PCA based clustering for brain tumor segmentation of T1w MRI images
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, cilt.140, ss.19-28, 2017 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 140
- Basım Tarihi: 2017
- Doi Numarası: 10.1016/j.cmpb.2016.11.011
- Dergi Adı: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.19-28
- Çukurova Üniversitesi Adresli: Evet
Özet
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.