[show abstract][hide abstract] ABSTRACT: Endovascular image-guided interventions (EIGI) involve navigation of a catheter through the vasculature followed by application of treatment at the site of anomaly using live 2D projection images for guidance. 3D images acquired prior to EIGI are used to quantify the vascular anomaly and plan the intervention. If fused with the information of live 2D images they can also facilitate navigation and treatment. For this purpose 3D- 2D image registration is required. Although several 3D-2D registration methods for EIGI achieve registration accuracy below 1 mm, their clinical application is still limited by insufficient robustness or reliability. In this paper, we propose a 3D-2D registration method based on matching a 3D vasculature model to intensity gradients of live 2D images. To objectively validate 3D-2D registration methods, we acquired a clinical image database of ten patients undergoing cerebral EIGI and established ¿gold standard¿ registrations by aligning fiducial markers in 3D and 2D images. The proposed method had mean registration accuracy below 0.65 mm, which was comparable to tested state-of-the-art methods, and execution time below 1 second. With the highest rate of successful registrations and the highest capture range the proposed method was the most robust and thus a good candidate for application in EIGI.
[show abstract][hide abstract] ABSTRACT: A novel game-theoretic framework for landmark-based image segmentation is presented. Landmark detection is formulated as a game, in which landmarks are players, landmark candidate points are strategies, and likelihoods that candidate points represent landmarks are payoffs, determined according to the similarity of image intensities and spatial relationships between the candidate points in the target image and their corresponding landmarks in images from the training set. The solution of the formulated game-theoretic problem is the equilibrium of candidate points that represent landmarks in the target image and is obtained by a novel iterative scheme that solves the segmentation problem in polynomial time. The object boundaries are finally extracted by applying dynamic programming to the optimal path searching problem between the obtained adjacent landmarks. The performance of the proposed framework was evaluated for segmentation of lung fields from chest radiographs and heart ventricles from cardiac magnetic resonance cross sections. The comparison to other landmark-based segmentation techniques shows that the results obtained by the proposed game-theoretic framework are highly accurate and precise in terms of mean boundary distance and area overlap. Moreover, the framework overcomes several shortcomings of the existing techniques, such as sensitivity to initialization and convergence to local optima.
IEEE transactions on medical imaging. 06/2012; 31(9):1761-1776.
[show abstract][hide abstract] ABSTRACT: Groupwise registration is concerned with bringing a group of images into the best spatial alignment. If images in the group are from different modalities, then the intensity correspondences across the images can be modeled by the joint density function (JDF) of the cooccurring image intensities. We propose a so-called treecode registration method for groupwise alignment of multimodal images that uses a hierarchical intensity-space subdivision scheme through which an efficient yet sufficiently accurate estimation of the (high-dimensional) JDF based on the Parzen kernel method is computed. To simultaneously align a group of images, a gradient-based joint entropy minimization was employed that also uses the same hierarchical intensity-space subdivision scheme. If the Hilbert kernel is used for the JDF estimation, then the treecode method requires no data-dependent bandwidth selection and is thus fully automatic. The treecode method was compared with the ensemble clustering (EC) method on four different publicly available multimodal image data sets and on a synthetic monomodal image data set. The obtained results indicate that the treecode method has similar and, for two data sets, even superior performances compared to the EC method in terms of registration error and success rate. The obtained good registration performances can be mostly attributed to the sufficiently accurate estimation of the JDF, which is computed through the hierarchical intensity-space subdivision scheme, that captures all the important features needed to detect the correct intensity correspondences across a multimodal group of images undergoing registration.
