In this paper, we describe a new method to identify the spots and to obtain the coordinates for the centroids from a Hartmann and Hartmann-Shack screen test when some noise and reflection errors are present using an independent dynamic thresholding method. The proposed algorithm is a robust one, working with almost no interactive operation. It proved to be good for noise removal in the presence of relatively high noise with low and uneven contrast and spot reflections. The process involves the binarization of the image through a thresholding operation. Subsequently, a data segmentation algorithm is used for spot identification. The spot identification and indexing is performed independently. Finally, the coordinates of each centroid are obtained using an appropriate masking for each spot. To test the procedure we used first synthetic images obtained from some specified functions and later we used a Hartmanngram image from a human cornea.
Hartmann and Shack-Hartmann, instead of measuring the wavefront deformations directly they measure the wavefront slopes, which are equivalent to the ray transverse aberrations. Numerous different integration methods had been described in the literature to obtain the wavefront deformations from these measurements. Basically, they can be classified in two different categories, i.e., model and zonal. In this work we briefly describe a modal method to integrate Hartmann, and Shack-Hartmann patterns. Using orthogonal wavefront slope aberration polynomials, instead of the commonly used Zernike polynomials for the wavefront deformations.
Recent advances in the acquisition of <i>in-vivo</i> high resolution retinal images through the use of Adaptive Optics (AO) have allowed the identification of cellular structures such as cones and rods, in and out of the fovea, in such a way that their histological characteristics can be studied <i>in-vivo</i> and later compared to data obtained <i>post-mortem</i>. In this work, an algorithm is proposed for the detection of photoreceptors; it consists of two stages: Early Cell Detection (ECD), to detect all candidate cells, and Refinement of Cell Detection (RCD), to reduce over-detection of photoreceptors. The algorithm has been tested using synthetic and real images, the latter acquired with an Adaptive Optics Scanning Light Ophthalmoscope (AOSLO). The proposed algorithm was compared against the one developed by Li and Roorda, and both algorithms were tested on synthetic and real images, yielding similar algorithm performance on both kinds of images when they had only cones; however, the algorithm developed by Li and Roorda, when applied to real images having cones and rods, identifies photoreceptors in vascular tissue, in addition to showing low rod detection.