Hossam E. Abdelmunim

Novel Image-Based 3D Reconstruction of the Human Jaw using Shape from Shading and Feature Descriptors

In this paper, we propose a novel approach for 3D surface reconstruction of the human jaw. Due to the difficulties of setting up a data acquisition system inside the mouth, we use an intra-oral camera to capture a sequence of calibrated images. These images are registered together to build a panoramic view of the jaw. We incorporate a shape from shading(SFS) algorithm that benefits from camera calibration parameters to build a 3D model from the panoramic image obtained from the previous stage. Our approach results in a 3D surface which has more fine details compared with those resulting from other literature techniques. We will demonstrate different artificial jaws surfaces reconstruction to show the efficiency of our system.

A novel probabilistic simultaneous segmentation and registration using level set

We propose a new shape-based segmentation approach using the statistical shape prior and level sets method. The segmentation depends on the image information and shape prior. Training shapes are grouped to form a probabilistic model. The shape model is embedded into the image domain taking in consideration the evolution of a contour represented by a level set function. The evolution of the front gathers information from the image intensities and shape prior. The segmentation approach is applied in segmenting the vertebral bodies in CT images. Our results shows that the technique is accurate and robust compared with the other alternative in the literature.

A new shape based segmentation framework using statistical and variational methods

In this paper, we propose a new shape based segmentation and registration of the vertebral bodies (VBs) in clinical computed tomography (CT) images. The VB and surrounding organs have very close gray level information and there are no strong edges in some CT images. To overcome these challenges, image appearance and shape information of VBs are used. There are three phases of our experiments: i) the detection of the VB region using the Matched filter, ii) initial segmentation using the graph cuts which integrates the intensity and spatial interaction models, iii) registration of the shape priors and initially segmented region to obtain the final segmentation. Preliminary results show that our proposed algorithm gives very encouraging results and can solve many segmentation and registration problems.

Elastic Shape Registration Using an Incremental Free Form Deformation Approach with the ICP Algorithm

Shape registration is one of the most challenging problems in computer vision and medical imaging. The process is affected by the way the shape is represented and the form of transformation used to move the source shape. Our paper handles the elastic shape registration by combining the incremental free form deformation (IFFD) with the point-based registration technique using the sum of least squares method. The iterative closest point (ICP) algorithm is used as a criteria to establish point correspondences in each level of the IFFD framework. The free form deformation (FFD) is well known in the literature and works by forming a lattice of control points that can move and hence deform the domain grid points smoothly and uniformly under some shape constrains. The control lattice resolution is increased step by step to achieve a satisfactory deformation of the source shape to exactly match the target boundaries. Our point-based registration is based on least squares that measure the Euclidean distance between source and target boundaries in addition to the shape constrains. The minimization gives a closed form solution of the lattice control points positions. Promising results will be demonstrated for closed and open shapes and structures. The approach can also work for structures that contain multiple parts without any problems.

Novel variational approach for the perspective shape from shading problem using calibrated images

In this paper, We propose a new approach for solving the Lambertian shape from shading (SFS) problem. The proposed technique employs image information as well as camera calibration parameters. We claim that the incorporation of camera parameters results in a better surface reconstruction. In this work, we slightly modify this more realistic model in order to take into account the attenuation of the illumination due to distance. We propose a new variational formulation that relates an evolving surface model with image information, taking into consideration that the image is taken by a perspective camera with known parameters. A new energy functional is formulated to incorporate brightness, smoothness, and integrability constraints. All of these terms assume a hyper surface that evolves in time to meet their criteria. Gradient descent optimization with Euler-Lagrange is used for optimization. We constructed a database of real surfaces as a ground-truth for the experiments. The proposed approach shows superior performance when compared with other approaches in the literature.

A 3D human teeth database construction based on a point-based shape registration

We propose a new system for building a 3D database for human teeth. Real extracted human teeth are scanned using a cone-beam CT scanner to build. The teeth models are segmented from the resulting images by means of level sets. The database includes 3D models of these teeth after minimizing global differences between the models. An affine transformation function is used to model the differences between the models. The transformation parameters are estimated by minimizing a least squares energy. A total of 280 teeth 3-D models have been created to build the real database and demonstrate the performance of the proposed framework.

Modeling of the lung nodules for detection in LDCT scans

A novel approach is proposed for generating data driven models of the lung nodules appearing in low dose CT (LDCT) scans of the human chest. Four types of common lung nodules are analyzed using Active Appearance Model methods to create descriptive lung nodule models. The proposed approach is also applicable for automatic classification of nodules into pathologies given a descriptive database. This approach is a major step forward for early diagnosis of lung cancer. We show the performance of the new nodule models on clinical datasets which illustrates significant improvements in both sensitivity and specificity.

Optical flow-based enhancement of spatio-temporal detection in videos

Accurate optical flow techniques are widely used in spatio-temporal object detection in videos. However, the computational complexity of the currently used techniques limits the effectiveness of spatio-temporal detection in applications such as action detection and event recognition. Therefore, in this paper we aim at employing rapid yet accurate optical flow techniques to promote the effectiveness of the detection system. The proposed design uses novel optical flow estimation techniques that are based on learned flow basis, known as PCA-Flow and PCA-Layers. PCA-Flow estimates dense flow from a linear flow model based on principle components of natural flow. PCA-Layers is an extension of PCA-Flow. PCA-Layers technique uses Markov random field (MRF) to combine several motion layers into dense optical flow. The motion in each layer is estimated by PCA-Flow. Our experimental results show that our approach maintains the overall performance of the baseline framework while 64% reduction in the computation time is achieved.

Elastic shape registration using an incremental free form deformation approach with the ICP algorithm

Our paper handles the elastic shape registration by combining the incremental free form deformation (IFFD) with the point-based registration technique using the sum of least squares method. The iterative closest point (ICP) algorithm is used as criteria to establish point correspondences in each level of the IFFD framework. The IFFD control lattice resolution is increased step by step to achieve a satisfactory deformation of the source shape to exactly match the target boundaries. Our point-based registration is based on least squares that measure the Euclidean distance between source and target boundaries in addition to the shape constrains. The minimization gives a closed form solution of the lattice control points positions. Promising results will be demonstrated for closed and open shapes and structures. The approach can also work for structures that contain multiple parts without any problems.