Local curvature analysis for differentiating Glioblastoma multiforme from solitary metastasis

Ambiguous imaging appearance of Glioblastoma multiforme (GBM) and solitary metastasis (MET) is a challenge to conventional Magnetic Resonance Imaging (MRI) based diagnosis. In this study, a local curvature analysis scheme is implemented to enable morphological differentiation between GBMs and METs. The first stage of the scheme takes advantage of a Diffusion Tensor Imaging (DTI) clustering segmentation technique, complemented by post-contrast T1 imaging for final tumor boundary definition. 3D tumor models are generated by morphological morphing interpolation to compensate for low z-axis resolution of a widely utilized MRI acquisition protocol, followed by triangulated surface mesh generation. Five 3D morphology descriptors, based on local curvature analysis, are tested in a pilot case of 12 lesions (8 GBMs and 4 METs) in terms of morphology differentiation capability, utilizing four first order statistics. Statistically significant differences are identified for all five descriptors tested, however for a varying first order statistics. Results demonstrate the potential of morphology analysis in pre-treatment brain MRI tumor differentiation. © 2016 IEEE

Exploiting morphology and texture of 3D tumor models in DTI for differentiating glioblastoma multiforme from solitary metastasis

Ambiguous imaging appearance of Glioblastoma Multiforme (GBM) and solitary Metastasis (MET) is a challenge to conventional Magnetic Resonance Imaging (MRI) based diagnosis, leading to exploitation of advanced MRI techniques, such as Diffusion Tensor Imaging (DTI). In this study, 3D tumor models are generated by a DTI clustering segmentation technique, providing up to 16 brain tissue diffusivities, complemented by T1 post-contrast imaging, resulting in the identification of tumor core, whose surface is refined by a Morphological Morphing interpolation technique. The 3D models are analyzed in terms of their surface and internal signal variations characteristics towards identification of discriminant features for differentiation between GBMs and METs, utilizing a case sample composed of 10 GBMs and 10 METs. Morphology analysis of tumor core surface is assessed by 5 local curvature features. Texture analysis considers 11 first and 16 second order 3D textural features. From the 16 second order features, 11 are based on Gray Level Co-Occurrence Matrices (GLCM) and 5 on Gray Level Run Length Matrices (GLRLM), calculated from DTI isotropic and anisotropic parametric maps, corresponding to 3D tumor core segmented from the clustering technique. Also, 3 different image quantization levels (QL) were tested for both GLCM and GLRLM analysis, while 1–4 pixel displacements (D) in case of GLCM analysis. Case sample distributions of morphology and texture features were analyzed using the Mann-Whitney U test, with a cut-off value of 0.05 to identify discriminant features. The discriminatory performance of the derived features was analyzed with Receiver Operating Characteristic (ROC) curve analysis. Results highlight the value of all 5 local curvature descriptors to capture differences between the boundary of GBMs and METs. Histogram analysis of isotropy maps revealed statistical significant differences for median value and kurtosis, while 7 out of the 11 GLCM features were capable of discriminating heterogeneity of anisotropic diffusion properties of GBMs and METs, at QL = 6 and D = 2. Finally, all 5 GLRLM features extracted from diffusion isotropy maps seem to discriminate structural properties of GBMs and METs, at QL = 5. Results demonstrate the potential of surface morphology and texture analysis of 3D tumor imaging appearance in pre-treatment brain MRI tumor differentiation. © 2018 Elsevier Lt

A two-stage method for microcalcification cluster segmentation in mammography by deformable models

Purpose: Segmentation of microcalcification (MC) clusters in x-ray mammography is a difficult task for radiologists. Accurate segmentation is prerequisite for quantitative image analysis of MC clusters and subsequent feature extraction and classification in computer-aided diagnosis schemes. Methods: In this study, a two-stage semiautomated segmentation method of MC clusters is investigated. The first stage is targeted to accurate and time efficient segmentation of the majority of the particles of a MC cluster, by means of a level set method. The second stage is targeted to shape refinement of selected individual MCs, by means of an active contour model. Both methods are applied in the framework of a rich scale-space representation, provided by the wavelet transform at integer scales. Segmentation reliability of the proposed method in terms of inter and intraobserver agreements was evaluated in a case sample of 80 MC clusters originating from the digital database for screening mammography, corresponding to 4 morphology types (punctate: 22, fine linear branching: 16, pleomorphic: 18, and amorphous: 24) of MC clusters, assessing radiologist's segmentations quantitatively by two distance metrics (Hausdorff distance - HDISTcluster, average of minimum distance - MINDISTcluster) and the area overlap measure (AOMcluster). The effect of the proposed segmentation method on MC cluster characterization accuracy was evaluated in a case sample of 162 pleomorphic MC clusters (72 malignant and 90 benign). Ten MC cluster features, targeted to capture morphologic properties of individual MCs in a cluster (area, major length, perimeter, compactness and spread), were extracted and a correlation-based feature selection method yielded a feature subset to feed in a support vector machine classifier. Classification performance of the MC cluster features was estimated by means of the area under receiver operating characteristic curve (Az Standard Error) utilizing tenfold cross-validation methodology. A previously developed B-spline active rays segmentation method was also considered for comparison purposes. Results: Interobserver and intraobserver segmentation agreements (median and [25%, 75%] quartile range) were substantial with respect to the distance metrics HDISTcluster (2.3 [1.8, 2.9] and 2.5 [2.1, 3.2] pixels) and AMINDISTcluster (0.8 [0.6, 1.0] and 1.0 [0.8, 1.2] pixels), while moderate with respect to AOMcluster (0.64 [0.55, 0.71] and 0.59 [0.52, 0.66]). The proposed segmentation method outperformed (0.800.04) statistically significantly (Mann-Whitney U-test, p < 0.05) the B-spline active rays segmentation method (0.690.04), suggesting the significance of the proposed semiautomated method. Conclusions: Results indicate a reliable semiautomated segmentation method for MC clusters offered by deformable models, which could be utilized in MC cluster quantitative image analysis. © 2015 American Association of Physicists in Medicine

A method for limiting pitfalls in the production of enhancement kinetic curves in 3T dynamic magnetic resonance mammography

Purpose: The aim of the present study is to investigate means for the reduction or even elimination of enhancement kinetic curve errors due to breast motion in order to avoid pitfalls and to increase the sensitivity and specificity of the method. Methods: 115 women underwent breast Magnetic Resonance Imaging (MRI). All patients were properly immobilized in a dedicated bilateral phased array coil. A magnetic resonance unit 3-Tesla (Signa, GE Healthcare) was used. The following sequences were applied: (I) axial T2-TSE, (II) axial STIR and (III) Vibrant axial T1-weighted fat saturation (six phases). Kinetic curves were derived semi-automatically using the software of the system and manually by positioning the regions of interest (ROI) from stable reference points in all the phases. Results: 376 abnormalities in 115 patients were investigated. In 81 (21.5%) cases, a change of the enhancement kinetic curve type was found when the two different methods were used. In cases of large fatty breasts, a change of the enhancement kinetic curve type in 13 lesions was found. In cases of small and dense breasts, only in 4 lesions the kinetic curve type changed, whereas in cases of small and fatty breasts, the kinetic curve type changed in 64 lesions (50 were observed in left breasts and 14 in right breasts). Conclusions: The derivation of enhancement kinetic curves should be performed by controlling and verifying that the ROIs lay at the same location of the lesion in all the phases of the dynamic study

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

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 contrast-enhanced 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