Combining Central and Peripheral Videokeratoscope Maps to Investigate Total Corneal Topography

Franklin, Ross; Morelande, Mark R.; Iskander, D. Robert; Collins, Michael J.; Davis, Brett A.

doi:10.1097/01.icl.0000172118.24284.f6

Cited by 19 publications

(28 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In that case, the mean dimension of 21 combined maps is 11.45 mm (standard deviation: 0.29 mm) horizontally and 11.33 mm (standard deviation: 0.40 mm) vertically. This result is comparable with the results reported by Franklin et al 6 One combined map for this example is displayed in Figure 7 having an increase in area of approximately 65% compared to the original central map.…”

Section: Resultssupporting

confidence: 93%

See 1 more Smart Citation

Corneal topography matching by iterative registration

Wang

Elsheikh

Davey

et al. 2014

Proc Inst Mech Eng H

View full text Add to dashboard Cite

Videokeratography is used for the measurement of corneal topography in overlapping portions (or maps) which must later be joined together to form the overall topography of the cornea. The separate portions are measured from different viewpoints and therefore must be brought together by registration of measurement points in the regions of overlap. The central map is generally the most accurate, but all maps are measured with uncertainty that increases towards the periphery. It becomes the reference (or static) map, and the peripheral (or dynamic) maps must then be transformed by rotation and translation so that the overlapping portions are matched. The process known as registration, of determining the necessary transformation, is a well-understood procedure in image analysis and has been applied in several areas of science and engineering. In this article, direct search optimisation using the Nelder-Mead algorithm and several variants of the iterative closest/corresponding point routine are explained and applied to simulated and real clinical data. The measurement points on the static and dynamic maps are generally different so that it becomes necessary to interpolate, which is done using a truncated series of Zernike polynomials. The point-to-plane iterative closest/corresponding point variant has the advantage of releasing certain optimisation constraints that lead to persistent registration and alignment errors when other approaches are used. The point-to-plane iterative closest/corresponding point routine is found to be robust to measurement noise, insensitive to starting values of the transformation parameters and produces high-quality results when using real clinical data.

show abstract

Section: Resultssupporting

confidence: 93%

“…These maps are generated based on the same conic surface in Franklin et al 6 (radius r 0 = 7:2 mm, asphericity Q = À 0:51). To prevent the test set from being rotationally symmetric, the conic surfaces produced have been elongated in the X-direction by a factor of ffiffiffiffiffiffiffiffiffi 1:05 p .…”

Section: Data Setsmentioning

confidence: 99%

Corneal topography matching by iterative registration

Wang

Elsheikh

Davey

et al. 2014

Proc Inst Mech Eng H

View full text Add to dashboard Cite

show abstract

“…Current Placido-based topography systems are limited in their ability to capture the corneal surface to a diameter of about 9 mm horizontally and 7.5 mm vertically. By developing a software algorithm that allowed capture of central and peripheral corneal topography, Franklin et al 36 were successful in representing an average of 11.3 mm of the cornea horizontally and 10.3 mm vertically. However, the procedure was tedious and computationally intensive.…”

Section: Figure 13mentioning

confidence: 99%

Variation in Normal Corneal Shape and the Influence of Eyelid Morphometry

Maseedupally¹,

Gifford

Swarbrick

2015

Optometry and Vision Science

View full text Add to dashboard Cite

show abstract

“…To illustrate the effectiveness of the proposed generalised conic function we consider data of an extended corneal surface recently reported by Franklin et al. (2006).…”

Section: Fitting Generalised Conicsmentioning

confidence: 99%

Approximating ocular surfaces by generalised conic curves

Kasprzak

Iskander

2006

Ophthalmic Physiologic Optic

View full text Add to dashboard Cite

Most of the optical models of the human eye use simple conic functions to represent its individual components such as corneal surfaces and the surfaces of the crystalline lens. Although a conic function provides an acceptable approximation for most anatomical eye surfaces, it also leads to a simple optical analysis of the whole eye system. To fill the gap between the classical use of conic surfaces and the use of more sophisticated functions that often invoke numerically expensive procedures in the optical analysis, a functional generalisation of the conic curve is proposed. A detailed derivation of the generalised conic function is presented for a two-dimensional (meridional) case. This is followed by a three-dimensional surface approximation. Examples are given in which the superiority of the proposed approximation over a classical conic function as well as the hyperbolic cosine approximation is evident. In particular, it is shown that for an average total corneal profile, the proposed generalisation results in a residual height error that is of an order smaller than those achieved with the conic and hyperbolic cosine approximations. In conclusion, the proposed generalised conic function can be a useful tool in eye modelling, where the simplicity of expression is often desirable.

show abstract

Combining Central and Peripheral Videokeratoscope Maps to Investigate Total Corneal Topography

Abstract: The topography of the entire cornea can be represented by combining multiple measurements from a Placido videokeratoscope. Conic fits based on central topography data are a poor representation of the total corneal shape.

Cited by 19 publications

References 16 publications

Corneal topography matching by iterative registration

Corneal topography matching by iterative registration

Variation in Normal Corneal Shape and the Influence of Eyelid Morphometry

Approximating ocular surfaces by generalised conic curves

Contact Info

Product

Resources

About