Page 68 - Computational Retinal Image Analysis

CHAPTER

Retinal image preprocessing,

enhancement, and registration 4

a
a
a,b
Carlos Hernandez-Matas , Antonis A. Argyros , Xenophon Zabulis
a Institute of Computer Science, Foundation for Research and Technology—Hellas (FORTH),
Heraklion, Greece
b Computer Science Department, University of Crete, Heraklion, Greece

1 Introduction
The first fundus images were acquired after the invention of the ophthalmoscope.
The concept of storing and analyzing retinal images for diagnostic purposes exists
ever since. The first work on retinal image processing was based on analog images
and regarded the detection of vessels in fundus images with fluorescein [1]. The
fluorescent agent enhances the appearance of vessels in the image, facilitating their
detection and measurement by the medical professional or the computer. However,
fluorescein angiography is an invasive and time-consuming procedure and is
associated with the cost of the fluorescent agent and its administration.
Digital imaging and digital image processing have proliferated the use
of retinal image analysis in screening and diagnosis. The ability to accurately
analyze fundus images has promoted the use of noninvasive, fundus imaging
in these domains. Moreover, the invention of new imaging modalities, such as
optical coherence tomography (OCT) and scanning laser ophthalmoscopy (SLO),
has broadened the scope and applications of retinal image processing. This review
regards both fundus imaging, as implemented by fundus photography and SLO
and OCT imaging.
Retinal image analysis supports pertinent diagnostic procedures. A number of
symptoms and diseases are diagnosed through observation of the human retina.
Retinal image analysis is useful not only in the diagnosis of ophthalmic diseases,
but also in that of systemic chronic diseases. Hypertension and diabetes are two
important examples of such diseases that affect small vessels and microcirculation
and which are noninvasively screened and assessed through the contribution of retinal
image analysis [2, 3]. In this context, two widely employed tasks are the detection
and measurement of anatomical features and properties, such as lesion detection
and measurement of vessel diameters. Achieving these tasks typically includes a
preprocessing stage, tuned according to the measured features and the method of
image analysis. This preprocessing stage usually regards the normalization of image

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