PCA based clustering for brain tumor segmentation of T1w MRI images


Kaya I. E. , Pehlivanli A. C. , SEKIZKARDES E. G. , İBRİKÇİ T.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, vol.140, pp.19-28, 2017 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 140
  • Publication Date: 2017
  • Doi Number: 10.1016/j.cmpb.2016.11.011
  • Title of Journal : COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
  • Page Numbers: pp.19-28

Abstract

Background and 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.