PCA based clustering for brain tumor segmentation of T1w MRI images. (March 2017)

• Record Type: Journal Article
• Authors: Kaya, Irem Ersöz; Pehlivanlı, Ayça Çakmak; Sekizkardeş, Emine Gezmez; Ibrikci, Turgay
• Is Part Of: Computer methods and programs in biomedicine. Volume 140(2017)
• Page Start: 19
• Page End: 28
• Publication Date: 2017-03
• Subjects: Dimension reduction -- PCA algorithms -- Clustering -- k-means -- Fuzzy C-Means; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.11.011
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: To investigate the performance of the PCA based clustering on MR images. In order to achieve the goal, the PCA methods were first implemented on the MRI images which were resized into three different sizes, as well as the original size. The two common methods, K-means and FCM, are preferred for clustering. The success of the five PCA algorithms, PCA, PPCA, EM-PCA, GHA, APEX, in dimensionality reduction for clustering and to evaluate the methods according to their propensity to cause information loss. The EM-PCA and the PPCA achieve the successful results with the two clustering algorithms implemented on the resized MRI images. Therefore, it can be concluded that the PPCA and the EM-PPCA work effectively with the two clustering algorithms. 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. 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. Conclusion: 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.