| P50 | author | Tom R. Gaunt | Q37390320 |
| | Jose A. Seoane | Q41049335 |
| | Carlos Fernandez-Lozano | Q43370325 |
| | Julian Dorado | Q48975674 |
| | Marcos Gestal | Q57152612 |
| P2093 | author name string | Colin Campbell | |
| P2860 | cites work | An introduction to ROC analysis | Q29299877 |
| | pROC: an open-source package for R and S+ to analyze and compare ROC curves | Q30050695 |
| | Computer-assisted detection of infectious lung diseases: a review | Q30459495 |
| | A pathway-based data integration framework for prediction of disease progression | Q30574210 |
| | Texture analysis of bone marrow in knee MRI for classification of subjects with bone marrow lesion - data from the Osteoarthritis Initiative. | Q30592922 |
| | A statistical framework for genomic data fusion | Q30929198 |
| | Malignant-lesion segmentation using 4D co-occurrence texture analysis applied to dynamic contrast-enhanced magnetic resonance breast image data | Q31097722 |
| | Texture analysis of MR images of patients with mild traumatic brain injury | Q33576347 |
| | FLAIR signal and texture analysis for lateralizing mesial temporal lobe epilepsy | Q33718146 |
| | Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme | Q33824804 |
| | Texture analysis of poly-adenylated mRNA staining following global brain ischemia and reperfusion | Q33866682 |
| | Monitoring pulmonary fibrosis by fusing clinical, physiological, and computed tomography features | Q34131879 |
| | Computer-assisted lip diagnosis on Traditional Chinese Medicine using multi-class support vector machines | Q34382013 |
| | Texture analysis of T1 - and T2 -weighted MR images and use of probabilistic neural network to discriminate posterior fossa tumours in children | Q35929955 |
| | Non-Gaussian statistical properties of breast images | Q36419933 |
| | Noninvasive classification of hepatic fibrosis based on texture parameters from double contrast-enhanced magnetic resonance images | Q36716734 |
| | A review of feature selection techniques in bioinformatics | Q36919689 |
| | A supervised learning approach for Crohn's disease detection using higher-order image statistics and a novel shape asymmetry measure | Q37196269 |
| | Non-Hodgkin lymphoma response evaluation with MRI texture classification | Q37265099 |
| | Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. | Q37911137 |
| | Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. | Q38061398 |
| | Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT. | Q41777454 |
| | Assessment of texture measures susceptibility to noise in conventional and contrast enhanced computed tomography lung tumour images | Q42645204 |
| | Texture-based and diffusion-weighted discrimination of parotid gland lesions on MR images at 3.0 Tesla | Q45193467 |
| | Improving enzyme regulatory protein classification by means of SVM-RFE feature selection. | Q45957691 |
| | Small-sample precision of ROC-related estimates | Q48231315 |
| | Adaptive elastic segmentation of brain MRI via shape-model-guided evolutionary programming | Q48429790 |
| | An automated method for analysis of flow characteristics of circulating particles from in vivo video microscopy. | Q48946863 |
| | Support vector machines for histogram-based image classification. | Q50660257 |
| | Hybrid model based on Genetic Algorithms and SVM applied to variable selection within fruit juice classification. | Q51120792 |
| | Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. | Q51529638 |
| | Development of tolerant features for characterization of masses in mammograms. | Q51616638 |
| | Characterization of breast cancer types by texture analysis of magnetic resonance images. | Q51772778 |
| | Segmentation of regions of interest in mammograms in a topographic approach. | Q51781644 |
| | Texture analysis for tissue discrimination on T1-weighted MR images of the knee joint in a multicenter study: Transferability of texture features and comparison of feature selection methods and classifiers. | Q51962486 |
| | Optimal replication and the importance of experimental design for gel-based quantitative proteomics. | Q51971547 |
| | Image feature selection by a genetic algorithm: application to classification of mass and normal breast tissue. | Q52294884 |
| | Automated texture-based segmentation of ultrasound images of the prostate. | Q52305391 |
| | Segmenting Ultrasound Images of the Prostate Using Neural Networks | Q52422874 |
| | Dynamic contrast-enhanced texture analysis of the liver: initial assessment in colorectal cancer | Q53259154 |
| | AUC: a misleading measure of the performance of predictive distribution models | Q56445156 |
| | Measuring classifier performance: a coherent alternative to the area under the ROC curve | Q56445159 |
| | Gene Selection for Cancer Classification using Support Vector Machines | Q56535529 |
| | A stagewise rejective multiple test procedure based on a modified Bonferroni test | Q56622249 |
| | Wrappers for feature subset selection | Q56689295 |
| | Properties of Sufficiency and Statistical Tests | Q56782447 |
| | A Tutorial on Support Vector Machines for Pattern Recognition | Q56885196 |
| | The Split-Apply-Combine Strategy for Data Analysis | Q56910804 |
| | High Order Texture-Based Analysis in Biomedical Images | Q56984275 |
| | An analysis of variance test for normality (complete samples) | Q57253423 |
| | kernlab- AnS4Package for Kernel Methods inR | Q57263861 |
| | The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance | Q57424075 |
| | Learning with Support Vector Machines | Q58758958 |
| P433 | issue | 9 | |
| P921 | main subject | feature selection | Q446488 |
| P304 | page(s) | 2469-2480 | |
| P577 | publication date | 2015-01-08 | |
| P1433 | published in | Soft Computing | Q15764822 |
| P1476 | title | Texture classification using feature selection and kernel-based techniques | |
| P478 | volume | 19 | |