[show abstract][hide abstract] ABSTRACT: Construction of a standardized near infrared (NIR) hyper-spectral teeth database is a first step in the development of a reliable diagnostic tool for quantification and early detection of dental diseases. The standardized diffuse reflectance hyper-spectral database was constructed by imaging 12 extracted human teeth with natural lesions of various degrees in the spectral range from 900 to 1700 nm with spectral resolution of 10 nm. Additionally, all the teeth were imaged by X-ray and digital color camera. The color and X-ray teeth images were presented to the expert for localization and classification of the dental diseases, thereby obtaining a dental disease gold standard. Accurate transfer of the dental disease gold standard to the NIR images was achieved by image registration in a groupwise manner, taking advantage of the multichannel image information and promoting image edges as the features for the improvement of spatial correspondence detection. By the presented fully automatic multi-modal groupwise registration method, images of new teeth samples can be accurately and reliably registered and then added to the standardized NIR hyper-spectral teeth database. Adding more samples increases the biological and patho-physiological variability of the NIR hyper-spectral teeth database and can importantly contribute to the objective assessment of the sensitivity and specificity of multivariate image analysis techniques used for the detection of dental diseases. Such assessment is essential for the development and validation of reliable qualitative and especially quantitative diagnostic tools based on NIR spectroscopy.
[show abstract][hide abstract] ABSTRACT: Medical image segmentation is typically used to locate boundaries of anatomical structures in images acquired by different modalities. As segmentation is of utmost importance for quantitative measurements and analysis of anatomical structures, tracking anatomical changes over time, building anatomical atlases and visualization of medical images, a huge amount of methods have been developed and tested on a wide range of applications in the past. Deformable or parametric shape models are a class of methods that have been widely used for segmentation. A drawback of deformable model approaches it that they require initialization near the final solution. In this paper, we present a segmentation algorithm that incorporates prior knowledge and is composed of two steps. First, reference points on the boundary of an anatomical structure are found by linear programming incorporating prior knowledge. Second, paths between reference points, representing boundary segments, are searched for by optimal control. The segmentation method has been applied to chest radiographs from the publicly available SCR database.
[show abstract][hide abstract] ABSTRACT: In this article, the authors propose a new gold standard data set for the validation of two-dimensional/three-dimensional (2D/3D) and 3D/3D image registration algorithms.
A gold standard data set was produced using a fresh cadaver pig head with attached fiducial markers. The authors used several imaging modalities common in diagnostic imaging or radiotherapy, which include 64-slice computed tomography (CT), magnetic resonance imaging using T1, T2, and proton density sequences, and cone beam CT imaging data. Radiographic data were acquired using kilovoltage and megavoltage imaging techniques. The image information reflects both anatomy and reliable fiducial marker information and improves over existing data sets by the level of anatomical detail, image data quality, and soft-tissue content. The markers on the 3D and 2D image data were segmented using ANALYZE 10.0 (AnalyzeDirect, Inc., Kansas City, KN) and an in-house software.
The projection distance errors and the expected target registration errors over all the image data sets were found to be less than 2.71 and 1.88 mm, respectively.
The gold standard data set, obtained with state-of-the-art imaging technology, has the potential to improve the validation of 2D/3D and 3D/3D registration algorithms for image guided therapy.
Medical Physics 03/2011; 38(3):1481-90. · 2.91 Impact Factor
[show abstract][hide abstract] ABSTRACT: A new gold standard data set for validation of 2D/3D registration based on a porcine cadaver head with attached fiducial markers was presented in the first part of this article. The advantage of this new phantom is the large amount of soft tissue, which simulates realistic conditions for registration. This article tests the performance of intensity- and gradient-based algorithms for 2D/3D registration using the new phantom data set.
Intensity-based methods with four merit functions, namely, cross correlation, rank correlation, correlation ratio, and mutual information (MI), and two gradient-based algorithms, the backprojection gradient-based (BGB) registration method and the reconstruction gradient-based (RGB) registration method, were compared. Four volumes consisting of CBCT with two fields of view, 64 slice multidetector CT, and magnetic resonance-T1 weighted images were registered to a pair of kV x-ray images and a pair of MV images. A standardized evaluation methodology was employed. Targets were evenly spread over the volumes and 250 starting positions of the 3D volumes with initial displacements of up to 25 mm from the gold standard position were calculated. After the registration, the displacement from the gold standard was retrieved and the root mean square (RMS), mean, and standard deviation mean target registration errors (mTREs) over 250 registrations were derived. Additionally, the following merit properties were computed: Accuracy, capture range, number of minima, risk of nonconvergence, and distinctiveness of optimum for better comparison of the robustness of each merit.
