Feature Extraction and Feature Selection Procedures for Medical Image Analysis

Abstract

This chapter delves into the critical processes of feature extraction and selection in medical image analysis, essential for transforming raw data into actionable insights. It begins with preprocessing techniques, including noise reduction using linear and nonlinear filters, to enhance image quality. Intensity-based methods utilize pixel statistics, while texture analysis techniques like Local Binary Patterns, co-occurrence matrices, wavelets, Fourier transforms, and orientation histograms capture intricate patterns. Deep learning-based features, especially autoencoders, automatically learn hierarchical data representations. For feature selection, filter methods evaluate relevance independently, wrapper methods iteratively train models to identify optimal subsets, and embedded methods integrate selection within training, promoting sparsity. Dimensionality reduction techniques like Principal Component Analysis (PCA) condense feature spaces, retaining essential information.