Amir Ghanei

A three-dimensional deformable model for segmentation of human prostate from ultrasound images

Segmentation of human prostate from ultrasound (US) images is a crucial step in radiation therapy, especially in real-time planning for US image-guided prostate seed implant. This step is critical to determine the radioactive seed placement and to ensure the adequate dose coverage of prostate. However, due to the low contrast of prostate and very low signal-to-noise ratio in US images, this task remains as an obstacle. The manual segmentation of this object is time consuming and highly subjective. In this work, we have proposed a three-dimensional (3D) deformable surface model for automatic segmentation of prostate. The model has a discrete structure made from a set of vertices in the 3D space that form triangle facets. The model converges from an initial shape to its equilibrium iteratively, by a weighted sum of the internal and external forces. Internal forces are based on the local curvature of the surface and external forces are extracted from the volumetric image data by applying an appropriate edge filter. We have also developed a method for initialization of the model from a few initial contours that are drawn on different slices. During the deformation, a resampling procedure is used to maintain the resolution of the model. The entire model is applied in a multiscale scheme, which increases the robustness and speed, and guarantees a better convergence. The model is tested on real clinical data and initial results are very promising.

Multiparametric MRI tissue characterization in clinical stroke with correlation to clinical outcome: Part 2

Background and Purpose— Multiparametric MRI generates different zones within the lesion that may reflect heterogeneity of tissue damage in cerebral ischemia. This study presents the application of a novel model of tissue characterization based on an angular separation between tissues obtained with the use of an objective (unsupervised) computer segmentation algorithm implementing a modified version of the Iterative Self-Organizing Data Analsis Technique (ISODATA). We test the utility of this model to identify ischemic tissue in clinical stroke. Methods— MR parameters diffusion-, T2-, and T1-weighted imaging (DWI, T2WI, and T1WI, respectively) were obtained from 10 patients at 3 time points (30 studies) after stroke: acute (≤12 hours), subacute (3 to 5 days), and chronic (3 months). The National Institutes of Health Stroke Scale (NIHSS) was measured, and volumes were obtained from the ISODATA, DWI, and T2WI maps on patients at each time point. Results— The acute (≤12 hours) multiparametric ISODATA volume was significantly correlated with the acute (≤12 hours) DWI (r =0.96, P <0.05; n=10) and chronic (3 months) T2WI volume (r =0.69, P <0.05; n=10). The ISODATA-defined tissue regions exhibited MR indices consistent with ischemic and/or infarcted tissue at each time point. The acute (≤12 hours) multiparametric ISODATA volumes were significantly correlated (r =0.82, P <0.009; n=10) with the final NIHSS score. In comparison, the acute (≤12 hours) DWI volumes were less correlated (r =0.77, P <0.05; n=10) and T2WI volume (≤12h) exhibited a marginal correlation (r =0.66, P <0.05; n=10) with the final NIHSS score. Conclusions— The integrated ISODATA approach to tissue segmentation and classification discriminated abnormal from normal tissue at each time point. The ISODATA volume was significantly correlated with the current MR standards used in the clinical setting and the 3-month clinical status of the patient.

Segmentation of the hippocampus from brain MRI using deformable contours

The application of a discrete dynamic contour model for segmentation of the hippocampus from brain MRI has been investigated. Solutions to several common problems of dynamic contours in this case and similar cases have been developed. A new method for extracting the discontinuous boundary of a structure with multiple edges near the structure has been developed. The method is based on detecting and following edges by external forces. The reliability of the final contour and the model stability have been improved by using a continuous mapping of the external energy and limiting movements of the contour. The problem of optimizing the internal force weight has been overcome by making it dependent on the amount of the external force. Finally, the results of applying the proposed algorithm, which implements the above modifications, to multiple applications have been evaluated.

A 3D deformable surface model for segmentation of objects from volumetric data in medical images.

In this paper we present a new 3D discrete dynamic surface model. The model consists of vertices and edges, which connect adjacent vertices. Basic geometry of the model surface is generated by triangle patches. The model deforms by internal and external forces. Internal forces are obtained from local geometry of the model and are related to the local curvature of the surface. External forces, on the other hand, are based on the image data and are calculated from desired image features. We also present a method for generating an initial volume for the model from a stack of initial contours, drawn by the user on cross sections of the volumetric data.

