Feature-based registration algorithms can be used to establish spatial correspondence between two image. Therefore, anatomical landmarks such as the breast boundary, pectoral muscle, nipple, duct and vessels need to be considered. The aim of this paper is to introduce a new approach which combine the pectoral muscle segmentation and nipple location, considering mammography quality assumptions. Pectoral muscle is initialized as a straight line from the top of the image to the nipple level. Afterwards, both pectoral muscle boundary and nipple position are optimized using an iterative approach. The results show that the nipple is localized on the contour of the corresponding area (error smaller than 10 mm) while the Dice’s coefficient of the pectoral muscle segmentation is equal to 0.84 ± 0.12 using a straight line which is improved using a Chan-Vese active contour approach, reaching 0.87 ± 0.13. Our algorithm is easily generalized and portable to a different mammographic system since it barely depends on images statistics -i.e. pixel intensity values-, and is just based on geometrical considerations.
Patient-specific finite element (FE) models of the breast have received increasing attention due to the potential capability of fusing information from different image modalities. During the Magnetic Resonance Imaging (MRI) to X-ray mammography (MG) registration procedure, a FE model is compressed mimicking the mammographic acquisition. To develop an accurate model of the breast, the elastic properties and stress-strain relationship of breast tissues need to be properly defined. Several studies (in vivo and ex vivo experiments) have proposed a range of values associated to the mechanical properties of different tissues. This work analyse the elastic parameters (Young Modulus and Poisson ratio) obtained during the process of registering MRI to X-ray MG images. Position, orientation, elastic parameters and amount of compression are optimised using a simulated annealing algorithm, until the biomechanical model reaches a suitable position with respect to the corresponding mammogram. FE models obtained from 29 patients, 46 MRI-MG studies, were used to extract the optimal elastic parameters for breast compression. The optimal Young modulus obtained in the entire dataset correspond to 4.46 ± 1.81 kP a for adipose and 16.32 ± 8.36 kP a for glandular tissue, while the average Poisson ratio was 0.0492 ± 0.004. Furthermore, we did not find a correlation between the elastic parameters and other patient-specific factors such as breast density or patient age.
Breast density is an important risk factor for the development of breast cancer. During the women lifetime, the breast glandularity varies due to hormonal changes. In particular, around menopause, the glandular tissue tends to decrease. The aim of this paper is to evaluate temporal breast density changes using density maps, provided by the commercial software VolparaTM. The dataset is composed of 563 mammograms from 55 patients (aged between 24 and 75 years old). The time frame between two acquisitions varies from less than one year to 4 years. Pairs of mammograms are registered using the morphons registration algorithm, in order to evaluate the structural similarity of the parenchymal distribution between the two acquisitions. To provide a fair comparison, the results are divided considering the patient age during the first mammographic acquisition and the time between the two studies. To evaluate the changes in breast density, local and global measures, such as the rate of change of the volumetric breast density, the histogram intersection between two density maps and the normalized cross-correlation after the registration, are considered. The results show significant differences in the statistics, mainly focused on patients younger than 30 years old and ranged between 56 and 65 years old with respect to those in the adulthood (between 30 and 55 years old). Similarly, the time between the two mammographic acquisitions shows a significant difference for patients older than 56 years old considering one and two year of difference between the two studies.
KEYWORDS: Scattering, Breast, Digital breast tomosynthesis, Monte Carlo methods, Signal detection, Sensors, Breast imaging, Particles, Digital mammography, Visibility, Detection and tracking algorithms, Photons, Image compression, Chest, Tissues, X-rays
Scattered radiation is an undesired signal largely present in most digital breast tomosynthesis (DBT) projection images as no physically rejection methods, i.e. anti-scatter grids, are regularly employed, in contrast to full- field digital mammography. This scatter signal might reduce the visibility of small objects in the image, and potentially affect the detection of small breast lesions. Thus accurate scatter models are needed to minimise the scattered radiation signal via post-processing algorithms. All prior work on scattered radiation estimation has assumed a rigid breast compression paddle (RP) and reported large contribution of scatter signal from RP in the detector. However, in this work, flexible paddles (FPs) tilting from 0° to 10° will be studied using Monte Carlo simulations to analyse if the scatter distribution differs from RP geometries. After reproducing the Hologic Selenia Dimensions geometry (narrow angle) with two (homogeneous and heterogeneous) compressed breast phantoms, results illustrate that the scatter distribution recorded at the detector varies up to 22% between RP and FP geometries (depending on the location), mainly due to the decrease in thickness of the breast observed for FP. However, the relative contribution from the paddle itself (3-12% of the total scatter) remains approximately unchanged for both setups and their magnitude depends on the distance to the breast edge.
