Mailman 3 April 2017 - scikit-image

Re: [scikit-image] Tools for 3D segmentation ?
by Gregory Lee April 5, 2017

April 5, 2017

> I was wondering if anyone on the list knew about software to do 3d > segmentation. > > Thanks > Vighnesh > > A nice, free GUI-based tool for interactive 3D segmentation is itk-snap http://www.itksnap.org/

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Re: [scikit-image] Tools for 3D segmentation ?
by Stefan van der Walt April 5, 2017

April 5, 2017

On Wed, Apr 5, 2017, at 09:29, Vighnesh Birodkar wrote: > I was wondering if anyone on the list knew about software to do 3d > segmentation. There's so much going on in that space that I wouldn't even know where to start without understanding your problem in more detail. But here's a place to start, a blog post by Emmanuelle, one of our core devs: http://emmanuelle.github.io/segmentation-of-3-d-tomography-images-with-pyth… Stéfan

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Re: [scikit-image] Tools for 3D segmentation ?
by Joe Kington April 5, 2017

April 5, 2017

For basic segmentation, scipy.ndimage has what you're looking for. For more specalized/advanced methods, you'll need to go beyond it (e.g. itk), but scipy.ndimage is sufficient for most tasks. On Wed, Apr 5, 2017 at 12:44 PM, Graham Kosiba <graham.kosiba(a)gmail.com> wrote: > I have also done 3D segmentation in the past with imageJ and specifically > the 3Dviewer and boneJ plugins. > > Graham > > On Wed, Apr 5, 2017 at 12:29 PM, Vighnesh Birodkar < > vighneshbirodkar(a)gmail.com> wrote: > >> Hello guys >> >> I was wondering if anyone on the list knew about software to do 3d >> segmentation. >> >> Thanks >> Vighnesh >> >> _______________________________________________ >> scikit-image mailing list >> scikit-image(a)python.org >> https://mail.python.org/mailman/listinfo/scikit-image >> >> > > _______________________________________________ > scikit-image mailing list > scikit-image(a)python.org > https://mail.python.org/mailman/listinfo/scikit-image > >

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Re: [scikit-image] Tools for 3D segmentation ?
by Graham Kosiba April 5, 2017

April 5, 2017

I have also done 3D segmentation in the past with imageJ and specifically the 3Dviewer and boneJ plugins. Graham On Wed, Apr 5, 2017 at 12:29 PM, Vighnesh Birodkar < vighneshbirodkar(a)gmail.com> wrote: > Hello guys > > I was wondering if anyone on the list knew about software to do 3d > segmentation. > > Thanks > Vighnesh > > _______________________________________________ > scikit-image mailing list > scikit-image(a)python.org > https://mail.python.org/mailman/listinfo/scikit-image > >

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回复: Tools for 3D segmentation ?
by imagepy＠sina.com April 5, 2017

April 5, 2017

-------------------------------- I known itk has many 2d,3d segment raster algrism, and cgal has some vector algrism. but it is not a independent work, you must do some pretreatment and use the right method, then the segment work will be esaier. -------- 原始邮件 -------- 发件人:Vighnesh Birodkar 时间:2017年4月6日 00:30(星期四) 收件人:scikit-image@python.org 主题:[scikit-image] Tools for 3D segmentation ? Hello guys I was wondering if anyone on the list knew about software to do 3d segmentation. ThanksVighnesh _______________________________________________ scikit-image mailing list scikit-image(a)python.org https://mail.python.org/mailman/listinfo/scikit-image

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Tools for 3D segmentation ?
by Vighnesh Birodkar April 5, 2017

April 5, 2017

Hello guys I was wondering if anyone on the list knew about software to do 3d segmentation. Thanks Vighnesh

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Re: [scikit-image] 3D to 2D projective transformation
by Stefan van der Walt April 5, 2017

April 5, 2017

On Tue, Apr 4, 2017, at 16:48, Shrawan wrote: > Could any one help me with a problem that involves finding > registration parameters between a 3D volume data and a 2D perspective > projection? The 3D data is from light microscopy and 2D data is from > scanning electron microscopy and I can provide more details if needed. > If such a tool doesn't exist already in scikit-image, would anyone be > interested in creating one? Of course, the person would be recognized > for his/her work in the peer-reviewed article that develops from this > collaboration. Thank you in advance You may want to direct this question to the nipy list: https://mail.python.org/mailman/listinfo/neuroimaging At least two of their packages do registration on volumetric data. scikit-image does not currently have this implemented. Stéfan

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3D to 2D projective transformation
by Shrawan April 4, 2017

