Computational Ophthalmology

OPT

Advances in multimodal imaging have revolutionized diagnostic and treatment monitoring in ophthalmic practice. In multimodal ophthalmic imaging, geometric deformations are inevitable and they contain inherent deformations arising from heterogeneity in the optical characteristics of imaging devices and patient related factors. The registration of ophthalmic images under such conditions is challenging. We propose a novel technique that overcomes these challenges, using Laplacian feature, Hessian affine feature space and phase correlation, to register blue autofluorescence, near-infrared reflectance and color fundus photographs of the ocular posterior pole with high accuracy. Our validation analysis – that used current feature detection and extraction techniques (speed-up robust features (SURF), a concept of wind approach (KAZE), and fast retina keypoint (FREAK)), and quantitative measures (Sørensen–Dice coefficient, Jaccard index, and Kullback–Leibler divergence scores) – showed that our approach has significant merit in registering multimodal images when compared with a mix-and-match SURF-KAZE-FREAK benchmark approach. Similarly, our evaluation analysis that used a state-of-the-art qualitative measure – the mean registration error – showed that the proposed approach is significantly better than the mix-and-match SURF-KAZE-FREAK benchmark approach, as well as a cutting edge image registration technique – Linear Stack Alignment with SIFT (scale-invariant feature transform) – in registering multimodal ophthalmic images. 

1. Raphael Lejoyeux, Raphael Atia, Kiran Vupparaboina, Mohamed Ibrahim, Shan Suthaharan, Jose-Alain Sahel, Kunal Dansingani, and Jay Chhablani. "En-face analysis of short posterior ciliary arteries crossing the sclera to choroid using wide-field swept-source optical coherence tomography," Scientific Reports 11, 8732 (2021). pp. 1-10.

2. Shan Suthaharan, Ethan A. Rossi, Valerie Snyder, Jay Chhablani, Raphael Lejoyeux, José-Alain Sahel, Kunal K. Dansingani, Laplacian feature detection and feature alignment for multimodal ophthalmic image registration using phase correlation and Hessian affine feature space. Signal Process. 177: 107733 (2020).

3. Shan Suthaharan, Gunjan Chhablani, Kiran K. Vupparaboina, Jose-Alain Sahel, Kunal K. Dansingani, Jay Chhablani. "An automated choroid segmentation approach using transfer learning and encoder-decoder networks," Investigative Ophthalmology & Visual Science June 2021, Vol.62, pp. 2158-2158.

4.  Kiran K. Vupparaboina, Amrish Selvam, Shan Suthaharan, Mohammed N. Ibrahim, Soumya Jana, Jose-Alain Sahel, Kunal Dansingani, Jay Chhablani. "Automated Choroid Layer Segmentation based on Wide-field SS-OCT Images using Deep Residual Encoder-decoder Architecture," Investigative Ophthalmology & Visual Science June 2021, Vol.62, pp. 2162-2162.

5. Shan Suthaharan, Ethan Rossi, Valerie Snyder, Raphael Lejoyeux, Jay Chhablani, Jose Sahel, Kunal Dansingani. 2020. Multimodal ophthalmic image registration using Hessian feature spaces, Investigative Ophthalmology & Visual Science June 2020, Vol.61, pp. 1149-1149.

6. E. Gofas, M. Zhang, Y. Rei, V. Snyder, K. Vienola, S. Suthaharan, E. Rossi. 2020. A refined detection scheme and image processing pipeline for multioffset adaptive optics scanning light ophthalmoscopy improves the contrast of retinal ganglion cell layer neurons in humans, Investigative Ophthalmology & Visual Science June 2020, Vol.61, pp. 205-205.