A multiresolution analysis framework for breast tumor classification based on DCE-MRI

Tzalavra, Alexia G.; Zacharaki, Evangelia I.; Tsiaparas, Nikolaos N.; Constantinidis, Fotios; Nikita, Konstantina S.

doi:10.1109/ist.2014.6958482

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…Multiresolution methods, such as the wavelet analysis, transform images into a representation containing both frequency and spatial information (33). The mean and entropy values extracted from the subimages resulting from wavelet decomposition of DCE-MRI images have been used to classify malignant and benign breast tumors (34, 35). Braman et al (36) used Gabor wavelet, co-occurrence measures and energy measures to generate 1980 features from DCE images to predict breast cancer response to NACT.…”

Section: Introductionmentioning

confidence: 99%

Early Prediction of Breast Cancer Therapy Response using Multiresolution Fractal Analysis of DCE-MRI Parametric Maps

et al. 2019

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We aimed to determine whether multiresolution fractal analysis of voxel-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps can provide early prediction of breast cancer response to neoadjuvant chemotherapy (NACT). In total, 55 patients underwent 4 DCE-MRI examinations before, during, and after NACT. The shutter-speed model was used to analyze the DCE-MRI data and generate parametric maps within the tumor region of interest. The proposed multiresolution fractal method and the more conventional methods of single-resolution fractal, gray-level co-occurrence matrix, and run-length matrix were used to extract features from the parametric maps. Only the data obtained before and after the first NACT cycle were used to evaluate early prediction of response. With a training (N = 40) and testing (N = 15) data set, support vector machine was used to assess the predictive abilities of the features in classification of pathologic complete response versus non-pathologic complete response. Generally the multiresolution fractal features from individual maps and the concatenated features from all parametric maps showed better predictive performances than conventional features, with receiver operating curve area under the curve (AUC) values of 0.91 (all parameters) and 0.80 (K trans ), in the training and testing sets, respectively. The differences in AUC were statistically significant ( P < .05) for several parametric maps. Thus, multiresolution analysis that decomposes the texture at various spatial-frequency scales may more accurately capture changes in tumor vascular heterogeneity as measured by DCE-MRI, and therefore provide better early prediction of NACT response.

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Section: Introductionmentioning

confidence: 99%

Early Prediction of Breast Cancer Therapy Response using Multiresolution Fractal Analysis of DCE-MRI Parametric Maps

et al. 2019

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“…In this context, the wavelet transform has effectively been used in the classification of breast DCE‐MRI images (Tzalavra et al, 2016). Curvelet multiresolution texture analysis features (Tzalavra et al, 2014) have yielded high performance in breast DCE‐MRI tumour classification since the curvelet transform (Candes et al, 2006) can capture the curve singularities in an efficient way and also derive sufficient directional details from the medical images.…”

Section: Introductionmentioning

confidence: 99%

Dynamic contrast enhanced‐magnetic resonance imaging radiomics combined with a hybrid adaptiveneuro‐fuzzyinference system‐particle swarm optimization approach for breast tumour classification

et al. 2021

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The authors propose a method for breast dynamic contrast enhanced‐magnetic resonance imaging classification by combining radiomic texture analysis with a hybrid adaptive neuro‐fuzzy inference system (ANFIS)‐particle swarm optimization (PSO) classifier. The fast discrete curvelet transform is utilized as a decomposition scheme in multiple scales. The mean and entropy features extracted from the produced scheme are used as texture descriptors. Principal component analysis (PCA) involves reduction of the dimensionality of the initial feature set. The transformed feature vector is subsequently introduced to a hybrid ANFIS‐PSO classifier. The average overall classification power of the proposed hybrid ANFIS‐PSO classifier is comparatively assessed to that obtained using several classifiers (ANFIS, linear discriminant analysis, Naïve Bayes, artificial neural networks, random forest and support vector machine) by using the 70 training‐30 testing data ratio. The comparison performed highlights the superiority of the proposed methodology, thus underlying the potential of ANFIS‐PSO for the breast cancer diagnosis with a classification accuracy of 94%.

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“…Gal et al [11] extracted spatiotemporal features from a parametric model of contrast enhancement. Tzalavra et al [12] extracted textural features from SWT detail sub-images in DCE-MRI data.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Multi-resolution Analysis Patterns for Texture Classification of Breast Tumors Based on DCE-MRI

Tzalavra

Dalakleidi

Zacharaki

et al. 2016

Machine Learning in Medical Imaging

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Abstract. Although Fourier and Wavelet Transform have been widely used for texture classification methods in medical images, the discrimination performance of FDCT has not been investigated so far in respect to breast cancer detection. Ιn this paper, three multi-resolution transforms, namely the Discrete Wavelet Transform (DWT), the Stationary Wavelet Transform (SWT) and the Fast Discrete Curvelet Transform (FDCT) were comparatively assessed with respect to their ability to discriminate between malignant and benign breast tumors in Dynamic Contrast-Enhanced Magnetic Resonance Images (DCE-MRI). The mean and entropy of the detail sub-images for each decomposition scheme were used as texture features, which were subsequently fed as input into several classifiers. FDCT features fed to a Linear Discriminant Analysis (LDA) classifier produced the highest overall classification performance (93,18 % Accuracy).

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A multiresolution analysis framework for breast tumor classification based on DCE-MRI

Cited by 3 publications

References 14 publications

Early Prediction of Breast Cancer Therapy Response using Multiresolution Fractal Analysis of DCE-MRI Parametric Maps

Early Prediction of Breast Cancer Therapy Response using Multiresolution Fractal Analysis of DCE-MRI Parametric Maps

Dynamic contrast enhanced‐magnetic resonance imaging radiomics combined with a hybrid adaptiveneuro‐fuzzyinference system‐particle swarm optimization approach for breast tumour classification

Comparison of Multi-resolution Analysis Patterns for Texture Classification of Breast Tumors Based on DCE-MRI

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