Preprocessing Techniques for Neuroimaging Modalities: An In-Depth Analysis

Abstract

Neuroimage analysis and data processing from various neuro-imaging modalities have been a multidisciplinary research field for a long time. Numerous types of research have been carried out in the area for multiple applications of neuroimaging and intelligent techniques to make faster and more accurate results. Different modalities gather information for detecting, treating, and identifying various neurological disorders. Each modality generates different kinds of data, including images and signals. Applying artificial intelligence-based techniques for analysing the inputs from the neuroimaging modalities requires preprocessing. Preprocessing techniques are used to fine-tune the data for better results and the application of intelligent methods. Various techniques and pipelines/workflows (steps for preprocessing the data from the imaging modalities) have been developed and followed by multiple researchers for the preprocessing of neuroimaging data. The preprocessing steps include the steps followed in removing noisy data from the inputs, converting the data to a different format, and adding additional information to improve the performance of the algorithm on the data. In this chapter, we compare the various neuroimaging techniques, the type of data they generate and the preprocessing techniques that various researchers frequently use to process data to apply them in artificial intelligence-based algorithms for the classification, prediction, and prognosis of various neurological disorders.