Mailman 3 November 2018 - scikit-image

回复:Re: Medial axis skeletonization length
by imagepy＠sina.com 11 Dec '18

11 Dec '18

you can also try sknw ----- 原始邮件 ----- 发件人: Leena Chourey<leenagour(a)gmail.com> 收件人: Mailing list for scikit-image (http://scikit-image.org)<scikit-image(a)python.org> 主题: [scikit-image] Re: Medial axis skeletonization length 日期: 2018-11-30 09:10 hi tried installing skan, successful install, but getting error while using : --------------------------------------------------------------- from skan import csr Traceback (most recent call last): File "<input>", line 1, in <module> File "/home/etu/.eclipse/org.eclipse.platform_3.8_155965261/plugins/org.python.pydev_4.5.5.201603221110/pysrc/_pydev_bundle/pydev_import_hook.py", line 21, in do_import module = self._system_import(name, *args, **kwargs) File "/usr/local/lib/python2.7/dist-packages/skan/__init__.py", line 1, in <module> from .csr import skeleton_to_csgraph, branch_statistics, summarise File "/home/etu/.eclipse/org.eclipse.platform_3.8_155965261/plugins/org.python.pydev_4.5.5.201603221110/pysrc/_pydev_bundle/pydev_import_hook.py", line 21, in do_import module = self._system_import(name, *args, **kwargs) File "/usr/local/lib/python2.7/dist-packages/skan/csr.py", line 164 junction_centroids, *, spacing=1): ^ SyntaxError: invalid syntax On Thu, Nov 29, 2018 at 6:20 AM Juan Nunez-Iglesias <jni(a)fastmail.com> wrote: Hi Stefanie, Jean-Patrick, et al, I invite you to look at my Skan (skeleton analysis) package. Docs here: https://jni.github.io/skan/ Paper here: https://peerj.com/articles/4312/ (the supplemental info mentions some of the edge cases we’ve taken care of.) Skan is installable with "pip install skan" or "conda install -c conda-forge skan”. I’m working on a new higher level Skeleton class, but it’s a WIP and you’ll need to install from source: https://github.com/jni/skan/blob/5c0d23e5b1eab2fb06b25ff599e98eaa11d621e6/s… (Not that that’s a big deal since the lib is pure Python.) Comments, suggestions, and contributions are all welcome! Juan. On 29 Nov 2018, at 8:37 am, Jean-Patrick Pommier <jeanpatrick.pommier(a)gmail.com> wrote: I tried this : https://dip4fish.blogspot.com/2014/05/construct-graph-from-skeleton-image-o… Le mer. 28 nov. 2018 à 19:59, Stefanie Lück <luecks(a)gmail.com> a écrit : Dear all, I have a question I use the medial axis skeletonization implementation of scikit-image. Is it possible to get the length of the medial axis and the number of branches growing from the medial axis? Thank you very much in advance Stefanie _______________________________________________ scikit-image mailing list -- scikit-image(a)python.org To unsubscribe send an email to scikit-image-leave(a)python.org -- http://dip4fish.blogspot.fr/ Dedicated to Digital Image Processing for FISH, QFISH and other things about the telomeres. _______________________________________________ scikit-image mailing list -- scikit-image(a)python.org To unsubscribe send an email to scikit-image-leave(a)python.org _______________________________________________ scikit-image mailing list -- scikit-image(a)python.org To unsubscribe send an email to scikit-image-leave(a)python.org _______________________________________________ scikit-image mailing list -- scikit-image(a)python.org To unsubscribe send an email to scikit-image-leave(a)python.org 来自新浪邮箱触屏版

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回复：Re: 回复:Re: Converting an image to a skeleton
by imagepy＠sina.com 01 Dec '18

