7 - Data Processing and Analysis

Abstract

This chapter provides an introduction to various biomedical data processing and analysis methods. Computerized medical image processing and analysis involves specific methods such as image enhancement, segmentation, feature extraction, and image interpretation. Biomedical data processing and analysis has become a major component of biomedical research and clinical applications. Image enhancement is the procedure used to alter the appearance of an image or the subset of the image for better contrast or visualization of certain features and to facilitate the subsequent image-based medical diagnosis. Image enhancement algorithms are categorized into two types: spatial domain– and transform-domain–based methods. Image segmentation is the process of partitioning an image into sets of pixels corresponding to regions of physiologic interest. The segmented image can be used to make measurements such as brain volume, to detect abnormalities, or to visualize areas such as the brain surface. Classification, comparison, or analysis of images is performed almost always in terms of a set of features extracted from the images. This is necessary for one or more of the following reasons: reduction of dimensionality, incorporation of cues from human perception, transcendence of the limits of human perception, or the need for invariance. Medical image interpretation involves how radiologists work and how they interpret a medical image. There are four processes in image interpretation: search, detection/rejection, description, and diagnosis. Diagnoses based on images vary in their certainty, and the degree of uncertainty may be included in the radiologist’s report on an image.