Rapid three-dimensional surface reconstruction of magnetic resonance images of large arteries and veins: A preliminary evaluation of clinical utility

1993 ◽  
Vol 16 (1) ◽  
pp. 25-29 ◽  
Author(s):  
Krishna Kandarpa ◽  
Tamas Sandor ◽  
James Tieman ◽  
Roshann Hooshmand ◽  
Paramjit S. Chopra ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Surface Reconstruction ◽  
Clinical Utility ◽  
Three Dimensional ◽  
Magnetic Resonance Images ◽  
Preliminary Evaluation ◽  
Large Arteries ◽  
Arteries And Veins ◽  
Dimensional Surface

scholarly journals Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
R. Rajesh Sharma ◽  
P. Marikkannu
Keyword(s):  
Brain Tumor ◽  
Magnetic Resonance ◽  
Gravitational Search Algorithm ◽  
Three Dimensional ◽  
Magnetic Resonance Images ◽  
Tumor Classification ◽  
Classification Model ◽  
Support Vector ◽  
Preliminary Evaluation ◽  
Distribution Method

A novel hybrid approach for the identification of brain regions using magnetic resonance images accountable for brain tumor is presented in this paper. Classification of medical images is substantial in both clinical and research areas. Magnetic resonance imaging (MRI) modality outperforms towards diagnosing brain abnormalities like brain tumor, multiple sclerosis, hemorrhage, and many more. The primary objective of this work is to propose a three-dimensional (3D) novel brain tumor classification model using MRI images with both micro- and macroscale textures designed to differentiate the MRI of brain under two classes of lesion, benign and malignant. The design approach was initially preprocessed using 3D Gaussian filter. Based on VOI (volume of interest) of the image, features were extracted using 3D volumetric Square Centroid Lines Gray Level Distribution Method (SCLGM) along with 3D run length and cooccurrence matrix. The optimal features are selected using the proposed refined gravitational search algorithm (RGSA). Support vector machines, over backpropagation network, andk-nearest neighbor are used to evaluate the goodness of classifier approach. The preliminary evaluation of the system is performed using 320 real-time brain MRI images. The system is trained and tested by using a leave-one-case-out method. The performance of the classifier is tested using the receiver operating characteristic curve of 0.986 (±002). The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods.

Three-dimensional surface reconstruction within noncontact diffuse optical tomography using structured light

2012 ◽  
Vol 17 (12) ◽  
pp. 126009 ◽  
Author(s):  
Kirstin Baum ◽  
Raimo Hartmann ◽  
Tobias Bischoff ◽  
Jan O. Oelerich ◽  
Stephan Finkensieper ◽  
...  
Keyword(s):  
Surface Reconstruction ◽  
Structured Light ◽  
Optical Tomography ◽  
Three Dimensional ◽  
Diffuse Optical Tomography ◽  
Dimensional Surface

The Safety of Operating on Breasts With a History of Prior Reduction Mammoplasty: Dynamic Magnetic Resonance Imaging Analysis of Angiogenesis

2021 ◽  
Author(s):  
Joseph Kyu-hyung Park ◽  
Seokwon Park ◽  
Chan Yeong Heo ◽  
Jae Hoon Jeong ◽  
Bola Yun ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Three Dimensional ◽  
Venous Drainage ◽  
Reduction Mammoplasty ◽  
Magnetic Resonance Images ◽  
Areolar Complex ◽  
Internal Mammary ◽  
Initial Procedure ◽  
Nipple Sparing Mastectomy ◽  
Nipple Sparing

Abstract Background Vascularity of the nipple-areolar complex (NAC) is altered after reduction mammoplasty, which increases complications risks after repeat reduction or nipple-sparing mastectomy. Objectives To evaluate angiogenesis of the NAC via serial analysis of breast magnetic resonance images (MRIs). Methods Breast MRIs after reduction mammoplasty were analyzed for 35 patients (39 breasts) using three-dimensional reconstructions of maximal intensity projection images. All veins terminating at the NAC were classified as internal mammary, anterior intercostal, or lateral thoracic in origin. The vein with the largest diameter was considered the dominant vein. Images were classified based on the time since reduction: <6 months, 6-12 months, 12-24 months, >2 years. Results The average number of veins increased over time: 1.17 (<6 months), 1.56 (6–12 months), 1.64 (12–24 months), 1.73 (>2 years). Within 6 months, the pedicle was the only vein. Veins from other sources began to appear at 6–12 months. In most patients, at least two veins were available after 1 year. After 1 year, the internal mammary vein was the most common dominant vein regardless of the pedicle used. Conclusions In the initial 6 months after reduction mammoplasty, the pedicle is the only source of venous drainage; however, additional sources are available after 1 year. The internal thoracic vein was the dominant in most patients. Thus, repeat reduction mammoplasty or nipple-sparing mastectomy should be performed ≥1 year following the initial procedure. After 1 year, the superior or superomedial pedicle may represent the safest option when the previous pedicle is unknown.

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