Volumetric texture analysis of breast lesions on contrast‐enhanced magnetic resonance images

Chen, Weijie; Giger, Maryellen L.; Li, Hui; Bick, Ulrich; Newstead, Gillian M.

doi:10.1002/mrm.21347

Cited by 266 publications

(234 citation statements)

References 23 publications

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“…However, this approach has not been fully investigated in the differentiation of malignant from benign breast lesions in DCE-MRI. Reported studies have demonstrated that malignant lesions can be differentiated from benign lesions by means of their increased heterogeneity as expressed on exchange rate parameter maps [14], on normalised maximum intensity-time ratio (nMITR) projection data [15], and on lesion data of a single postcontrast time frame [16][17][18][19].Specifically, Issa et al [14] quantified heterogeneity of breast lesions expressed on exchange rate parameter maps provided by pixel-wise three-compartment pharmacokinetic modelling over semi-automatically delineated ROIs. Heterogeneity was quantified by means of the standard deviation of the lesion exchange rate…”

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Assessing heterogeneity of lesion enhancement kinetics in dynamic contrast-enhanced MRI for breast cancer diagnosis

Karahaliou¹,

Vassiou²,

Arikidis³

et al. 2010

BJR

117

110

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ABSTRACT. The current study investigates the feasibility of using texture analysis to quantify the heterogeneity of lesion enhancement kinetics in order to discriminate malignant from benign breast lesions. A total of 82 biopsy-proven breast lesions (51 malignant, 31 benign), originating from 74 women subjected to dynamic contrastenhanced magnetic resonance imaging (DCE-MRI) were analysed. Pixel-wise analysis of DCE-MRI lesion data was performed to generate initial enhancement, post-initial enhancement and signal enhancement ratio (SER) parametric maps; these maps were subsequently subjected to co-occurrence matrix texture analysis. The discriminating ability of texture features extracted from each parametric map was investigated using a least-squares minimum distance classifier and further compared with the discriminating ability of the same texture features extracted from the first post-contrast frame. Selected texture features extracted from the SER map achieved an area under receiver operating characteristic curve of 0.922 ¡ 0.029, a performance similar to post-initial enhancement map features (0.906 ¡ 0.032) and statistically significantly higher than for initial enhancement map (0.767 ¡ 0.053) and first post-contrast frame (0.756 ¡ 0.060) features. Quantifying the heterogeneity of parametric maps that reflect lesion washout properties could contribute to the computer-aided diagnosis of breast lesions in DCE-MRI. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) significantly complements mammography and is characterised by its high sensitivity in detecting breast cancer. However, its specificity in distinguishing malignant from benign lesions is highly varied: reported values range from 37 up to 90% [1]. This variation is mainly due to the variety of image acquisition protocols and interpretation schemes adopted in clinical practice [2].Diagnostic criteria in DCE-MRI of breast masses [3, 4] include assessment of morphological features such as lesion shape, margin and enhancement homogeneity (internal architecture), as well as analysis/assessment of signal intensity-time curves generated from manually selected regions of interest (ROIs) within the lesion area. The analysis of signal intensity-time curves can be performed qualitatively (i.e. visual inspection of the curve shape [5,6]), by means of empirical parameters (e.g. relative enhancement, time-to-peak enhancement, washout ratio [7]) or quantitatively through pharmacokinetic modelling techniques [8].The subjective selection of ROI within the lesion accounts for the increased intra-and interobserver variability in the interpretation of lesion enhancement kinetics and for the discrepancy of reported findings [9,10]. While the selection of an ROI that captures the entire lesion is less subjective [11], it provides average enhancement kinetics estimates and completely ignores the heterogeneity of tumour vascular characteristics, which is diagnostically important [12]. Pixel-wise analysis of enhancement kinetics (based either on pharmacokinetic ...

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confidence: 99%

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confidence: 99%

Assessing heterogeneity of lesion enhancement kinetics in dynamic contrast-enhanced MRI for breast cancer diagnosis

Karahaliou¹,

Vassiou²,

Arikidis³

et al. 2010

BJR

117

110

View full text Add to dashboard Cite

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“…Specifically, it shows how often pairs of voxels with specific gray values occurred in the specific spatial relationship. One may refer to the related paper 28 for more detail about our texture descriptors. These additional image features were used to compare the classification performance of the curvature measures introduced by this study.…”

Section: D Additional Image Features For Breast Tumor Classificationmentioning

confidence: 99%

Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT

et al. 2015

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Purpose: The purpose of this study is to measure the effectiveness of local curvature measures as novel image features for classifying breast tumors. Methods: A total of 119 breast lesions from 104 noncontrast dedicated breast computed tomography images of women were used in this study. Volumetric segmentation was done using a seed-based segmentation algorithm and then a triangulated surface was extracted from the resulting segmentation. Total, mean, and Gaussian curvatures were then computed. Normalized curvatures were used as classification features. In addition, traditional image features were also extracted and a forward feature selection scheme was used to select the optimal feature set. Logistic regression was used as a classifier and leave-one-out cross-validation was utilized to evaluate the classification performances of the features. The area under the receiver operating characteristic curve (AUC, area under curve) was used as a figure of merit. Results: Among curvature measures, the normalized total curvature (C T ) showed the best classification performance (AUC of 0.74), while the others showed no classification power individually. Five traditional image features (two shape, two margin, and one texture descriptors) were selected via the feature selection scheme and its resulting classifier achieved an AUC of 0.83. Among those five features, the radial gradient index (RGI), which is a margin descriptor, showed the best classification performance (AUC of 0.73). A classifier combining RGI and C T yielded an AUC of 0.81, which showed similar performance (i.e., no statistically significant difference) to the classifier with the above five traditional image features. Additional comparisons in AUC values between classifiers using different combinations of traditional image features and C T were conducted. The results showed that C T was able to replace the other four image features for the classification task. Conclusions: The normalized curvature measure contains useful information in classifying breast tumors. Using this, one can reduce the number of features in a classifier, which may result in more robust classifiers for different datasets. C 2015 American Association of Physicists in Medicine.

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“…It has long been recognized that texture features play an important role in a wide variety of computer vision, image analysis and pattern recognition applications. In particular, texture features have been extensively used in medical images to quantify image properties such as homogeneity, contrast, and regularity [10][11][12].…”

Section: Texture Feature Extractionmentioning

confidence: 99%

Automatic brain MR image denoising based on texture feature-based artificial neural networks

Chang

2015

BME

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Abstract. Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

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Volumetric texture analysis of breast lesions on contrast‐enhanced magnetic resonance images

Cited by 266 publications

References 23 publications

Assessing heterogeneity of lesion enhancement kinetics in dynamic contrast-enhanced MRI for breast cancer diagnosis

Assessing heterogeneity of lesion enhancement kinetics in dynamic contrast-enhanced MRI for breast cancer diagnosis

Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT

Automatic brain MR image denoising based on texture feature-based artificial neural networks

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