scholarly journals Automatic determination of MS lesion subtypes based on fractal analysis in brain MR images

2012 ◽  
Vol 05 (04) ◽  
pp. 162-169
Author(s):  
Mahdi Mohamadkhanloo ◽  
Farzad Mehrabi ◽  
Abdolhamid Sohrabi
Keyword(s):  
Fractal Analysis ◽  
Mr Images ◽  
Automatic Determination ◽  
Brain Mr Images

Fractal analysis of tumor in brain MR images

2003 ◽  
Vol 13 (5-6) ◽  
pp. 352-362 ◽  
Author(s):  
Khan M. Iftekharuddin ◽  
Wei Jia ◽  
Ronald Marsh
Keyword(s):  
Fractal Analysis ◽  
Mr Images ◽  
Brain Mr Images

On Automatic Determination of Movement Phases in Manual Transport During the Precision Grip

2000 ◽  
Vol 28 (1-2) ◽  
pp. 237-245 ◽  
Author(s):  
Nasser Hosseini ◽  
Blanka Hejdukova ◽  
Pall E. Ingvarsson ◽  
Bo Johnels ◽  
Torsten Olsson
Keyword(s):  
Precision Grip ◽  
Automatic Determination

Optimal Density Determination of Bouguer Anomaly Using Fractal Analysis (Case of study: Charak area,IRAN)

2005 ◽  
Vol 1 (1) ◽  
pp. 21-24
Author(s):  
Hamid Reza Samadi
Keyword(s):  
Surface Roughness ◽  
Fractal Dimension ◽  
Fractal Analysis ◽  
Fractal Geometry ◽  
Host Rock ◽  
Bouguer Anomaly ◽  
Research Area ◽  
Density Difference ◽  
Optimal Density

In exploration geophysics the main and initial aim is to determine density of under-research goals which have certain density difference with the host rock. Therefore, we state a method in this paper to determine the density of bouguer plate, the so-called variogram method based on fractal geometry. This method is based on minimizing surface roughness of bouguer anomaly. The fractal dimension of surface has been used as surface roughness of bouguer anomaly. Using this method, the optimal density of Charak area insouth of Hormozgan province can be determined which is 2/7 g/cfor the under-research area. This determined density has been used to correct and investigate its results about the isostasy of the studied area and results well-coincided with the geology of the area and dug exploratory holes in the text area

Automatic Determination of Optimal FIB Operations for Improved Circuit Probing and Fast Reconfiguration

2000 ◽  
Author(s):  
Romain Desplats ◽  
Timothee Dargnies ◽  
Jean-Christophe Courrege ◽  
Philippe Perdu ◽  
Jean-Louis Noullet
Keyword(s):  
Integrated Circuit ◽  
Focused Ion Beam ◽  
Ion Beam ◽  
Semiconductor Devices ◽  
Front Side ◽  
Automatic Determination ◽  
Metal Line ◽  
New Approach ◽  
Metal Layers

Abstract Focused Ion Beam (FIB) tools are widely used for Integrated Circuit (IC) debug and repair. With the increasing density of recent semiconductor devices, FIB operations are increasingly challenged, requiring access through 4 or more metal layers to reach a metal line of interest. In some cases, accessibility from the front side, through these metal layers, is so limited that backside FIB operations appear to be the most appropriate approach. The questions to be resolved before starting frontside or backside FIB operations on a device are: 1. Is it do-able, are the metal lines accessible? 2. What is the optimal positioning (e.g. accessing a metal 2 line is much faster and easier than digging down to a metal 6 line)? (for the backside) 3. What risk, time and cost are involved in FIB operations? In this paper, we will present a new approach, which allows the FIB user or designer to calculate the optimal FIB operation for debug and IC repair. It automatically selects the fastest and easiest milling and deposition FIB operations.

Normal cerebral ventricular volume growth in childhood

2020 ◽  
Vol 26 (5) ◽  
pp. 517-524
Author(s):  
Noah S. Cutler ◽  
Sudharsan Srinivasan ◽  
Bryan L. Aaron ◽  
Sharath Kumar Anand ◽  
Michael S. Kang ◽  
...  
Keyword(s):  
Brain Mri ◽  
Volume Growth ◽  
Ventricular Volume ◽  
Growth Patterns ◽  
Diagnostic Process ◽  
Growth Charts ◽  
Normal Brain ◽  
Mr Images ◽  
Ventricular Volumes ◽  
Brain Mr Images

OBJECTIVENormal percentile growth charts for head circumference, length, and weight are well-established tools for clinicians to detect abnormal growth patterns. Currently, no standard exists for evaluating normal size or growth of cerebral ventricular volume. The current standard practice relies on clinical experience for a subjective assessment of cerebral ventricular size to determine whether a patient is outside the normal volume range. An improved definition of normal ventricular volumes would facilitate a more data-driven diagnostic process. The authors sought to develop a growth curve of cerebral ventricular volumes using a large number of normal pediatric brain MR images.METHODSThe authors performed a retrospective analysis of patients aged 0 to 18 years, who were evaluated at their institution between 2009 and 2016 with brain MRI performed for headaches, convulsions, or head injury. Patients were excluded for diagnoses of hydrocephalus, congenital brain malformations, intracranial hemorrhage, meningitis, or intracranial mass lesions established at any time during a 3- to 10-year follow-up. The volume of the cerebral ventricles for each T2-weighted MRI sequence was calculated with a custom semiautomated segmentation program written in MATLAB. Normal percentile curves were calculated using the lambda-mu-sigma smoothing method.RESULTSVentricular volume was calculated for 687 normal brain MR images obtained in 617 different patients. A chart with standardized growth curves was developed from this set of normal ventricular volumes representing the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles. The charted data were binned by age at scan date by 3-month intervals for ages 0–1 year, 6-month intervals for ages 1–3 years, and 12-month intervals for ages 3–18 years. Additional percentile values were calculated for boys only and girls only.CONCLUSIONSThe authors developed centile estimation growth charts of normal 3D ventricular volumes measured on brain MRI for pediatric patients. These charts may serve as a quantitative clinical reference to help discern normal variance from pathologic ventriculomegaly.

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