PCA based clustering for brain tumor segmentation of T1w MRI images

[1]  Christos Georgakis,et al.  Disturbance detection and isolation by dynamic principal component analysis , 1995 .

[2]  Mita Nasipuri,et al.  Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images , 2015, Appl. Soft Comput..

[3]  P O Hoyer,et al.  Independent component analysis applied to feature extraction from colour and stereo images , 2000, Network.

[4]  Meritxell Bach Cuadra,et al.  A multidimensional segmentation evaluation for medical image data , 2009, Comput. Methods Programs Biomed..

[5]  Yudong Zhang,et al.  AN MR BRAIN IMAGES CLASSIFIER VIA PRINCIPAL COMPONENT ANALYSIS AND KERNEL SUPPORT , 2012 .

[6]  Abolghasem A. Raie,et al.  Probabilistic principal component analysis for texture modelling of adaptive active appearance models and its application for head pose estimation , 2015, IET Comput. Vis..

[7]  S. Sk.  A Survey of MRI-Based Brain Tumor Segmentation Methods , 2014 .

[8]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[9]  Christopher M. Bishop,et al.  Mixtures of Probabilistic Principal Component Analyzers , 1999, Neural Computation.

[10]  Zoubin Ghahramani,et al.  A Unifying Review of Linear Gaussian Models , 1999, Neural Computation.

[11]  Paul M. Thompson,et al.  Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models , 2008, IEEE Transactions on Medical Imaging.

[12]  E. Oja.  Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.

[13]  Kai Xiao,et al.  A Study: Segmentation of Lateral Ventricles in Brain MRI Using Fuzzy C-Means Clustering with Gaussian Smoothing , 2009, RSFDGrC.

[14]  Christopher Bowd,et al.  Recognizing patterns of visual field loss using unsupervised machine learning , 2014, Medical Imaging.

[15]  Trupti Baraskar,et al.  A novel approach for medical image segmentation using PCA and K-means clustering , 2015, 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT).

[16]  Simone G. O. Fiori,et al.  An Experimental Comparison of Three PCA Neural Networks , 2000, Neural Processing Letters.

[17]  Ahmed Atwan,et al.  Multi-resolution MRI Brain Image Segmentation Based on Morphological Pyramid and Fuzzy C-mean Clustering , 2015 .

[18]  Dorothy T. Thayer,et al.  EM algorithms for ML factor analysis , 1982 .

[19]  Daniel Commenges,et al.  Dynamical Biostatistical Models , 2015 .

[20]  Domingo López-Rodríguez,et al.  Probabilistic PCA Self-Organizing Maps , 2009, IEEE Transactions on Neural Networks.

[21]  Terence D. Sanger,et al.  Optimal unsupervised learning in a single-layer linear feedforward neural network , 1989, Neural Networks.

[22]  S.Y. Kung,et al.  Adaptive Principal component EXtraction (APEX) and applications , 1994, IEEE Trans. Signal Process..

[23]  K. Somasundaram,et al.  Fast Brain Abnormality Detection Method for Magnetic Resonance Images (MRI) of Human Head Scans Using K-Means Clustering Technique , 2013 .

[24]  Vinod Kumar,et al.  Segmentation, Feature Extraction, and Multiclass Brain Tumor Classification , 2013, Journal of Digital Imaging.

[25]  Li-Hong Juang,et al.  MRI brain lesion image detection based on color-converted K-means clustering segmentation , 2010 .

[26]  Junbin Gao,et al.  Image Outlier Detection and Feature Extraction via L1-Norm-Based 2D Probabilistic PCA , 2015, IEEE Transactions on Image Processing.

[27]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[28]  Morteza Zahedi,et al.  Improving the unsupervised LBG clustering algorithm performance in image segmentation using principal component analysis , 2015, Signal, Image and Video Processing.

[29]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[30]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[31]  Lingbo Yu,et al.  Probabilistic principal component analysis with expectation maximization (PPCA-EM) facilitates volume classification and estimates the missing data. , 2010, Journal of structural biology.

[32]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[33]  Liu Jin,et al.  A survey of MRI-based brain tumor segmentation methods , 2014 .

[34]  Konstantinos N. Plataniotis,et al.  Ensemble-based discriminant learning with boosting for face recognition , 2006, IEEE Transactions on Neural Networks.

[35]  Juha Karhunen,et al.  Principal component neural networks — Theory and applications , 1998, Pattern Analysis and Applications.

[36]  Driss Aboutajdine,et al.  Unsupervised Approach Data Analysis Based on Fuzzy Possibilistic Clustering: Application to Medical Image MRI , 2013, Comput. Intell. Neurosci..

[37]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[38]  Shiri Gordon,et al.  Unsupervised Image Clustering Using the Information Bottleneck Method , 2002, DAGM-Symposium.

[39]  Sam T. Roweis,et al.  EM Algorithms for PCA and SPCA , 1997, NIPS.

[40]  Michael E. Tipping,et al.  Probabilistic Principal Component Analysis , 1999 .