01 Dec '18

Please see sknw's document on github's read me. the default multi = Falseand if it's a multigraph, you must add a index after two node id to get the edge, like: graph.edge(id1, id2)[0]graph = sknw.build_sknw(ske， multi=False)ske: should be a nd skeleton imagemulti: if True，a multigraph is retured, which allows more than one edge between two nodes and self-self edge. default is False.return: is a networkx Graph object----- 原始邮件 ----- 发件人：Deepa <deepamahm.iisc(a)gmail.com> 收件人：imagepy(a)sina.com, scikit-image(a)python.org 主题：Re: [scikit-image] 回复:Re: Converting an image to a skeleton 日期：2018年11月30日 17点36分 I checked the sknw package .I'm using the input matrix data of the skeleton image that is created using Mathematica.Here is my code that is written to highlight all the nodes and edges from skimage.morphology import skeletonizefrom skimage import dataimport sknwimport numpy as npimport matplotlib.pyplot as pltimport scipy.io as spioimport networkx as nxfrom networkx.drawing.nx_pydot import write_dotmat = spio.loadmat('file.mat', squeeze_me=True)print(type(mat))print(type(mat.get('Expression1')))print(mat.get('Expression1'))img1 = mat.get('Expression1')print(img1)ske = skeletonize(img1).astype(np.uint16)# build graph from skeletongraph = sknw.build_sknw(ske) # draw imageplt.imshow(img1, cmap='gray') # draw edges by ptsfor (s,e) in graph.edges(): ps = graph[s][e]['pts'] plt.plot(ps[:,1], ps[:,0], 'green') # draw node by onode, nodes = graph.node, graph.nodes()ps = np.array([node[i]['o'] for i in nodes])plt.plot(ps[:,1], ps[:,0], 'r.')#pos = nx.nx_agraph.graphviz_layout(graph)#print(pos)#plt.findpath(img1)# title and showplt.title('Build Graph')plt.show() Please find the input file.mat file here. Even with sknw package I face the same problem when there are multiple edges.Please the output image attached. Some edges are not highlighted in green. Could you please suggest how this can be improved? On Thu, Nov 29, 2018 at 7:33 PM <imagepy(a)sina.com> wrote: please see my mail earlier，skan and sknw on github. ----- 原始邮件 ----- 发件人: Leena Chourey<leenagour(a)gmail.com> 收件人: Mailing list for scikit-image (http://scikit-image.org)<scikit-image(a)python.org> 主题: [scikit-image] Re: Converting an image to a skeleton 日期: 2018-11-29 19:40 I need solution for same. Pls share. On Thu, 29 Nov 2018, 16:37 Deepa, <deepamahm.iisc(a)gmail.com> wrote: I would like to generate a skeleton out of an image. The resulting output of the skeleton image has disconnected edges. import skimage from skimage import data,io,filters import numpy as np import cv2 import matplotlib.pyplot as plt from skimage.filters import threshold_adaptive,threshold_mean from skimage.morphology import binary_dilation from skimage import feature from skimage.morphology import skeletonize_3d imgfile = "Bagah.jpeg" im = cv2.imread(imgfile) image = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) thresh = threshold_mean(image) thresh = threshold_mean(thresh) binary = image > thresh #dilate = skimage.morphology.binary_dilation(binary) gaussian = skimage.filters.gaussian(binary) edges = filters.sobel(gaussian) #dilate = feature.canny(edges)#binary,sigma=0) skeleton = skeletonize_3d(gaussian)#binary) fig, axes = plt.subplots(nrows=2,ncols=2, figsize=(8, 2)) ax = axes.ravel() ax[0].imshow(gaussian, cmap=plt.cm.gray) ax[0].set_title('gaussian') ax[1].imshow(skeleton, cmap=plt.cm.gray) ax[1].set_title('skeleton') for a in ax: a.axis('off') plt.show() Please find the attachments of my input and output files. I would like to translate this skeleton into a graph with nodes and edges. Could someone suggest how to obtain a skeleton with connected edges? I’m protected online with Avast Free Antivirus. Get it here — it’s free forever. _______________________________________________ scikit-image mailing list -- scikit-image(a)python.org To unsubscribe send an email to scikit-image-leave(a)python.org _______________________________________________ scikit-image mailing list -- scikit-image(a)python.org To unsubscribe send an email to scikit-image-leave(a)python.org 来自新浪邮箱触屏版_______________________________________________ scikit-image mailing list -- scikit-image(a)python.org To unsubscribe send an email to scikit-image-leave(a)python.org

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回复:Re: Converting an image to a skeleton
by imagepy＠sina.com 30 Nov '18

