Fundus foveal localization based on vessel model.

The problem of automatically locating the fovea, a spot located in the center of the macula and responsible for sharp central vision, on the retinal surface image is considered. The uniqueness of the strategy presented here is the relatively small amount of computational expense of a quick and robust method conducive to real-time applications. The algorithm first identifies the main blood vessels using the modified active shape model (ASM) and then expeditiously attempts to fit the parabolic shape to the overall result to locate the fovea.

Automated optic disk boundary detection by modified active contour model.

This paper presents a novel deformable-model-based algorithm for fully automated detection of optic disk boundary in fundus images. The proposed method improves and extends the original snake (deforming-only technique) in two aspects: clustering and smoothing update. The contour points are first self-separated into edge-point group or uncertain-point group by clustering after each deformation, and these contour points are then updated by different criteria based on different groups.

Wavelet transform domain data embedding in a medical image.

In this work, we propose a wavelet-based technique for embedding medical data in a medical image. In the spectral domain, the patient data are embedded into the wavelet coefficients of the host image. A diagnostic distortion measure (DDM) has been defined to measure the visible distortions between the original image and the embedded image.

Auto-adjusted 3-D optic disk viewing from low-resolution stereo fundus image.

Three-dimensional (3-D) visualization of the optic nerve head (optic disk) is very useful for clinical applications. It allows clinicians to measure the disk parameters more accurately and thus make the pathological diagnosis and progression monitoring easier. This paper describes an automatic, precise, 3-D optic nerve head reconstruction method from a pair of stereo images for which efficient steps including sparse-image registration and dense-depth recovery are used.

Automated feature extraction in color retinal images by a model based approach.

Color retinal photography is an important tool to detect the evidence of various eye diseases. Novel methods to extract the main features in color retinal images have been developed in this paper. Principal component analysis is employed to locate optic disk; A modified active shape model is proposed in the shape detection of optic disk; A fundus coordinate system is established to provide a better description of the features in the retinal images; An approach to detect exudates by the combined region growing and edge detection is proposed.

Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter.

In this paper, the fitness of estimating vessel profiles with Gaussian function is evaluated and an amplitude-modified second-order Gaussian filter is proposed for the detection and measurement of vessels. Mathematical analysis is given and supported by a simulation and experiments to demonstrate that the vessel width can be measured in linear relationship with the "spreading factor" of the matched filter when the magnitude coefficient of the filter is suitably assigned. The absolute value of vessel diameter can be determined simply by using a precalibrated line, which is typically required since images are always system dependent.