Said Benameur

3D/2D registration and segmentation of scoliotic vertebrae using statistical models

We propose a new 3D/2D registration method for vertebrae of the scoliotic spine, using two conventional radiographic views (postero-anterior and lateral), and a priori global knowledge of the geometric structure of each vertebra. This geometric knowledge is efficiently captured by a statistical deformable template integrating a set of admissible deformations, expressed by the first modes of variation in Karhunen-Loeve expansion, of the pathological deformations observed on a representative scoliotic vertebra population. The proposed registration method consists of fitting the projections of this deformable template with the preliminary segmented contours of the corresponding vertebra on the two radiographic views. The 3D/2D registration problem is stated as the minimization of a cost function for each vertebra and solved with a gradient descent technique. Registration of the spine is then done vertebra by vertebra. The proposed method efficiently provides accurate 3D reconstruction of each scoliotic vertebra and, consequently, it also provides accurate knowledge of the 3D structure of the whole scoliotic spine. This registration method has been successfully tested on several biplanar radiographic images and validated on 57 scoliotic vertebrae. The validation results reported in this paper demonstrate that the proposed statistical scheme performs better than other conventional 3D reconstruction methods.

A hierarchical statistical modeling approach for the unsupervised 3-D biplanar reconstruction of the scoliotic spine

This paper presents a new and accurate three-dimensional (3-D) reconstruction technique for the scoliotic spine from a pair of planar and conventional (postero-anterior with normal incidence and lateral) calibrated radiographic images. The proposed model uses a priori hierarchical global knowledge, both on the geometric structure of the whole spine and of each vertebra. More precisely, it relies on the specification of two 3-D statistical templates. The first, a rough geometric template on which rigid admissible deformations are defined, is used to ensure a crude registration of the whole spine. An accurate 3-D reconstruction is then performed for each vertebra by a second template on which nonlinear admissible global, as well as local deformations, are defined. Global deformations are modeled using a statistical modal analysis of the pathological deformations observed on a representative scoliotic vertebra population. Local deformations are represented by a first-order Markov process. This unsupervised coarse-to-fine 3-D reconstruction procedure leads to two separate minimization procedures efficiently solved in our application with evolutionary stochastic optimization algorithms. In this context, we compare the results obtained with a classical genetic algorithm (GA) and a recent Exploration Selection (ES) technique. This latter optimization method with the proposed 3-D reconstruction model, is tested on several pairs of biplanar radiographic images with scoliotic deformities. The experiments reported in this paper demonstrate that the discussed method is comparable in terms of accuracy with the classical computed-tomography-scan technique while being unsupervised and while requiring only two radiographic images and a lower amount of radiation for the patient.

Three-dimensional biplanar reconstruction of scoliotic rib cage using the estimation of a mixture of probabilistic prior models

In this paper, we present an original method for the three-dimensional (3-D) reconstruction of the scoliotic rib cage from a planar and a conventional pair of calibrated radiographic images (postero-anterior with normal incidence and lateral). To this end, we first present a robust method for estimating the model parameters in a mixture of probabilistic principal component analyzers (PPCA). This method is based on the stochastic expectation maximization (SEM) algorithm. Parameters of this mixture model are used to constrain the 3-D biplanar reconstruction problem of scoliotic rib cage. More precisely, the proposed PPCA mixture model is exploited for dimensionality reduction and to obtain a set of probabilistic prior models associated with each detected class of pathological deformations observed on a representative training scoliotic rib cage population. By using an appropriate likelihood, for each considered class-conditional prior model, the proposed 3-D reconstruction is stated as an energy function minimization problem, which is solved with an exploration/selection algorithm. The optimal 3-D reconstruction then corresponds to the class of deformation and parameters leading to the minimal energy. This 3-D method of reconstruction has been successfully tested and validated on a database of 20 pairs of biplanar radiographic images of scoliotic patients, yielding very promising results. As an alternative to computed tomography-scan 3-D reconstruction this scheme has the advantage of low radiation for the patient, and may also be used for diagnosis and evaluation of deformity of a scoliotic rib cage. The proposed method remains sufficiently general to be applied to other reconstruction problems for which a database of objects to be reconstructed is available (with two or more radiographic views).

