Tutorial: Multivariate Classification for Vibrational Spectroscopy in Biological Samples

Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have been successful methods for studying the interaction of light with biological materials and facilitating novel cell biology analysis. Spectrochemical analysis is very attractive in disease screening and diagnosis, microbiological studies and forensic and environmental investigations because of its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need for multivariate classification protocols allowing one to analyze biologically derived spectrochemical data to obtain accurate and reliable results. Multivariate classification comprises discriminant analysis and class-modeling techniques where multiple spectral variables are analyzed in conjunction to distinguish and assign unknown samples to pre-defined groups. The requirement for such protocols is demonstrated by the fact that applications of deep-learning algorithms of complex datasets are being increasingly recognized as critical for extracting important information and visualizing it in a readily interpretable form. Hereby, we have provided a tutorial for multivariate classification analysis of vibrational spectroscopy data (FTIR, Raman and near-IR) highlighting a series of critical steps, such as preprocessing, data selection, feature extraction, classification and model validation. This is an essential aspect toward the construction of a practical spectrochemical analysis model for biological analysis in real-world applications, where fast, accurate and reliable classification models are fundamental

Mitigation of Speckle Noise in Optical Coherence Tomograms

Optical Coherence Tomography (OCT) is a promising high-resolution imaging technique that works based on low coherent interferometry. However, like other low coherent imaging modalities, OCT suffers from an artifact called, speckle. Speckle reduces the detectability of diagnostically relevant features in the tissue. Retinal optical coherence tomograms are of a great importance in detecting and diagnosing eye diseases. Different hardware or software based techniques are devised in literatures to mitigate speckle noise. The ultimate aim of any software-based despeckling technique is to suppress the noise part of speckle while preserves the information carrying portion of that. In this chapter, we reviewed the most prominent speckle reduction methods for OCT images to date and then present a novel and intelligent speckle reduction algorithm to reduce speckle in OCT images of retina, based on an ensemble framework of Multi-Layer Perceptron (MLP) neural networks