Gross feature recognition of Anatomical Images based on Atlas grid (GAIA): Incorporating the local discrepancy between an atlas and a target image to capture the features of anatomic brain MRI.
Gross feature recognition of Anatomical Images based on Atlas grid (GAIA): Incorporating the local discrepancy between an atlas and a target image to capture the features of anatomic brain MRI.
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DOI：
10.1016/j.nicl.2013.08.006
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发表时间：
2013
影响因子：
4.2
通讯作者：
Oishi, Kenichi
中科院分区：
文献类型：
--
作者：
Qin, Yuan-Yuan;Hsu, Johnny T.;Yoshida, Shoko;Faria, Andreia V.;Oishi, Kumiko;Unschuld, Paul G.;Redgrave, Graham W.;Ying, Sarah H.;Ross, Christopher A.;van Zijl, Peter C. M.;Hillis, Argye E.;Albert, Marilyn S.;Lyketsos, Constantine G.;Miller, Michael I.;Mori, Susumu;Oishi, Kenichi
关键词：
我们的目标是开发一种新方法，将 T1 加权脑 MRI 转换为特征向量，可用于基于内容的图像检索 (CBIR)。为了克服临床病例中广泛的解剖变异性和成像协议的不一致，我们引入了基于图谱网格的解剖图像的粗体特征识别（GAIA），其中由病理（例如缺血）或生理（发育和衰老）强度变化以及图谱图像重合失调引起的局部强度变化用于捕获解剖特征 目标图像。作为概念验证，GAIA 用于对阿尔茨海默病、亨廷顿病、脊髓小脑共济失调 6 型和原发性进行性失语症的四种亚型的多个阶段的神经解剖学特征进行模式识别。对于每种疾病，将基于训练数据集的特征向量应用于测试数据集，以评估模式识别的准确性。从训练数据集中提取的特征向量与所选神经退行性疾病的已知病理特征非常吻合。总体而言，测试图像的判别分数准确地将这些测试图像分类到正确的疾病类别。没有典型疾病相关解剖特征的图像被错误分类。该方法是一种基于疾病相关解剖特征的图像特征提取的有前途的方法，它应该使用户能够提交患者图像并搜索具有相似解剖表型的过去的临床病例。介绍了一种将解剖脑 MRI 转换为特征向量的新方法。局部图集-图像不一致的程度用于捕获解剖特征。该方法应用于各种神经退行性疾病的模式识别。特征向量与已知的疾病病理特征非常吻合。该方法准确地将测试图像分类到正确的疾病类别。
We aimed to develop a new method to convert T1-weighted brain MRIs to feature vectors, which could be used for content-based image retrieval (CBIR). To overcome the wide range of anatomical variability in clinical cases and the inconsistency of imaging protocols, we introduced the Gross feature recognition of Anatomical Images based on Atlas grid (GAIA), in which the local intensity alteration, caused by pathological (e.g., ischemia) or physiological (development and aging) intensity changes, as well as by atlas–image misregistration, is used to capture the anatomical features of target images. As a proof-of-concept, the GAIA was applied for pattern recognition of the neuroanatomical features of multiple stages of Alzheimer's disease, Huntington's disease, spinocerebellar ataxia type 6, and four subtypes of primary progressive aphasia. For each of these diseases, feature vectors based on a training dataset were applied to a test dataset to evaluate the accuracy of pattern recognition. The feature vectors extracted from the training dataset agreed well with the known pathological hallmarks of the selected neurodegenerative diseases. Overall, discriminant scores of the test images accurately categorized these test images to the correct disease categories. Images without typical disease-related anatomical features were misclassified. The proposed method is a promising method for image feature extraction based on disease-related anatomical features, which should enable users to submit a patient image and search past clinical cases with similar anatomical phenotypes. A novel method to convert anatomical brain MRIs to feature vectors is introduced. Degree of local atlas–image disagreement is used to capture the anatomical features. The method was applied for pattern recognition of various neurodegenerative diseases. The feature vectors agreed well with the known pathological hallmarks of diseases. The method accurately categorized test images to the correct disease categories.
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