April 4, 2017

Dear scikit-image developers, Could any one help me with a problem that involves finding registration parameters between a 3D volume data and a 2D perspective projection? The 3D data is from light microscopy and 2D data is from scanning electron microscopy and I can provide more details if needed. If such a tool doesn't exist already in scikit-image, would anyone be interested in creating one? Of course, the person would be recognized for his/her work in the peer-reviewed article that develops from this collaboration. Thank you in advance Best, Shrawan -- Shrawan Kumar Mageswaran Postdoctoral Scholar Jensen Lab California Institute of Technology

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Re: [scikit-image] How to see full image after transformation not the cropped one
by Serge Shakhov April 4, 2017

April 4, 2017

Hi Stefan, Thanks for your reply.My experience with scikit-image just started with this script, I'm not experienced at all.Here is the test script:import skimage.transform import matplotlib.pyplot as plt from PIL import Image from skimage import data from skimage.transform import resize from skimage.transform import rescale import numpy as np import os from skimage import io srcIm = data.checkerboard() src = np.array([[(0,0),(100,0),(110,0)],[(0,110),(100,110),(110,110)]]) dst = np.array([[(0,0),(50,0),(70,0)],[(0,20),(60,50),(100,100)]]) src = src.astype(float) dst = dst.astype(float) #revert to 1D list src = src.reshape(((src.shape[0]*src.shape[1]),2), order='F') dst = dst.reshape(((dst.shape[0]*dst.shape[1]),2), order='F') #Perform transform piecewiseAffine = skimage.transform.PiecewiseAffineTransform() piecewiseAffine.estimate(src,dst) dstArr = skimage.transform.warp(srcIm, piecewiseAffine, order=1, mode='constant', cval=0, clip=False, preserve_range=False) #Visualise result dstArr = np.array(dstArr * 255., dtype=np.uint8) dstIm = Image.fromarray(dstArr) dstIm.show()Could you help me and point to what I have to modify here? If I reverse the piecewiseAffine.estimate(src,dst) -->> piecewiseAffine.estimate(dst, src)I can get the opposite transformation and it's not cropped, but not the one I need. Thanks. Best regards, Serge Shakhov. On Tuesday, 4 April 2017, 3:56, Stefan van der Walt <stefanv(a)berkeley.edu> wrote: Hi Serge On Sun, Apr 2, 2017, at 15:30, Serge Shakhov via scikit-image wrote: But I didn't find an answer. I'm doing 2D piecewise affine transformation with skimage.transform.PiecewiseAffineTransform Result image is heavily cropped. I tried to play with output_shape, mode and clip parameters but without any effect, image is still cropped. Could anyone point me what am I doing wrong? I thought we had this implemented, but I guess not yet. You can see how here: https://github.com/scikit-image/skimage-tutorials/blob/master/lectures/adv3… Specifically: from skimage.transform import SimilarityTransform # Shape of middle image, our registration target r, c = image.shape # Note that transformations take coordinates in (x, y) format, # not (row, column), in order to be consistent with most literature corners = np.array([[0, 0], [0, r], [c, 0], [c, r]]) # Warp the image corners to their new positions warped_corners = my_tf(corners) # The overall output shape will be max - min corner_min = np.min(corners, axis=0) corner_max = np.max(corners, axis=0) output_shape = (corner_max - corner_min) # Ensure integer shape with np.ceil and dtype conversion output_shape = np.ceil(output_shape[::-1]).astype(int) That calculates the shape you want. You now need to modify the transform to output an image inside of this shape: from skimage.transform import warp # This in-plane offset is the only necessary transformation for the middle image offset = SimilarityTransform(translation=-corner_min) shifted_transform = (my_tf + offset).inverse pano0_warped = warp(image, shifted_transform, order=3, output_shape=output_shape, cval=-1) Let us know how that goes! Stéfan _______________________________________________ scikit-image mailing list scikit-image(a)python.org https://mail.python.org/mailman/listinfo/scikit-image

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Re: [scikit-image] Fwd: SSIM with tolerances
by Stefan van der Walt April 3, 2017

April 3, 2017

Hi Mat On Mon, Apr 3, 2017, at 12:51, mat saunders wrote: > Thank you for feedback I will look at it more closely and see what I > can come up with. I did not know about the matplotlib comparison so I > will see what they do. I did come across this the other day and > wondered about down scaling. > http://tech.jetsetter.com/2017/03/21/duplicate-image-detection/ I suspect this mechanism is too crude for what you want---videos often have small codec artifacts, and you want to be insensitive to those. E.g., you may not care much about introducing blocking / smudging, but very much about color changes. Or, since you are working with video, perhaps you only care about key-frames, or only about sliding window (over time) averages. Feel free to give me a call sometime if you want to brainstorm ideas. Best regards Stéfan

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