Viekash, Janarthanam Jothi Balaji, Vasudevan Lakshminarayanan

Proceedings Volume 11941, 119410L (2022) https://doi.org/10.1117/12.2610713

KEYWORDS: Visualization, Retina, Eye, Optical coherence tomography, Angiography, Statistical analysis, Image processing, Vascular diseases

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Proceedings Article | 1 August 2021 Poster + Paper

Determination of foveal avascular zone parameters using a new location-aware deep-learning method

Zahra Sobhaninia, Hajar Danesh, Rahele Kafieh, J. Jothi Balaji, Vasudevan Lakshminarayanan

Proceedings Volume 11843, 1184311 (2021) https://doi.org/10.1117/12.2594152

KEYWORDS: Image segmentation, Optical coherence tomography, Blood vessels, Image processing, Feature extraction, Ophthalmology, Retina

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The Foveal Avascular Zone (FAZ) is of clinical importance since the retinal vascular arrangement around the fovea changes with retinal vascular diseases and in high myopic eyes. Therefore, it is important to segment and quantify the FAZs accurately. Using a novel location-aware deep learning method the FAZ boundary was segmented in en-face optical coherence tomography angiography (OCTA) images. The FAZ dimensions were compared the parameters determined using four methods: (1) device in-built software (Cirrus 5000 Angioplex), (2) manual segmentation using Image J software by an experienced clinician, and (4) the new method (new location-aware deep-learning method). The parameters were measured from OCTA data from healthy subjects (n=34) and myopic patients (n=66). For this purpose, FAZ location was manually delineated in en-face OCTA images of dimensions 420x420 pixels corresponding to 6mm x 6mm. A modified UNet segmentation with an additional channel from a Gaussian distribution around the likely location of the FAZ was designed and trained using 100 manually segmented OCTA images. The predicted FAZ and the related parameters were then obtained using a test dataset consisting of 100 images. For analysis, two strategies were applied. The segmentation of FAZ was compared using the Dice coefficient and Structural Similarity Index (SSIM) to determine the effectiveness of the proposed deep learning method when compared to the other three methods. Furthermore, to provide deeper insight, a set of FAZ dimensions namely area, perimeter, circularity index, eccentricity, perimeter, major axis, minor axis, inner circle radius, circumcircle radius, the maximum and minimum boundary dimensions, and orientation of major axis were compared between the 3 methods. Finally, vessel-related parameters including tortuosity, vessel diameter index (VDI) and vessel avascular density (VAD) were calculated and compared. The high myopic eyes exhibited a narrowing the FAZ area and perimeter. The currently developed algorithm does not correct for axial length variations. This analysis should be extended with a larger number of images in each group of myopia as well as correcting for axial length variations.

Proceedings Article | 1 August 2021 Poster + Paper

Development and validation of a novel automated method for quantification of choroidal thickness in age-related macular degeneration

A. Smitha, P. Jidesh, J. Jothi Balaji, Vasudevan Lakshminarayanan

Proceedings Volume 11842, 118422E (2021) https://doi.org/10.1117/12.2594557

KEYWORDS: Optical coherence tomography, Image segmentation, Retina, Image enhancement, Visualization, Image processing algorithms and systems, Image processing, Ophthalmology

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Proceedings Article | 1 August 2021 Poster + Paper

A deep learning approach to pupillometry

Aditya Chandra Mandal, Abhijeet Phatak, J. Jothi Balaji, Vasudevan Lakshminarayanan

Proceedings Volume 11843, 1184312 (2021) https://doi.org/10.1117/12.2594315

KEYWORDS: RGB color model, Data modeling, Image segmentation, Infrared imaging, Eye, Cameras, Neural networks, Iris recognition, Machine vision

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Proceedings Article | 5 March 2021 Poster + Paper

Is there a relationship between retinal blood vessel characteristics and myopia?

J. Jothi Balaji, Arnav Chavan, Gowreesh Mago, Vasudevan Lakshminarayanan

Proceedings Volume 11623, 116231Z (2021) https://doi.org/10.1117/12.2576987

KEYWORDS: Blood vessels, Distance measurement, Image segmentation, Image processing, Optical coherence tomography, Image classification, Eye, Angiography

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Proceedings Article | 5 March 2021 Poster + Paper

Is the optic disc tilt angle different in myopia?