30 Nov '18

please see my mail earlier，skan and sknw on github. ----- 原始邮件 ----- 发件人: Leena Chourey<leenagour(a)gmail.com> 收件人: Mailing list for scikit-image (http://scikit-image.org)<scikit-image(a)python.org> 主题: [scikit-image] Re: Converting an image to a skeleton 日期: 2018-11-29 19:40 I need solution for same. Pls share. On Thu, 29 Nov 2018, 16:37 Deepa, <deepamahm.iisc(a)gmail.com> wrote: I would like to generate a skeleton out of an image. The resulting output of the skeleton image has disconnected edges. import skimage from skimage import data,io,filters import numpy as np import cv2 import matplotlib.pyplot as plt from skimage.filters import threshold_adaptive,threshold_mean from skimage.morphology import binary_dilation from skimage import feature from skimage.morphology import skeletonize_3d imgfile = "Bagah.jpeg" im = cv2.imread(imgfile) image = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) thresh = threshold_mean(image) thresh = threshold_mean(thresh) binary = image > thresh #dilate = skimage.morphology.binary_dilation(binary) gaussian = skimage.filters.gaussian(binary) edges = filters.sobel(gaussian) #dilate = feature.canny(edges)#binary,sigma=0) skeleton = skeletonize_3d(gaussian)#binary) fig, axes = plt.subplots(nrows=2,ncols=2, figsize=(8, 2)) ax = axes.ravel() ax[0].imshow(gaussian, cmap=plt.cm.gray) ax[0].set_title('gaussian') ax[1].imshow(skeleton, cmap=plt.cm.gray) ax[1].set_title('skeleton') for a in ax: a.axis('off') plt.show() Please find the attachments of my input and output files. I would like to translate this skeleton into a graph with nodes and edges. Could someone suggest how to obtain a skeleton with connected edges? I’m protected online with Avast Free Antivirus. Get it here — it’s free forever. _______________________________________________ scikit-image mailing list -- scikit-image(a)python.org To unsubscribe send an email to scikit-image-leave(a)python.org _______________________________________________ scikit-image mailing list -- scikit-image(a)python.org To unsubscribe send an email to scikit-image-leave(a)python.org 来自新浪邮箱触屏版

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回复:Re: Medial axis skeletonization length
by imagepy＠sina.com 30 Nov '18

30 Nov '18

if 3d stack, make sure the images is solid without hole, else the result will be a bubble, it may lead a error when track a edge. ----- 原始邮件 ----- 发件人: Leena Chourey<leenagour(a)gmail.com> 收件人: Mailing list for scikit-image (http://scikit-image.org)<scikit-image(a)python.org> 主题: [scikit-image] Re: Medial axis skeletonization length 日期: 2018-11-30 09:10 hi tried installing skan, successful install, but getting error while using : --------------------------------------------------------------- from skan import csr Traceback (most recent call last): File "<input>", line 1, in <module> File "/home/etu/.eclipse/org.eclipse.platform_3.8_155965261/plugins/org.python.pydev_4.5.5.201603221110/pysrc/_pydev_bundle/pydev_import_hook.py", line 21, in do_import module = self._system_import(name, *args, **kwargs) File "/usr/local/lib/python2.7/dist-packages/skan/__init__.py", line 1, in <module> from .csr import skeleton_to_csgraph, branch_statistics, summarise File "/home/etu/.eclipse/org.eclipse.platform_3.8_155965261/plugins/org.python.pydev_4.5.5.201603221110/pysrc/_pydev_bundle/pydev_import_hook.py", line 21, in do_import module = self._system_import(name, *args, **kwargs) File "/usr/local/lib/python2.7/dist-packages/skan/csr.py", line 164 junction_centroids, *, spacing=1): ^ SyntaxError: invalid syntax On Thu, Nov 29, 2018 at 6:20 AM Juan Nunez-Iglesias <jni(a)fastmail.com> wrote: Hi Stefanie, Jean-Patrick, et al, I invite you to look at my Skan (skeleton analysis) package. Docs here: https://jni.github.io/skan/ Paper here: https://peerj.com/articles/4312/ (the supplemental info mentions some of the edge cases we’ve taken care of.) Skan is installable with "pip install skan" or "conda install -c conda-forge skan”. I’m working on a new higher level Skeleton class, but it’s a WIP and you’ll need to install from source: https://github.com/jni/skan/blob/5c0d23e5b1eab2fb06b25ff599e98eaa11d621e6/s… (Not that that’s a big deal since the lib is pure Python.) Comments, suggestions, and contributions are all welcome! Juan. On 29 Nov 2018, at 8:37 am, Jean-Patrick Pommier <jeanpatrick.pommier(a)gmail.com> wrote: I tried this : https://dip4fish.blogspot.com/2014/05/construct-graph-from-skeleton-image-o… Le mer. 28 nov. 2018 à 19:59, Stefanie Lück <luecks(a)gmail.com> a écrit : Dear all, I have a question I use the medial axis skeletonization implementation of scikit-image. Is it possible to get the length of the medial axis and the number of branches growing from the medial axis? Thank you very much in advance Stefanie _______________________________________________ scikit-image mailing list -- scikit-image(a)python.org To unsubscribe send an email to scikit-image-leave(a)python.org -- http://dip4fish.blogspot.fr/ Dedicated to Digital Image Processing for FISH, QFISH and other things about the telomeres. _______________________________________________ scikit-image mailing list -- scikit-image(a)python.org To unsubscribe send an email to scikit-image-leave(a)python.org _______________________________________________ scikit-image mailing list -- scikit-image(a)python.org To unsubscribe send an email to scikit-image-leave(a)python.org _______________________________________________ scikit-image mailing list -- scikit-image(a)python.org To unsubscribe send an email to scikit-image-leave(a)python.org 来自新浪邮箱触屏版

