A diagnostic information based framework for super-resolution and quality assessment of retinal OCT images.

High-resolution (HR) retinal optical coherence tomography (OCT) images are preferred by the ophthalmologists to diagnose retinal diseases. These images can be obtained by dense scanning of the target retinal region during acquisition. However, a dense scanning increases the image acquisition time and introduces motion artefacts, which corrupt diagnostic information. Therefore, researchers have a growing interest in developing image processing techniques to recover HR images from low-resolution (LR) OCT images. In this paper, we present an automated super-resolution (SR) scheme using diagnostic information weighted sparse representation framework to reconstruct HR images from LR OCT images. The proposed method performs fast and reliable reconstruction of the LR images. We also propose a 2D- variational mode decomposition (VMD) based OCT diagnostic distortion measure (QOCT) to quantify diagnostic distortion in the reconstructed OCT images. The SR method is evaluated on clinical grade OCT images with the proposed diagnostic distortion measure along with the conventional non-diagnostic measures like the contrast to noise ratio (CNR), the equivalent number of looks (ENL) and the peak signal to noise ratio (PSNR). The results show an average CNR of 4.07, ENL of 58.96 and PSNR of 27.72 dB. An average score of 1.53 is obtained using the proposed diagnostic distortion measure. Experimental results quantify that the proposed QOCT metric can effectively capture diagnostic distortion.

A novel diagnostic information based framework for super-resolution of retinal fundus images.

Advancements in tele-medicine have led to the development of portable and cheap hand-held retinal imaging devices. However, the images obtained from these devices have low resolution (LR) and poor quality that may not be suitable for retinal disease diagnosis. Therefore, this paper proposes a novel framework for the super-resolution (SR) of the LR fundus images. The method takes into consideration the diagnostic information in the fundus images during the SR process. In this work, SR is performed on the zone of interest of the fundus images. Clinical information of the selected zone is captured using the Shannon entropy, the contrast sensitivity index (CSI), the multi-resolution (MR) intra-band energy and the MR inter-band eigen features. The support vector machine (SVM) classifier is used to decide the clinical significance of the zone. Highly accurate learning based SR method or the bicubic interpolation is applied to the selected zone based on the classification output. The method is tested on the Standard Diabetic Retinopathy Database Calibration level 1 (DIARETDB1) and the Digital Retinal Images for Vessel Extraction (DRIVE) databases. Classification accuracy of 85.22% and 85.77% is achieved for the DIARETDB1 and DRIVE databases respectively. The SR performance of the algorithm is quantified in terms of peak signal to noise ratio (PSNR), structural similarity index measure (SSIM) and computational time. The proposed diagnostic information based SR achieves computational time efficiency without compromising with the high resolution (HR) reconstruction accuracy of the fundus image zones.