Showing papers by "Eli Peli published in 1993"

Contrast sensitivity to patch stimuli: effects of spatial bandwidth and temporal presentation

Abstract: Models of the spatial response of human vision are important for applied work, but the available contrast sensitivity function (CSF) data vary widely due to the diverse spatiotemporal stimuli used over the years. To assist selection, this paper: (1) reports measurements of the effects on the CSF of varying the spatial and temporal windows of grating patches; (2) demonstrates that the widely discrepant CSFs from previous studies can be accounted for by using these results; and (3) discusses simple criteria for choosing CSFs for practical applications. CSFs were measured for several combinations of spatial and temporal waveforms, using the same subjects under otherwise identical conditions. The CSF was measured over the range of 0.5-10 c/deg using Gabor-type patches of 1.0-, 0.5-, 0.25-, and 0.125-octave spatial bandwidths using both abrupt and gradual temporal presentations. The results were compared with the CSF obtained with a fixed aperture (4 deg x 4 deg) grating pattern. Increasing the number of cycles resulted in increased sensitivity at intermediate frequencies, changing the CSF to a narrower bandpass shape. For each patch bandwidth, the gradual presentation CSF had a narrower spatial pass band than with the abrupt presentation. The relevance of the large differences in the CSFs obtained with different stimuli to our understanding of visual performance is discussed.

Enhancement of retinal images: a critical evaluation of the technology

Abstract: Evaluation of retinal images is essential to modern ophthalmic care. With the advent of image processing equipment, digital recording and processing of retinal images is starting to replace the standard film based fundus photography. The abilityto enhance images is cited as one of the major benefits of this expensive technology. This paper critically reviews the practices employed in the image enhancement literature. It is argued that the papers published to date have not presented convincing evidence regarding the diagnostic value of retinal image enhancement The more elaborate studies in radiology suggest, at best,modest diagnostic improvement with enhancement. The special difficulties associated with the demonstration of an improveddiagnosis in ophthalmic imaging are discussed in terms of the diagnostic task and the selection of study populations. 1. INTRODUCTION Retinal images are routinely used in ophthalmic practice for diagnosis and follow-up of eye diseases. Retinal photographsare of great value to ophthalmic practitioners for detecting subtle fundus changes that may occur over time. The quality of theretinal image is frequently reduced by the specular reflections from the cornea and lens, and light scatter from cataracts or otherocular media turbidity. This degradation of image quality may greatly impede visual inspection and automated image processingof the photographs.Various techniques to improve fundus visibility in the presence of media turbidity have been investigated. Commonly, theoptical system was designed to separate the illumination and imaging pathways at the patient's pupil (the Gullstrand principle) toreduce specular reflections and backscatter from the cataract (Leutwein and Littman, 1980). Modification of the photographictechnique and the use of various filters may be helpful (Fariza et aL, 1988). However, even with the best imaging techniques,cataracts may degrade the image significantly. In the presence of moderate turbidity, the general appearance of the retina is clear,but very fine details, such as the retinal nerve fiber layer (RNFL) or small arteries in fluorescein angiography, are difficult toevaluate. In recent years digital image-enhancement techniques have been used in an effort to improve the visibility of fundusdetails from photographs taken through relatively clear media (Gilchrist, 1987; Peli et aL, 1986; Shakespear, 1987; Winstanley,1989) and through cataracts (Peli and Peli, 1989; Peli and Schwartz, 1987).Image enhancement algorithms are usually classified either as noise removal/restoration methods or as contrast enhancementtechniques, including both spatial filtering and gray level modification methods (Lim, 1984; T. Peli and Lim, 1982). In somecases a combination of both types of enhancement are applied. Image enhancement can be used as a me-processing stage for au-tomated computerized image analysis such as the automated detection of lesions in images (Lai et aL, 1989). For the purposesof this paper, however, enhancement is defined more narrowly as the processing of images designed to improve human observers'performance in evaluating those images.In this paper the value of image enhancement is examined critically. A number of practices used by many in the field, in-cluding the author, are questioned, and attempts are made to establish a more stringent requirement for the presentation of imageenhancement techniques. Specifically, I will argue that the results of new enhancement algorithms should be presented in a waythat clearly demonstrate the value-added benefit of the new algorithm over commonly available simple and fast enhancementmethods; the use of enhancement and restoration algorithms should be demonstrated with real degraded images rather than with