Voxel techniques
in [Matlab]
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From: kunal varaiya on 30 Mar 2010 11:32 How to use voxel techniques in matlab than i can put all image , and i can see my result, because i have 2d image with finding (found fibre) , and i read IEEE paper and they told me now you have to apply Voxel techniques and then you can your result , but i dont know how to use voxel techniques...........
From: us on 30 Mar 2010 11:40 On Mar 30, 5:32 pm, "kunal varaiya" <kvara...(a)gmail.com> wrote: > How to use voxel techniques in matlab than i can put all image , and i can see my result, > > because i have 2d image with finding (found fibre) , and i read IEEE paper and they told me now you have to apply Voxel techniques and then you can your result , but i dont know how to use voxel techniques........... well... since voxels are simply 3D arrays: the whole world of ML is open to you... if you need more specific answers: ask more specific questions... us
From: ImageAnalyst on 30 Mar 2010 12:01 There's a section in the help on 3D volume visualization. Check it out. Or if you want true volumetric imaging that's just not available in MATLAB, check out Avizo: http://www.vsg3d.com/vsg_prod_avizo_overview.php Avizo goes way way farther in 3D "voxel" visualization than anything in MATLAB.
From: kunal varaiya on 30 Mar 2010 12:17 us <us(a)neurol.unizh.ch> wrote in message <e8b726e2-fc56-40fa-8232-b10e5350821e(a)g10g2000yqh.googlegroups.com>... > On Mar 30, 5:32 pm, "kunal varaiya" <kvara...(a)gmail.com> wrote: > > How to use voxel techniques in matlab than i can put all image , and i can see my result, > > > > because i have 2d image with finding (found fibre) , and i read IEEE paper and they told me now you have to apply Voxel techniques and then you can your result , but i dont know how to use voxel techniques........... > > well... since voxels are simply 3D arrays: the whole world of ML is > open to you... > if you need more specific answers: ask more specific questions... > > us Thanks for your reply but My question thesis next step is below : Synthetic test volumes were used to evaluate the method and to find suitable parameters for different fibre dimensions. Volume images with straight synthetic fibres were generated with known dimensions and orientations of the individual fibres and known fibre orientation distribution. Tubular fibres were generated to simulate wood fibres. Here, some results that illustrate the performance of the method are presented for six volumes, V1 – V6, with different orientation anisotropies. The size of the test volumes was 300 × 300 × 300 voxels, the fibre diameter 5 voxels, and the fibre length 50 voxels. The spatial filter size was 7 × 7 × 7 voxels, the centre frequency /2, and the bandwidth 2 octaves. The results from measuring the fibre orientation anisotropy are presented in Table 1, where r1, r2, and r3 denote the eigenvalues of the outer product matrix, A, sorted in descending order. As can be seen in the table, the estimated anisotropies correspond well to the ground truth. Volume renderings showing the ground truth orientations and the estimated orientations from a part of volume V2 are shown in Figure 1. The colours indicate the orientation in each voxel neighbourhood. As seen in the figure, the correct orientation is estimated for most voxels. The model does not hold for the fibre ends, where the largest errors occur. However, note that this error is too small to be clearly visible in the figure. The HSV colour space is used to construct the colour map used in Figure 1. The projection of the orientation vector on the xy plane is mapped to hue with the composite material that consists of a plastic matrix reinforced with wood fibres that was imaged using X-ray microtomography at the European Synchrotron Radiation Facility in Grenoble. The original volumes are large images of 1024 × 1024 × 1024 voxels with a voxel size of 0.7 × 0.7 × 0.7 μm3. This high resolution is used to measure other properties of the material, but is not needed to estimate the fibre orientation. Since the method does not assume tubular fibres and the fibre orientation is a relatively large-scale property the samples can be downsampled to reduce processing time. A volume of 342×342 ×342 voxels and the same parameters as for the synthetic data were used. The result is presented in Figure 3. Fibre orientation seems to be well estimated by the method since the same orientation is estimated for entire fibres and fibres in similar orientation have similar colour. For visualisation purposes the orientation is shown only in voxels with high image intensities that correspond to the fibre part of the material. The anisotropy is r1/r2 = 3.1524 and r2/r3 = 6.2838 using the eigenvalues of the outer product matrix A.
From: us on 30 Mar 2010 15:02 "kunal varaiya" <kvaraiya(a)gmail.com> wrote in message <hot862$k1h$1(a)fred.mathworks.com>... > us <us(a)neurol.unizh.ch> wrote in message <e8b726e2-fc56-40fa-8232-b10e5350821e(a)g10g2000yqh.googlegroups.com>... > > On Mar 30, 5:32 pm, "kunal varaiya" <kvara...(a)gmail.com> wrote: > > > How to use voxel techniques in matlab than i can put all image , and i can see my result, > > > > > > because i have 2d image with finding (found fibre) , and i read IEEE paper and they told me now you have to apply Voxel techniques and then you can your result , but i dont know how to use voxel techniques........... > > > > well... since voxels are simply 3D arrays: the whole world of ML is > > open to you... > > if you need more specific answers: ask more specific questions... > > > > us > > Thanks for your reply but > My question thesis next step is below : > Synthetic test volumes were used to evaluate the > method and to find suitable parameters for different fibre > dimensions. Volume images with straight synthetic > fibres were generated with known dimensions and orientations > of the individual fibres and known fibre orientation > distribution. Tubular fibres were generated to > simulate wood fibres. > Here, some results that illustrate the performance of > the method are presented for six volumes, V1 – V6, with > different orientation anisotropies. The size of the test > volumes was 300 × 300 × 300 voxels, the fibre diameter > 5 voxels, and the fibre length 50 voxels. The spatial > filter size was 7 × 7 × 7 voxels, the centre frequency > /2, and the bandwidth 2 octaves. The results from > measuring the fibre orientation anisotropy are presented > in Table 1, where r1, r2, and r3 denote the eigenvalues > of the outer product matrix, A, sorted in descending > order. As can be seen in the table, the estimated > anisotropies correspond well to the ground truth. > Volume renderings showing the ground truth orientations > and the estimated orientations from a part of volume > V2 are shown in Figure 1. The colours indicate the > orientation in each voxel neighbourhood. As seen in the > figure, the correct orientation is estimated for most voxels. > The model does not hold for the fibre ends, where > the largest errors occur. However, note that this error is > too small to be clearly visible in the figure. > The HSV colour space is used to construct the colour > map used in Figure 1. The projection of the orientation > vector on the xy plane is mapped to hue with the > composite material that consists of a plastic matrix reinforced > with wood fibres that was imaged using X-ray > microtomography at the European Synchrotron Radiation > Facility in Grenoble. The original volumes are > large images of 1024 × 1024 × 1024 voxels with a > voxel size of 0.7 × 0.7 × 0.7 μm3. This high resolution > is used to measure other properties of the material, > but is not needed to estimate the fibre orientation. Since > the method does not assume tubular fibres and the fibre > orientation is a relatively large-scale property the samples > can be downsampled to reduce processing time. A > volume of 342×342 ×342 voxels and the same parameters > as for the synthetic data were used. The result is > presented in Figure 3. Fibre orientation seems to be well > estimated by the method since the same orientation is > estimated for entire fibres and fibres in similar orientation > have similar colour. For visualisation purposes the > orientation is shown only in voxels with high image intensities > that correspond to the fibre part of the material. > The anisotropy is r1/r2 = 3.1524 and r2/r3 = 6.2838 > using the eigenvalues of the outer product matrix A. thanks for this exhaustive letter... now, down to the real world: WHAT is your question re ML(?)... us
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