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Medial axis skeletonization length
by Stefanie Lück 30 Nov '18

30 Nov '18

Dear all, I have a question I use the medial axis skeletonization implementation of scikit-image. Is it possible to get the length of the medial axis and the number of branches growing from the medial axis? Thank you very much in advance Stefanie

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Converting an image to a skeleton
by Deepa 30 Nov '18

30 Nov '18

I would like to generate a skeleton out of an image. The resulting output of the skeleton image has disconnected edges. import skimage from skimage import data,io,filters import numpy as np import cv2 import matplotlib.pyplot as plt from skimage.filters import threshold_adaptive,threshold_mean from skimage.morphology import binary_dilation from skimage import feature from skimage.morphology import skeletonize_3d imgfile = "Bagah.jpeg" im = cv2.imread(imgfile) image = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) thresh = threshold_mean(image) thresh = threshold_mean(thresh) binary = image > thresh #dilate = skimage.morphology.binary_dilation(binary) gaussian = skimage.filters.gaussian(binary) edges = filters.sobel(gaussian) #dilate = feature.canny(edges)#binary,sigma=0) skeleton = skeletonize_3d(gaussian)#binary) fig, axes = plt.subplots(nrows=2,ncols=2, figsize=(8, 2)) ax = axes.ravel() ax[0].imshow(gaussian, cmap=plt.cm.gray) ax[0].set_title('gaussian') ax[1].imshow(skeleton, cmap=plt.cm.gray) ax[1].set_title('skeleton') for a in ax: a.axis('off') plt.show() Please find the attachments of my input and output files. I would like to translate this skeleton into a graph with nodes and edges. Could someone suggest how to obtain a skeleton with connected edges? <https://www.avast.com/en-in/recommend?utm_medium=email&utm_source=link&utm_…> I’m protected online with Avast Free Antivirus. Get it here — it’s free forever. <https://www.avast.com/en-in/recommend?utm_medium=email&utm_source=link&utm_…> <#DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2>

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回复：Re: Medial axis skeletonization length
by imagepy＠sina.com 29 Nov '18