KEYWORDS: Magnetic resonance imaging, 3D modeling, Breast, Mammography, Image registration, Finite element methods, X-rays, X-ray imaging, 3D acquisition, Tissues
Patient-specific finite element (FE) models of the breast have received increasing attention due to the potential capability of fusing images from different modalities. During the Magnetic Resonance Imaging (MRI) to X-ray mammography registration procedure, the FE model is compressed mimicking the mammographic acquisition. Subsequently, suspicious lesions in the MRI volume can be projected into the 2D mammographic space. However, most registration algorithms do not provide the reverse information, avoiding to obtain the 3D geometrical information from the lesions localized in the mammograms. In this work we introduce a fast method to localize the 3D position of the lesion within the MRI, using both cranio-caudal (CC) and medio-lateral oblique (MLO) mammographic projections, indexing the tetrahedral elements of the biomechanical model by means of an uniform grid. For each marked lesion in the Full-Field Digital Mammogram (FFDM), the X-ray path from source to the marker is calculated. Barycentric coordinates are computed in the tetrahedrons traversed by the ray. The list of elements and coordinates allows to localize two curves within the MRI and the closest point between both curves is taken as the 3D position of the lesion. The registration errors obtained in the mammographic space are 9.89 ± 3.72 mm in CC- and 8.04 ± 4.68 mm in MLO-projection and the error in the 3D MRI space is equal to 10.29 ± 3.99 mm. Regarding the uniform grid, it is computed spending between 0.1 and 0.7 seconds. The average time spent to compute the 3D location of a lesion is about 8 ms.
Automated three-dimensional breast ultrasound (ABUS) is a valuable adjunct to x-ray mammography for breast cancer screening of women with dense breasts. High image quality is essential for proper diagnostics and computer-aided detection. We propose an automated image quality assessment system for ABUS images that detects artifacts at the time of acquisition. Therefore, we study three aspects that can corrupt ABUS images: the nipple position relative to the rest of the breast, the shadow caused by the nipple, and the shape of the breast contour on the image. Image processing and machine learning algorithms are combined to detect these artifacts based on 368 clinical ABUS images that have been rated manually by two experienced clinicians. At a specificity of 0.99, 55% of the images that were rated as low quality are detected by the proposed algorithms. The areas under the ROC curves of the single classifiers are 0.99 for the nipple position, 0.84 for the nipple shadow, and 0.89 for the breast contour shape. The proposed algorithms work fast and reliably, which makes them adequate for online evaluation of image quality during acquisition. The presented concept may be extended to further image modalities and quality aspects.
Purpose: Magnetic resonance imaging is nowadays the hallmark to diagnose multiple sclerosis (MS), characterized by white matter lesions. Several approaches have been recently presented to tackle the lesion segmentation problem, but none of them have been accepted as a standard tool in the daily clinical practice. In this work we present yet another tool able to automatically segment white matter lesions outperforming the current-state-of-the-art approaches. Methods: This work is an extension of Roura et al. [1], where external and platform dependent pre-processing libraries (brain extraction, noise reduction and intensity normalization) were required to achieve an optimal performance. Here we have updated and included all these required pre-processing steps into a single framework (SPM software). Therefore, there is no need of external tools to achieve the desired segmentation results. Besides, we have changed the working space from T1w to FLAIR, reducing interpolation errors produced in the registration process from FLAIR to T1w space. Finally a post-processing constraint based on shape and location has been added to reduce false positive detections. Results: The evaluation of the tool has been done on 24 MS patients. Qualitative and quantitative results are shown with both approaches in terms of lesion detection and segmentation. Conclusion: We have simplified both installation and implementation of the approach, providing a multiplatform tool1 integrated into the SPM software, which relies only on using T1w and FLAIR images. We have reduced with this new version the computation time of the previous approach while maintaining the performance.
This paper presents a method based on shape-context and statistical measures to match interventional 2D Trans
Rectal Ultrasound (TRUS) slice during prostate biopsy to a 2D Magnetic Resonance (MR) slice of a pre-acquired
prostate volume. Accurate biopsy tissue sampling requires translation of the MR slice information on the TRUS
guided biopsy slice. However, this translation or fusion requires the knowledge of the spatial position of the
TRUS slice and this is only possible with the use of an electro-magnetic (EM) tracker attached to the TRUS
probe. Since, the use of EM tracker is not common in clinical practice and 3D TRUS is not used during biopsy,
we propose to perform an analysis based on shape and information theory to reach close enough to the actual
MR slice as validated by experts. The Bhattacharyya distance is used to find point correspondences between
shape-context representations of the prostate contours. Thereafter, Chi-square distance is used to find out those
MR slices where the prostates closely match with that of the TRUS slice. Normalized Mutual Information (NMI)
values of the TRUS slice with each of the axial MR slices are computed after rigid alignment and consecutively a
strategic elimination based on a set of rules between the Chi-square distances and the NMI leads to the required
MR slice. We validated our method for TRUS axial slices of 15 patients, of which 11 results matched at least
one experts validation and the remaining 4 are at most one slice away from the expert validations.
