Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
First and Second Order Statistics Features for Classification of Magnetic Resonance Brain Images
211
Zitationen
2
Autoren
2012
Jahr
Abstract
In literature, features based on First and Second Order Statistics that characterizes textures are used for classification of images. Features based on statistics of texture provide far less number of relevant and distinguishable features in comparison to existing methods based on wavelet transformation. In this paper, we investigated performance of texture-based features in comparison to wavelet-based features with commonly used classifiers for the classification of Alzheimer’s disease based on T2-weighted MRI brain image. The performance is evaluated in terms of sensitivity, specificity, accuracy, training and testing time. Experiments are performed on publicly available medical brain images. Experimental results show that the performance with First and Second Order Statistics based features is significantly better in comparison to existing methods based on wavelet transformation in terms of all performance measures for all classifiers.
Ähnliche Arbeiten
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
2018 · 6.344 Zit.
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
2014 · 6.226 Zit.
Brain tumor segmentation with Deep Neural Networks
2016 · 3.174 Zit.
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
2016 · 2.604 Zit.
Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations
2017 · 2.488 Zit.