29 Nov '18

https://github.com/jni/skan,https://github.com/yxdragon/sknw I think these two packages is what you needed. BestYXDragon ----- 原始邮件 ----- 发件人：Jean-Patrick Pommier <jeanpatrick.pommier(a)gmail.com> 收件人："Mailing list for scikit-image (http://scikit-image.org)" <scikit-image(a)python.org> 主题：[scikit-image] Re: Medial axis skeletonization length 日期：2018年11月29日 05点40分 I tried this : https://dip4fish.blogspot.com/2014/05/construct-graph-from-skeleton-image-o… Le mer. 28 nov. 2018 à 19:59, Stefanie Lück <luecks(a)gmail.com> a écrit : Dear all, I have a question I use the medial axis skeletonization implementation of scikit-image. Is it possible to get the length of the medial axis and the number of branches growing from the medial axis? Thank you very much in advanceStefanie _______________________________________________ scikit-image mailing list -- scikit-image(a)python.org To unsubscribe send an email to scikit-image-leave(a)python.org -- http://dip4fish.blogspot.fr/ Dedicated to Digital Image Processing for FISH, QFISH and other things about the telomeres. _______________________________________________ scikit-image mailing list -- scikit-image(a)python.org To unsubscribe send an email to scikit-image-leave(a)python.org

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glue v0.14 released
by Stefan van der Walt 26 Nov '18

26 Nov '18

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Perimeter and neighborhood
by Yann GAVET 16 Nov '18

16 Nov '18

I played around with the perimeter from skimage.measure. In the documentation, the neighborhood is described as: "neighbourhood : 4 or 8, optional Neighborhood connectivity for border pixel determination." There is an imprecision because this is different from the connectivity of the resulting contour. If N4 is chosen, then the contour is 8-connected, and conversely. The documentation could have this precision. yann -- Yann GAVET Assistant Professor - Ecole Nationale Supérieure des Mines de Saint-Etienne 158 Cours Fauriel, CS 62362, 42023 SAINT-ETIENNE cedex 2 Tel: (33) - 4 7742 0170

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回复：Re: DICOM Image analyses
by imagepy＠sina.com 15 Nov '18

15 Nov '18

scipy.ndimage and scikit-image is very suitable for dicom image process, and SimpleITK is recommended.and sometimes you may need some interactive operation, so you can have a try https://github.com/Image-Py/imagepy, It is a ui framework you can use many scipy.ndimage and skimage method without coding, and you can extend it by writting plugins, and https://github.com/Image-Py/itk-plgs is a itk plugins, (pydicom can not open some compressed pydicom, but SimpleITK can!)----- 原始邮件 ----- 发件人：Josh Warner <silvertrumpet999(a)gmail.com> 收件人："Mailing list for scikit-image (http://scikit-image.org)" <scikit-image(a)python.org> 主题：[scikit-image] Re: DICOM Image analyses 日期：2018年11月15日 03点54分 Look into pydicom to load your images. Then, you will need to either manually or - more difficult - automatically segment the breast(s), excluding the pectoralis muscles and adjacent air. The data in your images should be Hounsfield units, which are calibrated to quantitatively measure density (0 is water, -1000 is air at the scanner, everything positive is more dense than water). There are many published ranges you could use to start with to differentiate fat from non-fat tissue in your area of interest. Scikit-Image will likely be most helpful in the segmentation phase. There are several thresholding algorithms which may be of use also, if a simple threshold isn't ideal. Please note that differentiating fat from other soft tissue is relatively easy; differentiating glandular from 'other soft tissue' is not as they will share the same density. If this is a key part of the project, simple density-based thresholding will not be sufficient. On Wed, Nov 14, 2018 at 12:45 PM <deborah.akuoko(a)postgrad.manchester.ac.uk> wrote: I have over 500 images from cancer imaging archive. All these are CT images of the chest area (breast). I want to use scikit image to determine which part of the CT represent Fatty tissue and which part represents glands tissue. This might be based of gray scale levels or density I’m not sure. For instance I want to be able to label image 01 as : 10% fatty tissue 80% fibro glandular tissue and 10% other soft tissue I have never used scikit image for anything but I have some python programming experience I really need a good beginner explanation to get this done using sci kit image. I have seen the documentation but it’s too general. I need something specific for what I want to accomplish as stated above. Please assist if possible. _______________________________________________ scikit-image mailing list -- scikit-image(a)python.org To unsubscribe send an email to scikit-image-leave(a)python.org _______________________________________________ scikit-image mailing list -- scikit-image(a)python.org To unsubscribe send an email to scikit-image-leave(a)python.org

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