Abstract
Imaging and evaluation of the optic nerve head (ONH) plays an essential part in the detection and clinical management of glaucoma. The morphological characteristics of ONHs vary greatly from person to person and this variability means it is difficult to quantify them in a standardized way. We developed and evaluated a feature extraction approach using shift-invariant wavelet packet and kernel principal component analysis to quantify the shape features in ONH images acquired by scanning laser ophthalmoscopy (Heidelberg Retina Tomograph [HRT]). The methods were developed and tested on 1996 eyes from three different clinical centers. A shape abnormality score (SAS) was developed from extracted features using a Gaussian process to identify glaucomatous abnormality. SAS can be used as a diagnostic index to quantify the overall likelihood of ONH abnormality. Maps showing areas of likely abnormality within the ONH were also derived. Diagnostic performance of the technique, as estimated by ROC analysis, was significantly better than the classification tools currently used in the HRT software - the technique offers the additional advantage of working with all images and is fully automated.
Highlights
Glaucoma is a chronic, slowly progressive optic neuropathy that can lead to irreversible sight loss
We have presented a new method for detecting abnormality in optic nerve head images acquired using scanning laser tomography (HRT)
In contrast to the methods used on Heidelberg Retina Tomograph (HRT) software the technique derives objective morphological characteristics of optic nerve head (ONH) that can be used to derive a probability of abnormality (SAS)
Summary
Slowly progressive optic neuropathy that can lead to irreversible sight loss. By comparing the measured neuroretinal rim area to normative limits, globally and in six separate sectors (Fig. 1), the MRA classifies an ONH as “within normal limits”, “borderline”, or “outside normal limits”. This method corrects for the well-established relationship between ONH size and rim area: larger ONHs tend to have larger rim areas [11,12,13]. In order to quantify the size of ONH and rim area, the MRA requires manual delineation (contouring) of the ONH by connecting points on ONH margin that are subjectively selected by the operator/clinician This process inevitably introduces measurement variability, which in turn adversely affects the diagnostic performance of MRA [17]. Estimates of diagnostic performance of the technique were compared with MRA and GPS
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