PCA based clustering for brain tumor segmentation of T1w MRI images
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, vol.140, pp.19-28, 2017 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 140
- Publication Date: 2017
- Doi Number: 10.1016/j.cmpb.2016.11.011
- Journal Name: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Page Numbers: pp.19-28
- Çukurova University Affiliated: Yes
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.