Statistical atlas-based descriptor for an early detection of optic disc abnormalities

Abstract

Fantin Girard, Conrad Kavalec, Farida Cheriet, "Statistical atlas-based descriptor for an early detection of optic disc abnormalities," J. Med. Imag. 5(1), 014006 (2018), doi: 10.1117/1.JMI.5.1.014006. Abstract. Optic disc (OD) appearance in fundus images is one of the clinical indicators considered in the assessment of retinal diseases such as glaucoma. The cup-to-disc ratio (CDR) is the most common clinical measurement used to characterize glaucoma. However, the CDR only evaluates the relative sizes of the cup and the OD via their diameters. We propose to construct an atlas-based shape descriptor (ASD) to statistically characterize the geometric deformations of the OD shape and of the blood vessels' configuration inside the OD region. A local representation of the OD region is proposed to construct a well-defined statistical atlas using nonlinear registration and statistical analysis of deformation fields. The shape descriptor is defined as being composed of several statistical measures from the atlas. Analysis of the average model and its principal modes of deformation are performed on a healthy population. The components of the ASD show a significant difference between pathological and healthy ODs. We show that the ASD is able to characterize healthy and glaucomatous OD regions. The deviation map extracted from the atlas can be used to assist clinicians in an early detection of deformation abnormalities in the OD region.

Comparison of transverse mode instability in Yb, Tm and Ho doped bent high-power fiber amplifiers

“…The restoration yields plausible results since small details are preserved after artifact removal, even in fine fundus structures such as blood vessels. This segmentation via inpainting approach may prove useful in abnormality detection [24,25], including microaneurysms [26]. Finally, to comply with modern open and reproducible research practices, the archived version of the code can be freely accessed and executed through Code Ocean [27].…”

Deep convolutional neural network-based patch classification for retinal nerve fiber layer defect detection in early glaucoma

“…1 The vision loss due to glaucoma is asymptomatic in early stages and its irreversible nature of blindness in later stages makes the early detection quite important to prevent any further damage. 2,3 In this regard, the degeneration of retinal nerve fiber layer (RNFL) provides the earliest evidence of glaucoma much before the actual visual field defect begins. 4 The detection of RNFL defect (RNFLD) in fundus images is a cost effective technique as compared to optical coherence tomography (OCT) or GDx, which require skilled technician to acquire the images.…”