Among the merit functions used for the intensity-based method, MI reached the best accuracy with an RMS mTRE down to 1.30 mm. Furthermore, it was the only merit function that could accurately register the CT to the kV x rays with the presence of tissue deformation. As for the gradient-based methods, BGB and RGB methods achieved subvoxel accuracy (RMS mTRE down to 0.56 and 0.70 mm, respectively). Overall, gradient-based similarity measures were found to be substantially more accurate than intensity-based methods and could cope with soft tissue deformation and enabled also accurate registrations of the MR-T1 volume to the kV x-ray image.
In this article, the authors demonstrate the usefulness of a new phantom image data set for the evaluation of 2D/3D registration methods, which featured soft tissue deformation. The author's evaluation shows that gradient-based methods are more accurate than intensity-based methods, especially when soft tissue deformation is present. However, the current nonoptimized implementations make them prohibitively slow for practical applications. On the other hand, the speed of the intensity-based method renders these more suitable for clinical use, while the accuracy is still competitive.
Medical Physics 03/2011; 38(3):1491-502. · 2.91 Impact Factor
[show abstract][hide abstract] ABSTRACT: Despite major improvements in dental healthcare and technology, dental caries remains one of the most prevalent chronic diseases of modern society. The initial stages of dental caries are characterized by demineralization of enamel crystals, commonly known as white spots which are difficult to diagnose. If detected early enough, such demineralization can be arrested and reversed by non-surgical means through well established dental treatments (fluoride therapy, anti-bacterial therapy, low intensity laser irradiation). Near-infrared (NIR) hyper-spectral imaging is a new promising technique for early detection of demineralization based on distinct spectral features of healthy and pathological dental tissues. In this study, we apply NIR hyper-spectral imaging to classify and visualize healthy and pathological dental tissues including enamel, dentin, calculus, dentin caries, enamel caries and demineralized areas. For this purpose, a standardized teeth database was constructed consisting of 12 extracted human teeth with different degrees of natural dental lesions imaged by NIR hyper-spectral system, X-ray and digital color camera. The color and X-ray images of teeth were presented to a clinical expert for localization and classification of the dental tissues, thereby obtaining the gold standard. Principal component analysis was used for multivariate local modeling of healthy and pathological dental tissues. Finally, the dental tissues were classified by employing multiple discriminant analysis. High agreement was observed between the resulting classification and the gold standard with the classification sensitivity and specificity exceeding 85 % and 97 %, respectively. This study demonstrates that NIR hyper-spectral imaging has considerable diagnostic potential for imaging hard dental tissues.
[show abstract][hide abstract] ABSTRACT: The evaluation of vertebral deformations is of great importance in clinical diagnostics and therapy of pathological conditions affecting the spine. Although modern clinical practice is oriented towards the computed tomography (CT) and magnetic resonance (MR) imaging techniques, as they can provide a detailed D representation of vertebrae, the established methods for the evaluation of vertebral deformations still provide only a two-dimensional (2D) geometrical description. Segmentation of vertebrae in 3D may therefore not only improve their visualization, but also provide reliable and accurate 3D measurements of vertebral deformations. In this paper we propose a method for D segmentation of individual vertebral bodies that can be performed in CT and MR images. Initialized with a single point inside the vertebral body, the segmentation is performed by optimizing the parameters of a 3D deterministic model of the vertebral body to achieve the best match of the model to the vertebral body in the image. The performance of the proposed method was evaluated on five CT (40 vertebrae) and five T2-weighted MR (40 vertebrae) spine images, among them five are normal and five are pathological. The results show that the proposed method can be used for D segmentation of vertebral bodies in CT and MR images and that the proposed model can describe a variety of vertebral body shapes. The method may be therefore used for initializing whole vertebra segmentation or reliably describing vertebral body deformations.