KEYWORDS: 3D modeling, Image segmentation, Prostate, Data modeling, Magnetic resonance imaging, Image registration, 3D image processing, Statistical modeling, Affine motion model, Principal component analysis
Real-time fusion of Magnetic Resonance (MR) and Trans Rectal Ultra Sound (TRUS) images aid in the localization
of malignant tissues in TRUS guided prostate biopsy. Registration performed on segmented contours of
the prostate reduces computational complexity and improves the multimodal registration accuracy. However,
accurate and computationally efficient 3D segmentation of the prostate in MR images could be a challenging
task due to inter-patient shape and intensity variability of the prostate gland. In this work, we propose to
use multiple statistical shape and appearance models to segment the prostate in 2D and a global registration
framework to impose shape restriction in 3D. Multiple mean parametric models of the shape and appearance
corresponding to the apex, central and base regions of the prostate gland are derived from principal component
analysis (PCA) of prior shape and intensity information of the prostate from the training data. The estimated
parameters are then modified with the prior knowledge of the optimization space to achieve segmentation in 2D.
The 2D segmented slices are then rigidly registered with the average 3D model produced by affine registration
of the ground truth of the training datasets to minimize pose variations and impose 3D shape restriction. The
proposed method achieves a mean Dice similarity coefficient (DSC) value of 0.88±0.11, and mean Hausdorff
distance (HD) of 3.38±2.81 mm when validated with 15 prostate volumes of a public dataset in leave-one-out
validation framework. The results achieved are better compared to some of the works in the literature.
KEYWORDS: Image segmentation, Prostate, Signal to noise ratio, Binary data, Tissues, Principal component analysis, Speckle, Image registration, Magnetic resonance imaging, Data modeling
Real-time fusion of Magnetic Resonance (MR) and Trans Rectal Ultra Sound (TRUS) images aid in the localization
of malignant tissues in TRUS guided prostate biopsy. Registration performed on segmented contours of
the prostate reduces computational complexity and improves the multimodal registration accuracy. However,
accurate and computationally efficient segmentation of the prostate in TRUS images could be challenging in
the presence of heterogeneous intensity distribution inside the prostate gland, and other imaging artifacts like
speckle noise, shadow regions and low Signal to Noise Ratio (SNR). In this work, we propose to enhance the
texture features of the prostate region using Local Binary Patterns (LBP) for the propagation of a shape and
appearance based statistical model to segment the prostate in a multi-resolution framework. A parametric model
of the propagating contour is derived from Principal Component Analysis (PCA) of the prior shape and texture
information of the prostate from the training data. The estimated parameters are then modified with the prior
knowledge of the optimization space to achieve an optimal segmentation. The proposed method achieves a mean
Dice Similarity Coefficient (DSC) value of 0.94±0.01 and a mean segmentation time of 0.68±0.02 seconds when
validated with 70 TRUS images of 7 datasets in a leave-one-patient-out validation framework. Our method performs
computationally efficient and accurate prostate segmentation in the presence of intensity heterogeneities
and imaging artifacts.
This paper provides a comparison of spline-based registration methods applied to register interventional Trans Rectal Ultrasound (TRUS) and pre-acquired Magnetic Resonance (MR) prostate images for needle guided prostate biopsy. B-splines and Thin-plate Splines (TPS) are the most prevalent spline-based approaches to achieve deformable registration. Pertaining to the strategic selection of correspondences for the TPS registration, we use an automatic method already proposed in our previous work to generate correspondences in the MR and US prostate images. The method exploits the prostate geometry with the principal components of the segmented prostate as the underlying framework and involves a triangulation approach. The correspondences are generated with successive refinements and Normalized Mutual Information (NMI) is employed to determine the
optimal number of correspondences required to achieve TPS registration. B-spline registration with successive grid refinements are consecutively applied for a significant comparison of the impact of the strategically chosen correspondences on the TPS registration against the uniform B-spline control grids. The experimental results
are validated on 4 patient datasets. Dice Similarity Coefficient (DSC) is used as a measure of the registration accuracy. Average DSC values of 0.97±0.01 and 0.95±0.03 are achieved for the TPS and B-spline registrations respectively. B-spline registration is observed to be more computationally expensive than the TPS registration
with average execution times of 128.09 ± 21.7 seconds and
62.83 ± 32.77 seconds respectively for images with maximum width of 264 pixels and a maximum height of 211 pixels.
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