A hierarchical statistical modeling approach for the unsupervised 3D reconstruction of the scoliotic spine

In this paper, we propose a new and accurate 3D reconstruction technique for the scoliotic spine from a pair planar and conventional radiographic images (postero-anterior and lateral). The proposed model uses a priori hierarchical global knowledge, both on the geometric structure of the whole spine and of each vertebra. More precisely, it relies on the specification of two 3D templates. The first, a rough geometric template on which rigid admissible deformations are defined, is used to ensure a crude registration of the whole spine. 3D reconstruction is then refined for each vertebra, by a template on which nonlinear admissible global deformations are modeled, with statistical modal analysis of the pathological deformations observed on a representative scoliotic vertebra population. This unsupervised coarse-to-fine 3D reconstruction procedure is stated as a double energy function minimization problems efficiently solved with a stochastic optimization algorithm. The proposed method, tested on several pairs of biplanar radiographic images with scoliotic deformities, is comparable in terms of accuracy with the classical CT-scan technique while being unsupervised and requiring a lower amount of radiation for the patient.

3D biplanar reconstruction of scoliotic vertebrae using statistical models

This paper presents a new 3D reconstruction method of the scoliotic vertebrae of a spine, using two conventional radiographic views (postero-anterior and lateral), and global prior knowledge on the geometrical structure of each vertebra. This geometrical knowledge is efficiently captured by a statistical deformable template integrating a set of admissible deformations, expressed by the first modes of variation in the Karhunen-Loeve expansion of the pathological deformations observed on a representative scoliotic vertebra population. The proposed reconstruction method consists in fitting the projections of this deformable template with the segmented contours of the corresponding vertebra on the two radiographic views. The 3D reconstruction problem is stated as the minimization of a cost function for each vertebra and solved with a gradient descent technique. The reconstruction of the spine is then made vertebra by vertebra. This 3D reconstruction method has been successfully tested on several biplanar radiographic images, yielding very promising results.

Superpixel and Entropy-Based Multi-atlas Fusion Framework for the Segmentation of X-ray Images

X-ray images segmentation can be useful to aid in accurate diagnosis or faithful 3D bone reconstruction but remains a challenging and complex task, particularly when dealing with large and complex anatomical structures such as the human pelvic bone. In this paper, we propose a multi-atlas fusion framework to automatically segment the human pelvic structure from 45 or 135-degree oblique X-ray radiographic images. Unlike most atlas-based approach, this method combines a data set of a priori segmented X-ray images of the human pelvis (or multi-atlas) to generate an adaptive superpixel map in order to take efficiently into account both the imaging pose variability along with the inter-patient (bone) shape non-linear variability. In addition, we propose a new label propagation or fusion step based on the variation of information criterion for integrating the multi-atlas information into the final consensus segmentation. We thoroughly evaluated the method on 30 manually segmented 45 or 135 degree oblique X-ray radiographic images data set by performing a leave-one-out study. Compared to the manual gold standard segmentations, the accuracy of our automatic segmentation approach is \(85\%\) which remains in the error range of manual segmentations due to the inter intra/observer variability.

Image restoration using functional and anatomical information fusion with application to SPECT-MRI images

Image restoration is usually viewed as an ill-posed problem in image processing, since there is no unique solution associated with it. The quality of restored image closely depends on the constraints imposed of the characteristics of the solution. In this paper, we propose an original extension of the NAS-RIF restoration technique by using information fusion as prior information with application in SPECT medical imaging. That extension allows the restoration process to be constrained by efficiently incorporating, within the NAS-RIF method, a regularization term which stabilizes the inverse solution. Our restoration method is constrained by anatomical information extracted from a high resolution anatomical procedure such as magnetic resonance imaging (MRI). This structural anatomy-based regularization term uses the result of an unsupervised Markovian segmentation obtained after a preliminary registration step between the MRI and SPECT data volumes from each patient. This method was successfully tested on 30 pairs of brain MRI and SPECT acquisitions from different subjects and on Hoffman and Jaszczak SPECT phantoms. The experiments demonstrated that the method performs better, in terms of signal-to-noise ratio, than a classical supervised restoration approach using a Metz filter.