[show abstract][hide abstract] ABSTRACT: The vertebral pose in three dimensions (3D) may provide valuable information for quantitative clinical measurements or aid the initialization of image analysis techniques. We propose a method for automated determination of the vertebral pose in 3D that, in an iterative registration scheme, estimates the position and rotation of the vertebral coordinate system in 3D images. By searching for the hypothetical points, which are located where the boundaries of anatomical structures would have maximal symmetrical correspondences when mirrored over the vertebral planes, the asymmetry of vertebral anatomical structures is minimized. The method was evaluated on 14 normal and 14 scoliotic vertebrae in images acquired by computed tomography (CT). For each vertebra, 1000 randomly initialized experiments were performed. The results show that the vertebral pose can be successfully determined in 3D with mean accuracy of 0.5mm and 0.6° and mean precision of 0.17mm and 0.17. according to the 3D position and 3D rotation, respectively.
[show abstract][hide abstract] ABSTRACT: Dental caries is a disease characterized by demineralization of enamel crystals leading to the penetration of bacteria into the dentin and pulp. If left untreated, the disease can lead to pain, infection and tooth loss. Early detection of enamel demineralization resulting in increased enamel porosity, commonly known as white spots, is a difficult diagnostic task. Several papers reported on near infrared (NIR) spectroscopy to be a potentially useful noninvasive spectroscopic technique for early detection of caries lesions. However, the conducted studies were mostly qualitative and did not include the critical assessment of the spectral variability of the sound and carious dental tissues and influence of the water content. Such assessment is essential for development and validation of reliable qualitative and especially quantitative diagnostic tools based on NIR spectroscopy. In order to characterize the described spectral variability, a standardized diffuse reflectance hyper-spectral database was constructed by imaging 12 extracted human teeth with natural lesions of various degrees in the spectral range from 900 to 1700 nm with spectral resolution of 10 nm. Additionally, all the teeth were imaged by digital color camera. The influence of water content on the acquired spectra was characterized by monitoring the teeth during the drying process. The images were assessed by an expert, thereby obtaining the gold standard. By analyzing the acquired spectra we were able to accurately model the spectral variability of the sound dental tissues and identify the advantages and limitations of NIR hyper-spectral imaging.
[show abstract][hide abstract] ABSTRACT: Near-infrared hyperspectral imaging is becoming a popular tool in the biomedical field, especially for detection and analysis of different types of cancers, analysis of skin burns and bruises, imaging of blood vessels and for many other applications. As in all imaging systems, proper illumination is crucial to attain optimal image quality that is needed for best performance of image analysis algorithms. In hyperspectral imaging based on filters (AOTF, LCTF and filter wheel) the acquired spectral signature has to be representative in all parts of the imaged object. Therefore, the whole object must be equally well illuminated - without shadows and specular reflections. As there are no restrictions imposed on the material and geometry of the object, the desired object illumination can only be achieved with completely diffuse illumination. In order to minimize shadows and specular reflections in diffuse illumination the light illuminating the object must be spatially, angularly and spectrally uniform. We present and test two diffuse illumination system designs that try to achieve optimal uniformity of the above mentioned properties. The illumination uniformity properties were measured with an AOTF based hyperspectral imaging system utilizing a standard white diffuse reflectance target and a specially designed calibration target for estimating the spatial and angular illumination uniformity.
[show abstract][hide abstract] ABSTRACT: In hyper-spectral imaging systems with a wide spectral range, axial optical aberrations may lead to a significant blurring of image intensities in certain parts of the spectral range. Axial optical aberrations arise from the indexof- refraction variations that is dependent on the wavelength of incident light. To correct axial optical aberrations the point-spread function (PSF) of the image acquisition system needs to be identified. We proposed a multiframe joint blur identification and image restoration method that maximizes the likelihood of local image energy distributions between spectral images. Gaussian mixture model based density estimate provides a link between corresponding spatial information shared among spectral images so as to find and restore the image edges via a PSF update. Model of the PSF was assumed to be a linear combination of Gaussian functions, therefore the blur identification process had to find only the corresponding scalar weights of each Gaussian function. Using the identified PSF, image restoration was performed by the iterative Richardson-Lucy algorithm. Experiments were conducted on four different biological samples using a hyper-spectral imaging system based on acousto-optic tunable filter in the visible spectral range (0.55 - 1.0 mum). By running the proposed method, the quality of raw spectral images was substantially improved. Image quality improvements were quantified by a measure of contrast and demonstrate the potential of the proposed method for the correction of axial optical aberrations.
