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Neculai Archip

Publications and source records attributed to Neculai Archip.

8 recordsLinked to original sources
PubMed2006

Anatomical structure modeling from medical images.

Some clinical applications, such as surgical planning, require volumetric models of anatomical structures represented as a set of tetrahedra. A practical method of constructing anatomical models from medical images is presented. The method starts with a set of contours segmented from the medical images by a clinician and produces a model that has high fidelity with the contours. Unlike most modeling methods, the contours are not restricted to lie on parallel planes. The main steps are a 3D Delaunay tetrahedralization, culling of non-object tetrahedra, and refinement of the tetrahedral mesh. The result is a high-quality set of tetrahedra whose surface points are guaranteed to match the original contours. The key is to use the distance map and bit volume structures that were created along with the contours. The method is demonstrated on computed tomography, MRI and 3D ultrasound data. Models of 170,000 tetrahedra are constructed on a standard workstation in approximately 10s. A comparison with related methods is also provided.

Algorithms↗
PubMed2005

Spectral clustering algorithms for ultrasound image segmentation.

Image segmentation algorithms derived from spectral clustering analysis rely on the eigenvectors of the Laplacian of a weighted graph obtained from the image. The NCut criterion was previously used for image segmentation in supervised manner. We derive a new strategy for unsupervised image segmentation. This article describes an initial investigation to determine the suitability of such segmentation techniques for ultrasound images. The extension of the NCut technique to the unsupervised clustering is first described. The novel segmentation algorithm is then performed on simulated ultrasound images. Tests are also performed on abdominal and fetal images with the segmentation results compared to manual segmentation. Comparisons with the classical NCut algorithm are also presented. Finally, segmentation results on other types of medical images are shown.

Algorithms↗
PubMed2005

Ultrasound image segmentation using spectral clustering.

Segmentation of ultrasound images is necessary in a variety of clinical applications, but the development of automatic techniques is still an open problem. Spectral clustering techniques have recently become popular for data and image analysis. In particular, image segmentation has been proposed via the normalized cut (NCut) criterion. This article describes an initial investigation to determine the suitability of such segmentation techniques for ultrasound images. The adaptation of the NCut technique to ultrasound is described first. Segmentation is then performed on simulated ultrasound images. Tests are also performed on abdominal and fetal images with the segmentation results compared to manual segmentation. The success of the segmentation on these test cases warrants further research into NCut-based segmentation of ultrasound images.

Computer Simulation↗
PubMed2005

Automatic detection of fiducial markers in fluoroscopy images for on-line calibration.

Calibrated C-arm fluoroscopy is used for a variety of medical procedures where objects and anatomical structures need to be located in space. Calibration is often based on imaging a grid of fiducial markers and using the C-arm image's geometrical measurements (radius and center) together with the positions of the markers. An on-line technique is developed to automatically locate the fiducial markers and validated on 97 images. The success rate of the detection algorithm is 96.28% with an average error of 0.46 mm and a standard deviation of 0.32 mm.

Algorithms↗
PubMed2004

Automatic measurements of image geometry for online calibration of C-arm fluoroscopy.

Calibrated C-arm fluoroscopy is used for a variety of surgical procedures where surgical tools and anatomical structures need to be located in space. Calibration can be performed online by combining measurements of the geometry of the image (center and radius) with measurements of a grid of fiducial markers. This article focuses on the first aspect--image geometry--and describes a method to perform the geometry measurements automatically. The accuracy, robustness, and speed of the method are validated on 100 images obtained from several hospitals with different C-arm scanners. All 100 images were successfully measured with an average error of 0.8 mm for the center and 0.8 mm for the radius. The execution time is less than one second per image.

Algorithms↗
PubMed2002

A computer aided diagnostic system for radiotherapy planning.

Planning for radiation therapy intervention implies the definition of treatment volumes as well as a clear delimitation of normal tissue. This paper presents a Computer Aided Diagnostic system for the automatic CT image analysis. Two important problems are solved: the spinal cord segmentation and the detection of lung metastases. Some subordinate problems are also solved: the detection of spinal canal, lamina, lungs, and ribs, as well as the identification of thorax contour. The developed methodologies use a knowledge-driven image processing based on Anatomical Structures Maps and task-oriented architecture. Experiments were performed on CT images from La Chaux de Fonds Hospital (Switzerland). Evaluations were performed using a visual inspection of the contours projected on the CT image slices. The radiologist decided whether each of the contours obtained with our system was acceptable or not. The accuracy of the method was defined as the fraction of CT slices in which the particular contour was correctly located. In the case of spinal cord segmentation, the procedure was tested on 23 patients (1051 images), resulting in an accuracy of 91%. In the case of lung tumors detection, the method showed an accuracy of > 90%, with testing performed on 20 patients for a total of 988 images. The experiments performed show that the method is reliable, with possible future application in an oncology department.

Humans↗
PubMed2002

A knowledge-based approach to automatic detection of the spinal cord in CT images.

Accurate planning of radiation therapy entails the definition of treatment volumes and a clear delimitation of normal tissue of which unnecessary exposure should be prevented. The spinal cord is a radiosensitive organ, which should be precisely identified because an overexposure to radiation may lead to undesired complications for the patient such as neuronal disfunction or paralysis. In this paper, a knowledge-based approach to identifying the spinal cord in computed tomography images of the thorax is presented. The approach relies on a knowledge-base which consists of a so-called anatomical structures map (ASM) and a task-oriented architecture called the plan solver. The ASM contains a frame-like knowledge representation of the macro-anatomy in the human thorax. The plan solver is responsible for determining the position, orientation and size of the structures of interest to radiation therapy. The plan solver relies on a number of image processing operators. Some are so-called atomic (e.g., thresholding and snakes) whereas others are composite. The whole system has been implemented on a standard PC. Experiments performed on the image material from 23 patients show that the approach results in a reliable recognition of the spinal cord (92% accuracy) and the spinal canal (85% accuracy). The lamina is more problematic to locate correctly (accuracy 72%). The position of the outer thorax is always determined correctly.

Adult↗