Nitish Gudapati, S. Swedha, J. Jothi Balaji, Vasudevan Lakshminarayanan

Proceedings Volume 11623, 1162328 (2021) https://doi.org/10.1117/12.2578657

KEYWORDS: Optical discs, Optical coherence tomography, Image segmentation, Eye, Optic nerve, Macula, Image processing, Image classification, Head

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Optic disc tilt (ODT), peripapillary atrophy (PPA), and abnormally large or small optic discs are the earliest known changes in myopic eyes and may precede the development of pathological myopia. Increasing ODT and distance between the macula and optic nerve head have been reported as being associated with progressive myopia. Therefore, it is important to segment and quantify the ODT accurately. Using a newly developed automated image processing method we measured the ODT in both myopes and emmetropes. We determined the ODT from myopic eyes (n= 90) and compared the results with emmetropia (n=14). All 104 optical coherence tomography (OCT) images had dimensions of 200x200 pixels corresponding to 6mm x 6mm. The myopic OCT images were labeled based on a severity scale based on the spherical error (SE) as low (-0.5 to -3.00 D SE), moderate (-3.12 to -6.00 D SE), high (-6.12 to -9.00 D SE), and very high (worse than -9.00 D SE) using standard myopia classifications. Each OCT image was segmented by a clinician (CEM) and by the newly proposed method (NAM). The NAM used 8-bit grayscale OCT images which was preprocessed by applying Gaussian blur and Contrast Limited Adaptive Histogram Equalization based thresholding to remove noise and locate regions of interest. Then the images were split into two halves to fit straight lines separately. Morphological erosion and dilation were performed on the images to remove artifacts. They were tested with three combinations of erosion and dilation iterations. Univariate linear regression Lines were fit to trace the white band on each half and angle between the lines was determined. Both the methods showed higher horizontal ODT than vertical. The mean ± SD horizontal ODT (in degrees) in the myopic eye was 18.47 ± 7.67 and 15.84± 6.61 by the NAM and CEM methods. The vertical ODT in the myopic eyes (in degrees) was 16.32 ± 7.10, and 14.52 ± 7.05 by the NAM and CEM methods respectively. However, the NAM showed a maximum difference (2.26 ± 5.68) between horizontal and vertical ODT. The study results show that the ODT in very high myopic eyes (26.33±8.99)is significantly different (p<0.05) when compared to emmetropic eyes (19.47±3.99).

Proceedings Article | 5 March 2021 Poster + Paper

A new method for quantification of retinal blood vessel characteristics

Arnav Chavan, Gowreesh Mago, J. Jothi Balaji, Vasudevan Lakshminarayanan

Proceedings Volume 11623, 1162320 (2021) https://doi.org/10.1117/12.2576984

KEYWORDS: Blood vessels, Distance measurement, Target detection, Optical coherence tomography, Mathematical modeling, Image segmentation, Image analysis, Angiography

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Proceedings Article | 21 August 2020 Poster + Presentation + Paper

The Foveal Avascular Zone Image Database (FAZID)

Arpit Agarwal, Jothi Balaji J., Rajiv Raman, Vasudevan Lakshminarayanan

Proceedings Volume 11510, 1151027 (2020) https://doi.org/10.1117/12.2567580

KEYWORDS: Image segmentation, Databases, Optical coherence tomography, Retina, Ophthalmology, Medical image processing

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Proceedings Article | 19 February 2020 Paper

A new technique for estimating the foveal avascular zone dimensions

Arpit Agarwal, J. Jothi Balaji, Vasudevan Lakshminarayanan

Proceedings Volume 11218, 112181R (2020) https://doi.org/10.1117/12.2543906

KEYWORDS: Image segmentation, Image processing, Image filtering, Eye, Gaussian filters, Edge detection, Optical coherence tomography, Angiography, Diagnostics, Retina

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Proceedings Article | 19 February 2020 Paper

Comparison of foveal avascular zone in diabetic retinopathy, high myopia, and normal fundus images

Jothi Balaji, Arpit Agarwal, Rajiv Raman, Vasudevan Lakshminarayanan

Proceedings Volume 11218, 112181O (2020) https://doi.org/10.1117/12.2544817

KEYWORDS: Angiography, Optical coherence tomography, Image segmentation, Capillaries, Retina, Image processing, Eye, Visualization

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Showing 5 of 17 publications

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