An homomorphic filtering and expectation maximization approach for the point spread function estimation in ultrasound imaging

In modern ultrasound imaging systems, the spatial resolution is severely limited due to the effects of both the finite aperture and overall bandwidth of ultrasound transducers and the non-negligible width of the transmitted ultrasound beams. This low spatial resolution remains the major limiting factor in the clinical usefulness of medical ultrasound images. In order to recover clinically important image details, which are often masked due to this resolution limitation, an image restoration procedure should be applied. To this end, an estimation of the Point Spread Function (PSF) of the ultrasound imaging system is required. This paper introduces a novel, original, reliable, and fast Maximum Likelihood (ML) approach for recovering the PSF of an ultrasound imaging system. This new PSF estimation method assumes as a constraint that the PSF is of known parametric form. Under this constraint, the parameter values of its associated Modulation Transfer Function (MTF) are then efficiently estimated using a homomorphic filter, a denoising step, and an expectation-maximization (EM) based clustering algorithm. Given this PSF estimate, a deconvolution can then be efficiently used in order to improve the spatial resolution of an ultrasound image and to obtain an estimate (independent of the properties of the imaging system) of the true tissue reflectivity function. The experiments reported in this paper demonstrate the efficiency and illustrate all the potential of this new estimation and blind deconvolution approach.

Spect image restoration via Recursive Inverse Filtering constrained by a probabilistic MRI atlas

3D Brain SPECT imagery is a well established functional imaging method which has become a great help to physicians in the diagnosis of several neurological and cerebrovascular diseases. However, mainly due to the effects of attenuation and the scattering of emitted photons, inherent to this imaging process, 3D SPECT images are generally blurred and exhibit poor spatial resolution. This leads to substantial errors in measurements of regional brain blood flow, and therefore in the estimations of brain activity. In order to improve the resolution of these images and then to facilitate their interpretation, we herein propose an original extension of the NAS-RIF (Recursive Inverse Filtering) deconvolution technique proposed by Kundur and Hatzinakos [1]. The proposed extension allows to efficiently integrate, in the deconvolution process, a set of soft constraints given by a probabilistic MRI atlas containing expertss prior knowledge about the spatial localization of the different brain structures (or tissue classes). This extension has three interesting properties ; first it allows to exploit (or fuse) reliable anatomical and (high resolution) geometrical information extracted horn a probabilistic 3D MRI atlas. Second, it allows to incorporate, into the NAS-RIF method, a regularization term which efficiently stabilizes the inverse solution. Third and contrary to multi-modal restoration techniques, it does not require a MRI scan of the patient. This method has been successfully tested on numerous real brain SPECT images (of different patients suffering from epilepsy), yielding promising restoration results.

An Edge-Preserving Anatomical-Based Regularization Term for the Nas-Rif Restoration of Spect Images

Nowadays, brain 3D SPECT is a well established functional imaging method that is widely used in clinical settings for the assessment of neurological and cerebrovascular diseases. However, due to the scattering of the emitted photons, inherent to this imaging process, brain 3D SPECT images exhibit poor spatial and inter-slice resolution. More precisely, SPECT images are blurred, leading to substantial errors in measurement of regional brain activity and making difficult and subjective, a reliable and accurate diagnosis by the nuclear physician. In order to improve the resolution of these images and then to facilitate their interpretation, we herein propose an original extension of the NAS-RIF deconvolution technique of Kundur and Hatzinakos. The proposed extension has two interesting properties; it allows to exploit or fuse anatomical and geometrical information extracted from a high resolution anatomical magnetic resonance (MR) image and also to efficiently incorporate, into the NAS-RIF method, a regularization term to stabilize the inverse solution. In our application, this anatomical-based regularization term exploits the result of an unsupervised Markovian segmentation obtained after a preliminary registration step between the MR and SPECT data volume coming from a same patient. This method has been successfully tested on numerous pairs of brain MR and SPECT images of different patients, yielding very promising restoration results.