MATLAB Central – How to create 3D mesh model?

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Subject: How to create 3D mesh model?

From: Tong

Date: 14 Jul, 2009 19:55:03

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I have segmented meniscus images from MRI that is created in about 3mm slices. How would I combine these slices together to create a 3D model of the meniscus?

Subject: How to create 3D mesh model?

From: Luigi Giaccari

Date: 14 Jul, 2009 20:49:03

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Please send me that models of yours, I am plannig to build a surface recostructor for sliced cloud. Send to : giaccariluigi@msn.com

In the mean time look for:

http://www.mathworks.com/matlabcentral/fileexchange/22185
http://giaccariluigi.altervista.org/blog/

and related

Subject: How to create 3D mesh model?

From: Brad Henrie

Date: 17 Jul, 2009 21:45:18

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‘Tong ‘ <celticbaseball06@gmail.com> wrote in message <h3inqn$ni5$1@fred.mathworks.com>…
> I have segmented meniscus images from MRI that is created in about 3mm slices. How would I combine these slices together to create a 3D model of the meniscus?

First place all of your slices into a 3-d matrix. This will give you a cube of data. You can then view it from multiple planes by using this format variable(:,:,a) where a is the slice position in a direction directly into your displayed image. Using the same format you can display other planes variable(:,a,:). Converting your image to greyscale will allow you to display it using implay.

I’m sure that since you are working with MRI you have access to the image processing toolbox.

While viewing images in a plane where the pixels are not square you need to scale your image. (if you have a 3x3x5 voxel and display the 3×5 pixel representation) Also remember your slice separation if you don’t have 3-d k-space.

Subject: How to create 3D mesh model?

From: Image Analyst

Date: 18 Jul, 2009 04:02:35

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‘Tong ‘ <celticbaseball06@gmail.com> wrote in message <h3inqn$ni5$1@fred.mathworks.com>…
> I have segmented meniscus images from MRI that is created in about 3mm slices. How would I combine these slices together to create a 3D model of the meniscus?
—————————————-
I’m not sure what you mean by ‘model,’ but you can combine 2D images together to form a 3D image by using the cat(3, slice1, slice2, slice3, slice4, slice5,……) function.

Subject: How to create 3D mesh model?

From: Tong

Date: 20 Jul, 2009 18:36:02

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‘Image Analyst’ <imageanalyst@mailinator.com> wrote in message <h3rhgr$of5$1@fred.mathworks.com>…
> ‘Tong ‘ <celticbaseball06@gmail.com> wrote in message <h3inqn$ni5$1@fred.mathworks.com>…
> > I have segmented meniscus images from MRI that is created in about 3mm slices. How would I combine these slices together to create a 3D model of the meniscus?
> —————————————-
> I’m not sure what you mean by ‘model,’ but you can combine 2D images together to form a 3D image by using the cat(3, slice1, slice2, slice3, slice4, slice5,……) function.

What about when I am using regions of interest, not images?

Subject: How to create 3D mesh model?

From: fabio freschi

Date: 20 Jul, 2009 21:06:01

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you can try iso2mesh in FE
fabio »

leica SP2 confocal microscopy

Leica AOBS SP2 confocal microscopy

http://www.aecom.yu.edu/aif/instructions/aobs/index.htm

The Leica confocal is one of a few laser scanning confocal microscopes in the AIF.  The  Leica may be programmed to scan many different wavelength ranges.   The Leica AOBS uses variable spectral detection instead of traditional emission filters.  There are a few laser lines for excitation and the dyes are limited by these.  Any dye that is excited at one of these following wavelengths can be used:   405; 458; 476; 488; 514; 561; and 633 nm.   According to the sales literature, the lasers are:  diode 20 mW 405 nm; Ar 100 mW 457 nm, 488 nm, 514 nm; diode 10mW 561 nm; HeNe 10 mW 633 nm.

Objectives:
63X N.A. 1.4-0.60 Oil l_BL HCX PL APO
40X N.A.  1.25-0.75 Oil CS HCX PL APO
20X N.A.  0.70 1mm corr l_BL HC PL APO

Automated stage for tiling XY, XYZ or XYT  volumes with 166.6 mm of travel in Z.

Here’s a picture of the new system as installed at the AIF on November 6, 2002.