A discrete curvature-based deformable surface model with application to segmentation of volumetric images

In this paper, we present a new curvature-based three-dimensional (3-D) deformable surface model. The model deforms under defined force terms. Internal forces are calculated from local model curvature, using a robust method by a least-squares error (LSE) approximation to the Dupin indicatrix. External forces are calculated by applying a step expansion and restoration filter (SEF) to the image data. A solution for one of the most common problems associated with deformable models, self-cutting, has been proposed in this work. We use a principal axis analysis and reslicing of the deformable model, followed by triangulation of the slices, to remedy self-cutting. We use vertex resampling, multiresolution deformation, and refinement of the mesh grid to improve the quality of the model deformation, which leads to better results. Examples of the model application to different cases (simulation, magnetic resonance imaging (MRI), computerized tomography (CT), and ultrasound images) are presented, showing diversity and flexibility of the model.

Boundary-based warping of brain MR images.

The goal of this work was to develop a warping technique for mapping a brain image to another image or atlas data, with minimum user interaction and independent of gray level information. We have developed and tested three different methods for warping magnetic resonance (MR) brain images. We utilize a deformable contour to extract and warp the boundaries of the two images. A mesh‐grid coordinate system is constructed for each brain, by applying a distance transformation to the resulting contours, and scaling. In the first method (MGC), the first image is mapped to the second image based on a one‐to‐one mapping between different layers defined by the mesh‐grid. In the second method (IDW), the corresponding pixels in the two images are found using the above mesh‐grid system and a local inverse‐distance weights interpolation. In the third proposed method (TSB), a subset of grid points is used for finding the parameters of a spline transformation, which defines the global warping. The warping methods were applied to clinical MR consisting of diffusion‐weighted and T2‐weighted images of the human brain. The IDW and TSB methods were superior in ranking of diagnostic quality of the warped MR images to the MGC (P < 0.01) as defined by a neuroradiologist. The deformable contour warping produced excellent diagnostic quality for the diffusion‐weighted images coregistered and warped to T2 weighted images. J. Magn. Reson. Imaging 2000;12:417–429.

A deformable model for hippocampus segmentation: improvements and extension to 3D

The application of a deformable model to the segmentation of hippocampus in brain MRI has been investigated. Common problems of the model in this case and similar cases have been discussed and solved. A new method for extracting discontinuous boundaries of an object with multiple unwanted edges has been developed. This method is based on detecting and following the edge by external forces. For improving the contour stability, its movement has been limited. Also, adaptive values for internal force weights have been used. In the next step, the model has been extended to 3D which is a Deformable Surface Model. A geometric structure used for this purpose. This helps in definition of normal vectors and internal forces. Finally, a method for generating the initial volume from individual initial polygons has been developed.

Boundary-based warping of brain MR images

The goal of this work was to develop a warping technique for mapping a brain image to another image or atlas data, with minimum user interaction and independent of gray level information. We have developed and tested three different methods for warping magnetic resonance (MR) brain images. We utilize a deformable contour to extract and warp the boundaries of the two images. A mesh-grid coordinate system is constructed for each brain, by applying a distance transformation to the resulting contours, and scaling. In the first method (MGC), the first image is mapped to the second image based on a one-to-one mapping between different layers defined by the mesh-grid. In the second method (IDW), the corresponding pixels in the two images are found using the above mesh-grid system and a local inverse-distance weights interpolation. In the third proposed method (TSB), a subset of grid points is used for finding the parameters of a spline transformation, which defines the global warping. The warping methods were applied to clinical MR consisting of diffusion-weighted and T2-weighted images of the human brain. The IDW and TSB methods were superior in ranking of diagnostic quality of the warped MR images to the MGC (P < 0.01) as defined by a neuroradiologist. The deformable contour warping produced excellent diagnostic quality for the diffusion-weighted images coregistered and warped to T2 weighted images. J. Magn. Reson. Imaging 2000;12:417-429.

Homothetical warping of brain MR images

We have developed a new method for warping MR brain images to other brain images or to the atlas data. We first used a deformable contour model to extract and warp the boundaries of the two brain images. We use a balloon force in this stage to ensure good matching of the final contour to the brain boundaries regardless of the initial contour. The applied deformable contour model captures the general shape of each brain from its boundary contour, after which, the outer boundaries can be mapped to each other. A mesh grid coordinate system is constructed for each brain thereafter, by applying a distance transformation to the resulting contours. The first image is mapped to the other image based on a one-to-one mapping between different layers defined by a mesh grid coordinate system.

Automatic segmentation of hippocampus from brain MRI using deformable contours

The application of deformable contours for segmentation of the hippocampus has been investigated. Common problems of dynamic contours in this case and cases like this have been discussed and solved. A new method for extracting the discontinuous boundary of a structure with multiple unwanted edges near it has been developed. The method is based on detecting and following the edge by external forces. The final oscillation of the contour has been remedied by using interpolation of external energy and limiting the movements of the contour. Also, the problem of optimizing the weights has been overcome by making them dependent on the amount of forces.