Image Texture Based Classification Methods to Mimic Perceptual Models of Search and Localization in Medical Images.

Abstract

This study explores the validity of texture-based classification in the early stages of visual search/classification. Initially, we summarize our group's prior findings regarding the prediction of signal detection difficulty based on second-order statistical image texture features in tomographic breast images. Alongside the development of visual search model observers to accurately mimic search and localization in medical images, we continue examining the efficacy of texture-based classification/segmentation methods. We consider both first and second-order features through a combination of texture maps and Gaussian mixture model (GMM). Our aim is to evaluate the advantages of integrating these methods at the early stages of the visual search process, particularly in scenarios where target morphological features may be less apparent or known, as in clinical data. By merging knowledge of imaging physics and texture based GMM, we enhance classification efficiency and refine localization of regions suspected of containing target locations.