[show abstract][hide abstract] ABSTRACT: A growing number of clinical applications using 2D/3D registration have been presented recently. Usually, a digitally reconstructed radiograph is compared iteratively to an x-ray image of the known projection geometry until a match is achieved, thus providing six degrees of freedom of rigid motion which can be used for patient setup in image-guided radiation therapy or computer-assisted interventions. Recently, stochastic rank correlation, a merit function based on Spearman's rank correlation coefficient, was presented as a merit function especially suitable for 2D/3D registration. The advantage of this measure is its robustness against variations in image histogram content and its wide convergence range. The considerable computational expense of computing an ordered rank list is avoided here by comparing randomly chosen subsets of the DRR and reference x-ray. In this work, we show that it is possible to omit the sorting step and to compute the rank correlation coefficient of the full image content as fast as conventional merit functions. Our evaluation of a well-calibrated cadaver phantom also confirms that rank correlation-type merit functions give the most accurate results if large differences in the histogram content for the DRR and the x-ray image are present.
Physics in Medicine and Biology 10/2010; 55(19):N465-71. · 2.70 Impact Factor
[show abstract][hide abstract] ABSTRACT: Pellet coating processes are usually driven by fairly well optimized procedures, while coating suspension sprayed on pellets and adverse effects, such as agglomeration, can not be seen during coating process and are only detected at the very end of the process, when it is too late for any adjustments of the coating process. The aim of this study is to evaluate digital visual imaging as process analytical technology (PAT) tool for fluid-bed pellet coating processes. The method accurately estimates spherical diameter, coating thickness and adverse agglomeration of pellets by contactless measurements, classification and analysis of pellets based on digital imaging. Calibration and thorough assessment of the accuracy, precision, stability and speed of the proposed method was performed with high precision bearing balls. The obtained results on real pellets indicated that the method is feasible for real-time controlling, understanding, designing and optimizing of fluid-bed pellet coating processes according to PAT guidance.
European journal of pharmaceutical sciences: official journal of the European Federation for Pharmaceutical Sciences 09/2010; 41(1):156-62. · 2.61 Impact Factor
[show abstract][hide abstract] ABSTRACT: A new image database with a reference-based standardized evaluation methodology for objective evaluation and comparison of three-dimensional/two-dimensional (3D/2D) registration methods has been introduced.
Computed tomography (CT) images of a male and female from the Visible Human Project were used and 16 subvolumes, each containing one of vertebrae T3-T12 and L1-L5 and the pelvis, were defined from the CTs. Six pairs of 2D fluoroscopic x-ray images from different views, showing the thoracic, lumbar, and pelvic regions, were rendered from the CT data using a ray-casting algorithm with an energy conversion function. Furthermore, a single 13-gauge needle was analytically simulated and projected onto the 2D images. By the novel standardized evaluation methodology, a 3D/2D registration method is evaluated by four evaluation criteria: Accuracy, reliability, robustness, and algorithm complexity.
To demonstrate the usefulness of the proposed data set and the standardized evaluation methodology, a part of the data set was used in an evaluation study of two gradient-based 3D/2D registration methods. It was shown that the use of a failure criterion to calculate the registration accuracy and reliability is not required, since all the information about a registration method can be determined from the estimated distribution of registration errors.
The proposed simulated image data set with quite realistic synthetic 2D images, depicting soft tissues and outliers, is especially suitable for preliminary testing of 3D/2D registration algorithms. Since the aim of this article is to provide objective comparison and unbiased evaluation of 3D/2D registration methods, the standardized evaluation methodology is available upon request from the authors.
Medical Physics 09/2010; 37(9):4643-7. · 2.91 Impact Factor