Biomedical subjects

Reza A Zoroofi

Publications and source records attributed to Reza A Zoroofi.

4 recordsLinked to original sources

PubMed2005

3-D quantification and visualization of vascular structures from confocal microscopic images using skeletonization and voxel-coding.

This paper presents an image processing approach for information extraction from three-dimensional (3-D) images of vasculature. It extracts quantitative information such as skeleton, length, diameter, and vessel-to-tissue ratio for different vessels as well as their branches. Furthermore, it generates 3-D visualization of vessels based on desired anatomical characteristics such as vessel diameter or 3-D connectivity. Steps of the proposed approach are: (1) pre-processing, (2) distance mappings, (3) branch labeling, (4) quantification, and (5) visualization. We have tested and evaluated the proposed algorithms using simulated images of multi-branch vessels and real confocal microscopic images of the vessels in rat brains. Experimental results illustrate performance of the methods and usefulness of the results for medical image analysis applications.

Animals↗

PubMed2004

Automated segmentation of necrotic femoral head from 3D MR data.

Segmentation of diseased organs is an important topic in computer assisted medical image analysis. In particular, automatic segmentation of necrotic femoral head is of importance for various corresponding clinical tasks including visualization, quantitative assessment, early diagnosis and adequate management of patients suffering from avascular necrosis of the femoral head (ANFH). Early diagnosis and treatment of ANFH is crucial since the disease occurs in relatively young individuals with an average age of 20-50, and since treatment options for more advanced disease are frequently unsuccessful. The present paper describes several new techniques and software for automatic segmentation of necrotic femoral head based on clinically obtained multi-slice T1-weighted MR data. In vivo MR data sets of 50 actual patients are used in the study. An automatic method built up to manage the segmentation task according to image intensity of bone tissues, shape of the femoral head, and other characters. The processing scheme consisted of the following five steps. (1) Rough segmentation of non-necrotic lesions of the femur by applying a 3D gray morphological operation and a 3D region growing technique. (2) Fitting a 3D ellipse to the femoral head by a new approach utilizing the constraint of the shape of the femur, and employing a principle component analysis and a simulated annealing technique. (3) Estimating the femoral neck location, and also femoral head axis by integrating anatomical information of the femur and boundary of estimated 3D ellipse. (4) Removal of non-bony tissues around the femoral neck and femoral head ligament by utilizing the estimated femoral neck axis. (5) Classification of necrotic lesions inside the estimated femoral head by a k-means technique. The above method was implemented in a Microsoft Windows software package. The feasibility of this method was tested on the data sets of 50 clinical cases (3000 MR images).

Automation↗

PubMed2003

Wavelet analysis for neonatal electroencephalographic seizures.

Electroencepholographs (EEGs) of neonatal seizures differ from those of children and adults. This study evaluated whether wavelet transform analysis, a nonstationary frequency analysis of EEG, can recognize and characterize neonatal seizures. Twenty-second segments were analyzed from 69 EEG seizures in 15 neonatal patients whose seizures lasted 10 seconds or longer. The wavelet transform results were examined, as were EEG seizure durations and dominant frequencies. The wavelet transform results were correlated with the occurrence, after an 18-month follow-up, of postneonatal seizures. Wavelet transform analysis identified 40 seizures (58%) with a "sustained dominant frequency component" that lasted 10 seconds or longer and 29 seizures without a sustained dominant frequency component. The mean seizure duration of the 40 seizures with sustained dominant frequency components was 63.3 seconds, longer than the mean duration (33.6 seconds) of the seizures without sustained dominant frequency components, P < 0.01. Eleven patients manifested postneonatal epileptic seizures. Fifty-two EEG seizures in these 11 patients revealed more sustained dominant frequency components (74%) than 17 seizures in the 4 patients without postneonatal seizures (only 12%), P < 0.05. Wavelet transform analysis can identify neonatal EEG seizures and characterize their epileptic components. The presence of sustained dominant frequency components may predict postneonatal epileptic seizures.

Asphyxia Neonatorum↗

PubMed2003

Automated segmentation of acetabulum and femoral head from 3-D CT images.

This paper describes several new methods and software for automatic segmentation of the pelvis and the femur, based on clinically obtained multislice computed tomography (CT) data. The hip joint is composed of the acetabulum, cavity of the pelvic bone, and the femoral head. In vivo CT data sets of 60 actual patients were used in the study. The 120 (60 x 2) hip joints in the data sets were divided into four groups according to several key features for segmentation. Conventional techniques for classification of bony tissues were first employed to distinguish the pelvis and the femur from other CT tissue images in the hip joint. Automatic techniques were developed to extract the boundary between the acetabulum and the femoral head. An automatic method was built up to manage the segmentation task according to image intensity of bone tissues, size, center, shape of the femoral heads, and other characters. The processing scheme consisted of the following five steps: 1) preprocessing, including resampling 3-D CT data by a modified Sinc interpolation to create isotropic volume and to avoid Gibbs ringing, and smoothing the resulting images by a 3-D Gaussian filter; 2) detecting bone tissues from CT images by conventional techniques including histogram-based thresholding and binary morphological operations; 3) estimating initial boundary of the femoral head and the joint space between the acetabulum and the femoral head by a new approach utilizing the constraints of the greater trochanter and the shapes of the femoral head; 4) enhancing the joint space by a Hessian filter; and 5) refining the rough boundary obtained in step 3) by a moving disk technique and the filtered images obtained in step 4). The above method was implemented in a Microsoft Windows software package and the resulting software is freely available on the Internet. The feasibility of this method was tested on the data sets of 60 clinical cases (5000 CT images).

Acetabulum↗