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Glaucoma is a leading cause of blindness with permanent damage to optic nerve head. ARGALI is an automated computer-aided diagnosis system designed for glaucoma detection via optic cup-to-disc ratio assessment. It employs several methods to determine the optic cup and disc from retinal images. Optic disc detection and segmentation works have been widely reported with high success rate. However, the...
We propose a method for improving the accuracy of the optic cup detected from the ARGALI system. This method makes use of key points from the branching points of large vessels, the analysis of intensity variation and kinks from small vessels to obtain an enhanced optic cup. Measures used to assess the detection of the optic cup showed an 11% and 40% improvement in the mean average overlap and relative...
This paper proposes a method to detect the macula in the retinal fundus image automatically. The method makes use of the optic disc height obtained from the ARGALI to define the region of interest. Regions of dark spots are then detected by finding the coordinates with the lowest pixel intensity and determining the average pixel neighbourhood intensities. These regions are ranked to determine the...
This paper presents a photometric restoration technique that automatically corrects shading within retinal images taken with a fundus camera. The proposed technique is based on the observation that the background of retinal images usually shows flat reflectance variations due to its high similarity in color and texture. It estimates shading through an iterative polynomial interpolation procedure that...
Glaucoma is a leading cause of permanent blindness. ARGALI, an automated system for glaucoma detection, employs several methods for segmenting the optic cup and disc from retinal images, combined using a fusion network, to determine the cup to disc ratio (CDR), an important clinical indicator of glaucoma. This paper discusses the use of SVM as an alternative fusion strategy in ARGALI, and evaluates...
Automatic classification of working areas in peripheral blood smears can provide objective and reproducible quality control for the evaluation of smears and smear maker devices. However, it has drawn little research attention. In this paper we study this topic using image analysis and statistical pattern recognition methods. We employ generic features without requiring the extraction of individual...
Glaucoma is a leading cause of permanent blindness. However, disease progression can be limited if detected early. The optic cup-to-disc ratio (CDR) is one of the main clinical indicators of glaucoma, and is currently determined manually, limiting its potential in mass screening. In this paper, we propose an automatic CDR determination method using a variational level-set approach to segment the optic...
The ratio of the optic cup to disc (CDR) in retinal fundus images is one of the principal physiological characteristics in the diagnosis of glaucoma. Currently the CDR is manually determined which can be subjective and limits its use in mass screening. To automatically extract the disc, a variational level set method is proposed in this paper. For the cup, two methods making use of color intensity...
The acquisition of multiple brain imaging types for a given study is a very common practice. However these data are typically examined in separate analyses, rather than in a combined model. We propose a novel methodology to perform joint independent component analysis across image modalities, including structural MRI data, functional MRI activation data and EEG data, and to visualize the results via...
The acquisition of multiple brain imaging types for a given study is a very common practice. However these data are typically examined in separate analyses, rather than in a combined model. We propose a novel methodology to perform joint independent component analysis across image modalities, including structural MRI data, functional MRI activation data and EEG data, and to visualize the results via...
The acquisition of multiple brain imaging types for a given study is a very common practice. However these data are typically examined in separate analyses, rather than in a combined model. We propose a novel methodology to perform joint independent component analysis across image modalities, including structural MRI data, functional MRI activation data and EEG data, and to visualize the results via...
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