scholarly journals Region of Interest Identification for Brain Tumors in Magnetic Resonance Images

2020 ◽  
Author(s):  
Fateme Mostafaie ◽  
Reihaneh Teimouri ◽  
Zahra Nabizadeh Shahre Babak ◽  
Nader Karimi ◽  
Shadrokh Samavi
Keyword(s):  
Magnetic Resonance ◽  
Brain Tumors ◽  
Region Of Interest ◽  
Magnetic Resonance Images

Measurement of tumor resection volumes from computerized images

1992 ◽  
Vol 77 (1) ◽  
pp. 151-154 ◽  
Author(s):  
Duc H. Duong ◽  
Robert C. Rostomily ◽  
David R. Haynor ◽  
G. Evren Keles ◽  
Mitchel S. Berger
Keyword(s):  
Magnetic Resonance ◽  
Brain Tumors ◽  
Tumor Resection ◽  
Region Of Interest ◽  
Magnetic Resonance Images ◽  
Content Type ◽  
Slide Scanner ◽  
Program Error ◽  
Fine Print ◽  
Area And Volume

✓ The authors describe a method for quantitation of the area and volume of the resection cavity in patients who have undergone surgery for brain tumors. Using a slide scanner and Image 1.27, a public domain program for the Apple Macintosh II computer, computerized tomography scans and magnetic resonance images can be digitized and analyzed for a particular region of interest, such as the area and volume of tumor on preoperative and postresection scans. Phantom scans were used to analyze the accuracy of the program and the program users. User error was estimated at 2%, program error was 4.5%. This methodology is proposed as a means of retrospectively calculating the extent of tumor resection.

scholarly journals Vascular phenotyping of brain tumors using magnetic resonance microscopy (μMRI)

2011 ◽  
Vol 31 (7) ◽  
pp. 1623-1636 ◽  
Author(s):  
Eugene Kim ◽  
Jiangyang Zhang ◽  
Karen Hong ◽  
Nicole E Benoit ◽  
Arvind P Pathak
Keyword(s):  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Three Dimensional ◽  
Region Of Interest ◽  
Tumor Model ◽  
Magnetic Resonance Microscopy ◽  
Vascular Phenotype ◽  
Brain Tumor Model ◽  
Novel Method

Abnormal vascular phenotypes have been implicated in neuropathologies ranging from Alzheimer's disease to brain tumors. The development of transgenic mouse models of such diseases has created a crucial need for characterizing the murine neurovasculature. Although histologic techniques are excellent for imaging the microvasculature at submicron resolutions, they offer only limited coverage. It is also challenging to reconstruct the three-dimensional (3D) vasculature and other structures, such as white matter tracts, after tissue sectioning. Here, we describe a novel method for 3D whole-brain mapping of the murine vasculature using magnetic resonance microscopy (μMRI), and its application to a preclinical brain tumor model. The 3D vascular architecture was characterized by six morphologic parameters: vessel length, vessel radius, microvessel density, length per unit volume, fractional blood volume, and tortuosity. Region-of-interest analysis showed significant differences in the vascular phenotype between the tumor and the contralateral brain, as well as between postinoculation day 12 and day 17 tumors. These results unequivocally show the feasibility of using μMRI to characterize the vascular phenotype of brain tumors. Finally, we show that combining these vascular data with coregistered images acquired with diffusion-weighted MRI provides a new tool for investigating the relationship between angiogenesis and concomitant changes in the brain tumor microenvironment.

Computer Aided Interpretation of Fibrous Texture in Hepatic Magnetic Resonance Images

2013 ◽  
Vol 647 ◽  
pp. 325-330 ◽  
Author(s):  
Yu Fan Zeng ◽  
Xue Jun Zhang ◽  
Wen Yan ◽  
Li Ling Long ◽  
Yu Kun Huang ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Hepatic Fibrosis ◽  
Region Of Interest ◽  
Texture Features ◽  
Magnetic Resonance Images ◽  
Optimal Number ◽  
Support Vector ◽  
Mr Images ◽  
Accuracy Rate ◽  
Occurrence Matrix

The fibrous texture in liver is one of important signs for interpreting the chronic liver diseases in radiologists’ routines. In order to investigate the usefulness of various texture features calculated by computer algorithm on hepatic magnetic resonance (MR) images, 15 texture features were calculated from the gray level co-occurrence matrix (GLCM) within a region of interest (ROI) which was selected from the MR images with 6 stages of hepatic fibrosis. By different combination of 15 features as input vectors, the classifier had different performance in staging the hepatic fibrosis. Each combination of texture features was tested by Support Vector Machine (SVM) with leave one case out method. 173 patients’ MR images including 6 stages of hepatic fibrosis were scanned within recent two years. The result showed that optimal number of features was confirmed from 3 to 7 by investigating the classified accuracy rate between each stage/group. It is evident that angular second moment, entropy, sum average and sum entropy played the most significant role in classification.

