We propose an automated segmentation method to detect, segment, and quantify hyperreflective foci (HFs) in three-dimensional (3-D) spectral domain optical coherence tomography (SD-OCT). The algorithm is divided into three stages: preprocessing, layer segmentation, and HF segmentation. In this paper, a supervised classifier (random forest) was used to produce the set of boundary probabilities in which an optimal graph search method was then applied to identify and produce the layer segmentation using the Sobel edge algorithm. An automated grow-cut algorithm was applied to segment the HFs. The proposed algorithm was tested on 20 3-D SD-OCT volumes from 20 patients diagnosed with proliferative diabetic retinopathy (PDR) and diabetic macular edema (DME). The average dice similarity coefficient and correlation coefficient (r) are 62.30%, 96.90% for PDR, and 63.80%, 97.50% for DME, respectively. The proposed algorithm can provide clinicians with accurate quantitative information, such as the size and volume of the HFs. This can assist in clinical diagnosis, treatment, disease monitoring, and progression.
The usefulness of wavelet transforms has been compared and contrasted to Fourier transforms. Most importantly, wavelets transform provide a much needed alternative to Fourier transform for certain application such as pattern based monitoring and control. Effort has been made to provide a technique to extract essential trends from process signals and provide a compact representation. The effectiveness of a signal processing technique depends to a large extent on the nature of the signals involved. On technique that works for specific signal trends might not be effective in dealing with other signal trends. More so in pre-processing stage, signal extension has been identified as the critical factor influencing signal representation and retention of trends. This paper introduce a new algorithm in solving the present problems in sensor signal monitoring and control. The New Extension Technique (NET) was introduced, which provide an accurate wavelet decomposition irrespective of the nature of the signal. This method uses a statistical approach to provide a good approximation of the signal outside the boundaries of the signal depending on signal trends at the boundaries. Different statistical approaches were adopted for this purpose and four new extension methods were also introduced in order to ascertain which extension methods provide a reliable extension for all cases. The concept behind these methods is the same, since the signal samples close to the boundary are considered and a mean value is determined. The procedure for determining this mean value differs for each of these four methods; NET A, NET B, NET C, and NET D. The signal is then extended by making it symmetric with respect to that mean value and then inverting it.