脳形態変化のパターン分類と可視化のための画像データマイニング

DOI Web Site Web Site PubMed 参考文献6件
  • 村川 彩希
    熊本大学大学院保健学教育部
  • 生田 理恵
    熊本大学医学部保健学科放射線技術学専攻 大阪大学医学部附属病院医療技術部放射線部門
  • 内山 良一
    熊本大学生命科学研究部先端生命医療科学部門
  • 白石 順二
    熊本大学生命科学研究部先端生命医療科学部門

書誌事項

タイトル別名
  • Image Data Mining for Pattern Classification and Visualization of Morphological Changes in Brain MR Images
  • ノウ ケイタイ ヘンカ ノ パターン ブンルイ ト カシカ ノ タメ ノ ガゾウ データマイニング

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Hospital information systems (HISs) and picture archiving and communication systems (PACSs) are archiving large amounts of data (i.e., “big data”) that are not being used. Therefore, many research projects in progress are trying to use “big data” for the development of early diagnosis, prediction of disease onset, and personalized therapies. In this study, we propose a new method for image data mining to identify regularities and abnormalities in the large image data sets. We used 70 archived magnetic resonance (MR) images that were acquired using three-dimensional magnetization-prepared rapid acquisition with gradient echo (3D MP-RAGE). These images were obtained from the Alzheimer’s disease neuroimaging initiative (ADNI) database. For anatomical standardization of the data, we used the statistical parametric mapping (SPM) software. Using a similarity matrix based on cross-correlation coefficients (CCs) calculated from an anatomical region and a hierarchical clustering technique, we classified all the abnormal cases into five groups. The Z score map identified the difference between a standard normal brain and each of those from the Alzheimer’s groups. In addition, the scatter plot obtained from two similarity matrixes visualized the regularities and abnormalities in the image data sets. Image features identified using our method could be useful for understanding of image findings associated with Alzheimer’s disease.

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