scholarly journals Computer-Aided Detection of Metastatic Brain Tumors in Magnetic Resonance Images

2013 ◽  
Vol 69 (6) ◽  
pp. 632-640 ◽  
Author(s):  
Tomomi Takenaga ◽  
Yoshikazu Uchiyama ◽  
Toshinori Hirai ◽  
Hideo Nakamura ◽  
Yutaka Kai ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Brain Tumors ◽  
Magnetic Resonance Images ◽  
Computer Aided Detection ◽  
Metastatic Brain Tumors ◽  
Computer Aided

scholarly journals A Hybrid Enhanced Independent Component Analysis Approach for Segmentation of Brain Magnetic Resonance Image

2018 ◽  
Vol 3 (3) ◽  
pp. 285 ◽  
Author(s):  
Shaik Basheera ◽  
MSatya Sai Ram
Keyword(s):  
Magnetic Resonance ◽  
Medical Imaging ◽  
Brain Tumors ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Gaussian Mixture ◽  
Magnetic Resonance Images ◽  
Independent Component ◽  
Segmentation Evaluation ◽  
Daunting Task

<p>Medical imaging and analysis plays a crucial role in diagnosis and treatment planning. The anatomical complexity of human brain makes the process of imaging and analyzing very difficult. In spite of huge advancements in medical imaging procedures, accurate segmentation and classification of brain abnormalities remains a challenging and daunting task. This challenge is more visible in the case of brain tumors because of different possible shapes of tumors, locations and image intensities of different types of tumors. In this paper we have presented a method for automated segmentation of brain tumors from magnetic resonance images. An enhanced and modified Gaussian mixture mode model and the independent component analysis segmentation approach has been employed for segmenting brain tumors in magnetic resonance images. The results of segmentation are validated with the help of segmentation evaluation parameters.</p>

scholarly journals Semi-automated cardiac segmentation on cine magnetic resonance images using GVF-Snake deformable models

2009 ◽  
Author(s):  
Constantin Constantinides ◽  
Yasmina Chenoune ◽  
Nadjia Kachenoura ◽  
Elodie Roullot ◽  
Elie Mousseaux ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Ejection Fraction ◽  
Region Of Interest ◽  
Left Ventricular ◽  
Magnetic Resonance Images ◽  
Deformable Model ◽  
Papillary Muscles ◽  
Myocardial Mass ◽  
Endocardial Border ◽  
Cardiac Segmentation

The segmentation of left ventricular structures is necessary for the evaluation of the ejection fraction (EF) and the myocardial mass (LVM). A semi-automated 2D algorithm using connected filters and a deformable model allowing an accurate endocardial detection was proposed. The epicardial border was deduced using a deformable model restricted inside a region of interest defined from the endocardial border. Papillary muscles were detected using a fuzzy k-means algorithm. The method was applied to the challenge training and validation databases, consisting of 15 subjects each. The evaluation was performed using the tools provided by the challenge. For both datasets, results show a mean Dice metric of 0.89 for endocardial borders (0.92 for epicardial borders). Overall average perpendicular distance was 2.2 mm. Very good correlation was obtained for the EF and LVM parameters. Visual overall rating given by the challenge’s cardiologist was 1.2. Segmentation was robust and performed successfully on both datasets.

scholarly journals Discrete tortuosity as a morphometric measure in brain tumors

2017 ◽  
Author(s):  
◽  
H. Hevia-Montiel
Keyword(s):  
Magnetic Resonance ◽  
Brain Tumors ◽  
Morphological Changes ◽  
Magnetic Resonance Images ◽  
Alternative Procedure ◽  
Resonance Imaging ◽  
Chain Code ◽  
Necrotic Region ◽  
Morphometric Measure ◽  
Morphometric Index

Morphological changes in brain tumors may be related to their malignancy. The objective of this work is to be able to detect and quantify these changes in a magnetic resonance imaging, since it can represent an important advantage for the noninvasive diagnosis in patients. One way to identify such morphological changes can be through the measurement of their tortuosity. The discrete tortuosity is a descriptor that characterizes bi-dimensional curves, as the contour of a region. In this work an alternative procedure for calculating the volumetric tortuosity of a surface is proposed. This technique is based in the slope chain code of the surface contour of a volume, and here we call it tridimensional discrete tortuosity. This descriptor is used as a morphometric index to study the tortuosity of brain tumors. For this, magnetic resonance images from 20 patients with low and high malignancy levels were analyzed, considering four regions: edema, whole tumor, enhancing region, and necrotic region. As a result, the tortuosities of the different regions are presented, with significant differences only in some of them. It should be noted that a disadvantage that is present, is the dependence of the measurement to the use of a robust method of segmentation, nevertheless the proposal of the discrete tortuosity for volumetric surfaces is satisfactory.

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