Accurate automatic segmentation of the prostate in ultrasound images is still a challenging research problem. In this work, we propose the use of gray level images, constructed with a sample of gray level profiles perpendicular to the contour of the prostate. A two dimensional principal component analysis (2D PCA) was performed on a set of training contour images. The reconstruction error from the 2D PCA was used as an objective function for automatic adjustment of a point distribution model of the prostate. Our method was validated on 9 ultrasound images of the prostate and compared to the optimization of an objective function based on the mean Mahalanobis distance of a sampled gray level profile to the corresponding statistical profile model. Our new method based on a 2D PCA shows improved prostate segmentation results.
Computer Assisted Orthopedic Surgery (CAOS) requires a correct registration between the patient in the operating room and the virtual models representing the patient in the computer. In order to increase the precision and accuracy of the registration a set of new techniques that eliminated the need to use fiducial markers have been developed. The majority of these newly developed registration systems are based on costly intraoperative imaging systems like Computed Tomography (CT scan) or Magnetic resonance imaging (MRI). An alternative to these methods is the use of an Ultrasound (US) imaging system for the implementation of a more cost efficient intraoperative registration solution. In order to develop the registration solution with the US imaging system, the bone surface is segmented in both preoperative and intraoperative images, and the registration is done using the acquire surface. In this paper, we present the a preliminary results of a new approach to segment bone surface from ultrasound volumes acquired by means 3D freehand ultrasound. The method is based on the enhancement of the voxels that belongs to surface and its posterior segmentation. The enhancement process is based on the information provided by eigenanalisis of the multiscale 3D Hessian matrix. The preliminary results shows that from the enhance volume the final bone surfaces can be extracted using a singular value thresholding.
Image-guided interventions allow the physician to have a better planning and visualization of a procedure. 3D freehand ultrasound is a non-invasive and low-cost imaging tool that can be used to assist medical procedures. This tool can be used in the diagnosis and treatment of breast cancer. There are common medical practices that involve large needles to obtain an accurate diagnosis and treatment of breast cancer. In this study we propose the use of 3D freehand ultrasound for planning and guiding such procedures as core needle biopsy and radiofrequency ablation. The proposed system will help the physician to identify the lesion area, using image-processing techniques in the 3D freehand ultrasound images, and guide the needle to this area using the information of position and orientation of the surgical tools. We think that this system can upgrade the accuracy and efficiency of these procedures.
In recent years it has been more common to see 3D visualization of objects applied in many different areas. In
neuroscience research, 3D visualization of neurons acquired at different depth views (i.e. image stacks) by means
of confocal microscopy are of increase use. However in the best case, these visualizations only help to have a
qualitative description of the neuron shape. Since it is well know that neuronal function is intimately related to
its morphology. Having a precise characterization of neuronal structures such as axons and dendrites is critical
to perform a quantitative analysis and thus it allows to design neuronal functional models based on neuron
morphology. Currently there exists different commercial software to reconstruct neuronal arbors, however these
processes are labor intensive since in most of the cases they are manually made. In this paper we propose a new
software capable to reconstruct 3D neurons from confocal microscopy views in a more efficient way, with minimal
user intervention. The propose algorithm is based on finding the tubular structures present in the stack of images
using a modify version of the minimal graph cut algorithm. The model is generated from the segmented stack
with a modified version of the Marching Cubes algorithm to generate de 3D isosurface. Herein we describe the
principles of our 3D segmentation technique and the preliminary results.
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