title: PCA based clustering for brain tumor segmentation of T1w MRI images.
abstract: OBJECTIVE
  Medical images are huge collections of information that are difficult to store and process consuming extensive computing time. Therefore, the reduction techniques are commonly used as a data pre-processing step to make the image data less complex so that a high-dimensional data can be identified by an appropriate low-dimensional representation. PCA is one of the most popular multivariate methods for data reduction. This paper is focused on T1-weighted MRI images clustering for brain tumor segmentation with dimension reduction by different common Principle Component Analysis (PCA) algorithms. Our primary aim is to present a comparison between different variations of PCA algorithms on MRIs for two cluster methods.
  
  
  METHODS
  Five most common PCA algorithms; namely the conventional PCA, Probabilistic Principal Component Analysis (PPCA), Expectation Maximization Based Principal Component Analysis (EM-PCA), Generalize Hebbian Algorithm (GHA), and Adaptive Principal Component Extraction (APEX) were applied to reduce dimensionality in advance of two clustering algorithms, K-Means and Fuzzy C-Means. In the study, the T1-weighted MRI images of the human brain with brain tumor were used for clustering. In addition to the original size of 512 lines and 512 pixels per line, three more different sizes, 256 × 256, 128 × 128 and 64 × 64, were included in the study to examine their effect on the methods.
  
  
  RESULTS
  The obtained results were compared in terms of both the reconstruction errors and the Euclidean distance errors among the clustered images containing the same number of principle components.
  
  
  CONCLUSIONS
  According to the findings, the PPCA obtained the best results among all others. Furthermore, the EM-PCA and the PPCA assisted K-Means algorithm to accomplish the best clustering performance in the majority as well as achieving significant results with both clustering algorithms for all size of T1w MRI images.
authors.affiliation: Mersin University, Software Eng. Dept. 33440 Tarsus, Mersin, Turkey. Electronic address: iremer@mersin.edu.tr.
authors.forename: Irem Ersöz
authors.initials: IE
authors.lastname: Kaya
authors.affiliation: Mimar Sinan Fine Arts University, Department of Statistics, Istanbul, Turkey. Electronic address: ayca.pehlivanli@msgsu.edu.tr.
authors.forename: Ayça Çakmak
authors.initials: AÇ
authors.lastname: Pehlivanlı
authors.affiliation: Cukurova University, Electrical-Electronics Eng. Dept. 01330, Adana, Turkey. Electronic address: eminegezmez@yahoo.com.
authors.forename: Emine Gezmez
authors.initials: EG
authors.lastname: Sekizkardeş
authors.affiliation: Cukurova University, Electrical-Electronics Eng. Dept. 01330, Adana, Turkey. Electronic address: ibrikci@cu.edu.tr.
authors.forename: Turgay
authors.initials: T
authors.lastname: Ibrikci
mesh_terms: D001932:Brain Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging; D006801:Humans; D008279:Magnetic Resonance Imaging; D025341:Principal Component Analysis
keywords: Clustering; Dimension reduction; Fuzzy C-Means; PCA algorithms; k-means