Sajib Saha

Sajib Kumar Saha, Basura Fernando, Jorge Cuadros et al.

Purpose To develop a computer based method for the automated assessment of image quality in the context of diabetic retinopathy (DR) to guide the photographer. Methods A deep learning framework was trained to grade the images automatically. A large representative set of 7000 color fundus images were used for the experiment which were obtained from the EyePACS that were made available by the California Healthcare Foundation. Three retinal image analysis experts were employed to categorize these images into Accept and Reject classes based on the precise definition of image quality in the context of DR. A deep learning framework was trained using 3428 images. Results A total of 3572 images were used for the evaluation of the proposed method. The method shows an accuracy of 100% to successfully categorise Accept and Reject images. Conclusion Image quality is an essential prerequisite for the grading of DR. In this paper we have proposed a deep learning based automated image quality assessment method in the context of DR. The method can be easily incorporated with the fundus image capturing system and thus can guide the photographer whether a recapture is necessary or not.

Basura Fernando, Sezer Karaoglu, Sajib Kumar Saha

This paper presents a novel multi scale gradient and a corner point based shape descriptors. The novel multi scale gradient based shape descriptor is combined with generic Fourier descriptors to extract contour and region based shape information. Shape information based object class detection and classification technique with a random forest classifier has been optimized. Proposed integrated descriptor in this paper is robust to rotation, scale, translation, affine deformations, noisy contours and noisy shapes. The new corner point based interpolated shape descriptor has been exploited for fast object detection and classification with higher accuracy.

Sajib Saha, Frank de Hoog, Ya. I. Nesterets et al.

Purpose: Localizing the sources of electrical activity from electroencephalographic (EEG) data has gained considerable attention over the last few years. In this paper, we propose an innovative source localization method for EEG, based on Sparse Bayesian Learning (SBL). Methods: To better specify the sparsity profile and to ensure efficient source localization, the proposed approach considers grouping of the electrical current dipoles inside human brain. SBL is used to solve the localization problem in addition with imposed constraint that the electric current dipoles associated with the brain activity are isotropic. Results: Numerical experiments are conducted on a realistic head model that is obtained by segmentation of MRI images of the head and includes four major components, namely the scalp, the skull, the cerebrospinal fluid (CSF) and the brain, with appropriate relative conductivity values. The results demonstrate that the isotropy constraint significantly improves the performance of SBL. In a noiseless environment, the proposed method was 1 found to accurately (with accuracy of >75%) locate up to 6 simultaneously active sources, whereas for SBL without the isotropy constraint, the accuracy of finding just 3 simultaneously active sources was <75%. Conclusions: Compared to the state-of-the-art algorithms, the proposed method is potentially more consistent in specifying the sparsity profile of human brain activity and is able to produce better source localization for EEG.