Volumetric Image Pattern Recognition Using Three-Way Principal Component Analysis

Conference paper

DOI: 10.1007/978-3-319-51237-2_9

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10126)
Cite this paper as:
Itoh H., Imiya A., Sakai T. (2016) Volumetric Image Pattern Recognition Using Three-Way Principal Component Analysis. In: Reuter M., Wachinger C., Lombaert H. (eds) Spectral and Shape Analysis in Medical Imaging. SeSAMI 2016. Lecture Notes in Computer Science, vol 10126. Springer, Cham


The aim of the paper is to develop a relaxed closed form for tensor principal component analysis (PCA) for the recognition, classification, compression and retrieval of volumetric data. The tensor PCA derives the tensor Karhunen-Loève transform which compresses volumetric data, such as organs, cells in organs and microstructures in cells, preserving both the geometric and statistical properties of objects and spatial textures in the space. Furthermore, we numerically clarify that low-pass filtering after applying the multi-dimensional discrete cosine transform (DCT) efficiently approximates the data compression procedure based on tensor PCA. These orthogonal-projection-based data compression methods for three-way data is extracts outline shapes of biomedical objects such as organs and compressed expressions for the interior structures of cells.

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of Advanced Integration ScienceChiba UniversityInage-kuJapan
  2. 2.Institute of Management and Information TechnologiesChiba UniversityInage-kuJapan
  3. 3.Graduate School of EngineeringNagasaki UniversityNagasakiJapan

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