Contents:

• Main manual for turn on / shut down
• Environmental control for live cell work
• A few example images

Leica Application Notes:

• FRAP
• More FRAP
• appnotes/app_note_custom_templates2.PDF
• appnotes/app_note_customize_profile2.pdf
• appnotes/app_note_customize_toolbars2.pdf
• appnotes/Sequential-Scanning-Letter.pdf
• appnotes/app_note_quantify.pdf
• appnotes/app_note_Scale_Bar_1537.pdf
• appnotes/app_note_LCS_backup.pdf

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Sample method section for a paper:

Images were collected with a Leica TCS SP2 AOBS confocal microscope (Mannheim, Germany) with 25X and 60X oil immersion optics. Laser lines at 488nm and 543nm for excitation of Cy2 and Cy3 were provided by an Ar laser and a HeNe laser. Detection ranges were set to eliminate crosstalk between fluorophores.

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Selected Bibliography

1. Azios, NG, Krishnamoorthy, L, Harris, M., Cubano, LA, Cammer, M, Dharmawardhane, SF.   Estrogen and Resveratrol Regulate Rac and Cdc42 Signaling to the Actin Cytoskeleton of Metastatic Breast Cancer Cells. Neoplasia. 2007 Feb; 9(2):147-158.
2. Bhatia S, Edidin M, Almo SC, Nathenson SG. (2005) Different cell surface oligomeric states of B7-1 and B7-2: Implications for signaling. Proc Natl Acad Sci U S A. 102(43):15569-15574. PMID: 16221763
3. Eugenin EA, Berman JW.  Gap junctions mediate human immunodeficiency virus-bystander killing in astrocytes. J Neurosci. 2007 Nov 21;27(47):12844-50. PMID: 18032656
4. Herskovits AZ, Davies P. (2006) The regulation of tau phosphorylation by PCTAIRE 3: Implications for the pathogenesis of Alzheimer’s disease. Neurobiol Dis. 2006 Jun 9; [Epub ahead of print] PMID: 16766195
5. Lazar-Molnar E, Almo SC, Nathenson SG.  The interchain disulfide linkage is not a prerequisite but enhances CD28 costimulatory function. Cell Immunol. 2007 Apr 27; [Epub ahead of print] PMID: 17467674
6. Maxson, ME, Cook, E, Casadevall, A, Zaragoza, O.  The volume and hydration of the Cryptococcus neoformans polysaccharide capsule. Fungal Genetics and Biology. March 2007; 44(3)180-186
7. Shav-Tal Y, Darzacq X, Shenoy SM, Fusco D, Janicki SM, Spector DL, Singer RH. (2004) Dynamics of Single mRNPs in Nuclei of Living Cells. Science 304(5678):1797-1800.
8. Zhang X, Schwartz JC, Guo X, Bhatia S, Cao E, Lorenz M, Cammer M, Chen L, Zhang ZY, Edidin MA, Nathenson SG, Almo SC. (2004) Structural and functional analysis of the costimulatory receptor programmed death-1. Immunity. 20(3):337-47.

SPM version 8b statistical parametric mapping MATLAB

les 2 liens les plus « usefull »:

http://www.fil.ion.ucl.ac.uk/spm/software/spm8b/#Introduction

http://www.fil.ion.ucl.ac.uk/spm/doc/intro/

http://www.fil.ion.ucl.ac.uk/spm/ext/

The SPM approach in brief

The Statistical Parametric Mapping approach is voxel based:

• Images are realigned, spatially normalised into a standard space, and smoothed.
• Parametric statistical models are assumed at each voxel, using the General Linear Model GLM to describe the data in terms of experimental and confounding effects, and residual variability.
• For fMRI the GLM is used in combination with a temporal convolution model.
• Classical statistical inference is used to test hypotheses that are expressed in terms of GLM parameters. This uses an image whose voxel values are statistics, a Statistic Image, or Statistical Parametric Map (SPM{t}, SPM{Z}, SPM{F})
• For such classical inferences, the multiple comparisons problem is addressed using continuous random field theory RFT, assuming the statistic image to be a good lattice representation of an underlying continuous stationary random field. This results in inference based on corrected p-values.
• Bayesian inference can be used in place of classical inference resulting in Posterior Probability Maps PPMs .
• For fMRI, analyses of effective connectivity can be implemented using Dynamic Causal Modelling DCM.