Andrade Diego, Gifford Howard C, Das Mini
Department of Biomedical Engineering, University of Houston, Houston, 77204, USA.
Department of Physics, University of Houston, Houston, 77204, USA.
Proc SPIE Int Soc Opt Eng. 2024 Feb;12929. doi: 10.1117/12.3008844. Epub 2024 Mar 29.
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.
本研究探讨了基于纹理的分类在视觉搜索/分类早期阶段的有效性。首先，我们总结了我们团队先前关于基于断层乳腺图像的二阶统计图像纹理特征预测信号检测难度的研究结果。随着视觉搜索模型观察者的发展，以准确模拟医学图像中的搜索和定位，我们继续研究基于纹理的分类/分割方法的有效性。我们通过纹理图和高斯混合模型（GMM）的组合来考虑一阶和二阶特征。我们的目标是评估在视觉搜索过程的早期阶段整合这些方法的优势，特别是在临床数据中目标形态特征可能不太明显或未知的情况下。通过融合成像物理知识和基于纹理的GMM，我们提高了分类效率并优化了疑似包含目标位置区域的定位。
