Improving brain tumor characterization on MRI by probabilistic neural networks and non-linear transformation of textural features

Georgiadis, Pantelis; Cavouras, D.; Kalatzis, Ioannis; Daskalakis, Antonis; Kagadis, George C.; Sifaki, Koralia; Malamas, Menelaos; Nikiforidis, George; Solomou, Ekaterini

doi:10.1016/j.cmpb.2007.10.007

Cited by 128 publications

(60 citation statements)

References 22 publications

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“…This process would allow for the extraction of more useful underlying information based on quantitatively derived features: Radiomics. Several institutes have reported quantitative analysis studies, with a focus on radiomic features, for different imaging modalities such as computed tomography (CT),1, 2, 3 and magnetic resonance imaging (MRI) 4, 5, 6. The investigation of positron emission tomography (PET) radiomics was first reported in 2009 7, 8, 9.…”

Section: Introductionmentioning

confidence: 99%

Reproducibility of F18‐FDG PET radiomic features for different cervical tumor segmentation methods, gray‐level discretization, and reconstruction algorithms

Altazi

Zhang

Fernandez

et al. 2017

J Applied Clin Med Phys

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Site‐specific investigations of the role of radiomics in cancer diagnosis and therapy are emerging. We evaluated the reproducibility of radiomic features extracted from 18Flourine–fluorodeoxyglucose (18F‐FDG) PET images for three parameters: manual versus computer‐aided segmentation methods, gray‐level discretization, and PET image reconstruction algorithms. Our cohort consisted of pretreatment PET/CT scans from 88 cervical cancer patients. Two board‐certified radiation oncologists manually segmented the metabolic tumor volume (MTV1 and MTV2) for each patient. For comparison, we used a graphical‐based method to generate semiautomated segmented volumes (GBSV). To address any perturbations in radiomic feature values, we down‐sampled the tumor volumes into three gray‐levels: 32, 64, and 128 from the original gray‐level of 256. Finally, we analyzed the effect on radiomic features on PET images of eight patients due to four PET 3D‐reconstruction algorithms: maximum likelihood‐ordered subset expectation maximization (OSEM) iterative reconstruction (IR) method, fourier rebinning‐ML‐OSEM (FOREIR), FORE‐filtered back projection (FOREFBP), and 3D‐Reprojection (3DRP) analytical method. We extracted 79 features from all segmentation method, gray‐levels of down‐sampled volumes, and PET reconstruction algorithms. The features were extracted using gray‐level co‐occurrence matrices (GLCM), gray‐level size zone matrices (GLSZM), gray‐level run‐length matrices (GLRLM), neighborhood gray‐tone difference matrices (NGTDM), shape‐based features (SF), and intensity histogram features (IHF). We computed the Dice coefficient between each MTV and GBSV to measure segmentation accuracy. Coefficient values close to one indicate high agreement, and values close to zero indicate low agreement. We evaluated the effect on radiomic features by calculating the mean percentage differences (d¯) between feature values measured from each pair of parameter elements (i.e. segmentation methods: MTV1‐MTV2, MTV1‐GBSV, MTV2‐GBSV; gray‐levels: 64‐32, 64‐128, and 64‐256; reconstruction algorithms: OSEM‐FORE‐OSEM, OSEM‐FOREFBP, and OSEM‐3DRP). We used false|normald¯false| as a measure of radiomic feature reproducibility level, where any feature scored false|normald¯false| ±SD ≤ |25|% ± 35% was considered reproducible. We used Bland–Altman analysis to evaluate the mean, standard deviation (SD), and upper/lower reproducibility limits (U/LRL) for radiomic features in response to variation in each testing parameter. Furthermore, we proposed U/LRL as a method to classify the level of reproducibility: High— ±1% ≤ U/LRL ≤ ±30%; Intermediate— ±30% < U/LRL ≤ ±45%; Low— ±45 < U/LRL ≤ ±50%. We considered any feature below the low level as nonreproducible (NR). Finally, we calculated the interclass correlation coefficient (ICC) to evaluate the reliability of radiomic feature measurements for each parameter. The segmented volumes of 65 patients (81.3%) scored Dice coefficient >0.75 for all three volumes. The result outcomes revealed a tendency of higher radiomic fe...

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Section: Introductionmentioning

confidence: 99%

Reproducibility of F18‐FDG PET radiomic features for different cervical tumor segmentation methods, gray‐level discretization, and reconstruction algorithms

Altazi

Zhang

Fernandez

et al. 2017

J Applied Clin Med Phys

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“…100 features extracted by Zarchari et al [16] where they used contour based methods, statistical, GLCM, intensity, and Gabor. Georgiadis et al [17] extracted 4 different features from histograms, 4 different and 10 features from GLRLM matrix and 22 features from GLCM matrix. Karssemeijer et al [18] found 67% accuracy for BIRADS-IV images using k-NN classifier Sampaio [19] considered 623 images from BIRADS-II image database.…”

Section: Related Workmentioning

confidence: 99%

Breast tumor detection and classification in Mammograms: Gabor wavelet vs. statistical features

Singh¹,

Sharma²

2018

Preprint

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Breast cancer is the second cause of fatality among all cancers for women. Automatic classification of breast cancer lesions in mammograms is a challenging task due to the irregularity and complexity of the location, size, shape, and texture of these lesions. The intensity dissimilarity has been found between breast cancer tissues and normal tissues, when a multispectral anatomical mammographic screening scans have been done. In this work, two approaches have been evaluated to classify the breast tumor lesions. The first one is through Gabor wavelet features and the second one is Statistical features. Subsequently, support vector machine, Multilayer Perceptron and KNN classifiers have been used with computer based method for breast tumor classification.

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“…Here texture features are extracted using 1st order and 2nd order statistics methods of the tumor region tissue [15][16][17]. In our experiment, we calculated five FOS features which are the average gray level, standard deviation, entropy, skewness, and kurtosis [22].…”

Section: Texture (Statistical) Featuresmentioning

confidence: 99%

“…Authors in [15] developed an algorithm for discriminating between benign or malignant brain tumors on MR image based on texture features. Classification was performed by probabilistic neural network.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Efficiency of different Feature Sets on Brain Tumor Classification in MR Images

Eltayeb¹,

Salem²,

Al-Atabany³

2018

IJCA

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In this paper, a study for evaluating the efficacy of different feature sets that used brain tumor classification is presented. Different features sets are extracted as shape, 1 st order texture features (FOS), 2 nd order (GLCM, GLRLM), boundary features, and wavelet-based features. The brain tumors are extracted using the k-means clustering algorithm. Then different classifiers such as Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) were used in the classification process.A set of 65 real and simulated (Flair modality) MRI images from multimodal brain tumor image segmentation benchmark (BRATS) organized by MICCAI 2012 challenge is used for performance evaluation. The overall segmentation results for the 65 volumes are 90.15±0.12. For the Feature sets efficacy step, the highest accuracy of 94.74% is achieved by the SVM when using the wavelet-based features. The lowest accuracy achieved by the three classifiers obtained when using the second order texture features..

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Improving brain tumor characterization on MRI by probabilistic neural networks and non-linear transformation of textural features

Cited by 128 publications

References 22 publications

Reproducibility of F18‐FDG PET radiomic features for different cervical tumor segmentation methods, gray‐level discretization, and reconstruction algorithms

Reproducibility of F18‐FDG PET radiomic features for different cervical tumor segmentation methods, gray‐level discretization, and reconstruction algorithms

Breast tumor detection and classification in Mammograms: Gabor wavelet vs. statistical features

Evaluating the Efficiency of different Feature Sets on Brain Tumor Classification in MR Images

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