Mailman 3 March 2015 - scikit-image

Range of beta values in segmentation algorithm?
by Yuta Sato 12 Mar '15

12 Mar '15

In the following skimage.segmentation.random_walker algorithm: What is the range of 'beta' values that can be supplied? I am working with a single band 8bit unsigned image. Is it 0 to 255? skimage.segmentation.random_walker(data, labels, beta=130, mode='bf', tol=0.001, copy=True,multichannel=False, return_full_prob=False, spacing=None) beta : float [Penalization coefficient for the random walker motion (the greater beta, the more difficult the diffusion)] Thanks for your support.

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Re: Equivalent of watershed for cutting connected components of an image of particles?
by Thomas Caswell 11 Mar '15

11 Mar '15

Jumping in from the peanut gallery, can you reliable identify when the segmentation has gone sideways? Looking at the second moment, area to bounding box area, or some other compactness measure? If you can get away with it, you could just drop the offending cells. If not, then you can try eroding the joined cells until they split into multiple segments. Tom On Wed, Mar 11, 2015, 17:15 Claiborne Morton <claiborne.morton(a)gmail.com> wrote: > Hey thanks for the help, here are a few other issues we are running into. > When a sickle cell is in contact with a regular cell, we cannot find a way > to separate the two. Also bottom-middle circle is of a healthy blood cell > that is on its side. The watershed function tends to break these cells into > two or more partitions when the should not be separated. > Any idea on how to fix these problems? > > > On Tue, Mar 10, 2015 at 2:12 PM, Claiborne Morton < > claiborne.morton(a)gmail.com> wrote: > >> Hey guys, Im following up on Adam's behalf, but this is an example of an >> image we are working with in trying to separate cells that are touching >> each other. >> Also you can see the top middle particle has a crescent shape, but is >> actually a healthy red blood cell that has been segmented incorrectly >> because of glare. Is that a way to connect the two tips of the shape so >> that I could then run "binary_fill_holes()" to correctly segment the cell. >> Thanks! >> >> >> On Wednesday, February 18, 2015 at 7:04:10 PM UTC-5, Adam Hughes wrote: >> >>> Hi, >>> >>> In ImageJ, one can select watershedding to break up connected regions of >>> particles. Are there any examples of using watershed in this capacity in >>> scikit image? All of the examples I see seem to use watershedding to do >>> segmentation, not to break connected particles in an already-segmented >>> black and white image. >>> >>> Also, is there a straightforward way to remove particles on a the edge >>> of an image? Sorry, googling is failing me, but I know this is possible. >>> >>> Thanks >>> >> -- >> You received this message because you are subscribed to a topic in the >> Google Groups "scikit-image" group. >> To unsubscribe from this topic, visit >> https://groups.google.com/d/topic/scikit-image/VL6SZTWvAz8/unsubscribe. >> To unsubscribe from this group and all its topics, send an email to >> scikit-image+unsubscribe(a)googlegroups.com. > > >> For more options, visit https://groups.google.com/d/optout. >> > -- > You received this message because you are subscribed to the Google Groups > "scikit-image" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to scikit-image+unsubscribe(a)googlegroups.com. > For more options, visit https://groups.google.com/d/optout. >

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Re: Equivalent of watershed for cutting connected components of an image of particles?
by Claiborne Morton 11 Mar '15

11 Mar '15

Hey guys, Im following up on Adam's behalf, but this is an example of an image we are working with in trying to separate cells that are touching each other. Also you can see the top middle particle has a crescent shape, but is actually a healthy red blood cell that has been segmented incorrectly because of glare. Is that a way to connect the two tips of the shape so that I could then run "binary_fill_holes()" to correctly segment the cell. Thanks! On Wednesday, February 18, 2015 at 7:04:10 PM UTC-5, Adam Hughes wrote: > Hi, > > In ImageJ, one can select watershedding to break up connected regions of > particles. Are there any examples of using watershed in this capacity in > scikit image? All of the examples I see seem to use watershedding to do > segmentation, not to break connected particles in an already-segmented > black and white image. > > Also, is there a straightforward way to remove particles on a the edge of > an image? Sorry, googling is failing me, but I know this is possible. > > Thanks >

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Re: SciPy 2015 Conference: Call for proposals
by Stéfan van der Walt 11 Mar '15

11 Mar '15

On Mon, Mar 9, 2015 at 5:19 PM, Tony Yu <tsyu80(a)gmail.com> wrote: > SciPy 2015 is requesting proposals for tutorials, talks, and posters for > this year's conference. Note that there's a mini-symposium on > "Visualization, Vision and Imaging", so this year will be particularly > relevant for people here. I am interested in presenting a tutorial this year, but will be gone most of June, so I need a partner in crime to share in the fun. Any volunteers? Stéfan

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Re: hough ellipse fit inaccurate?
by Johannes Schoenberger 11 Mar '15

11 Mar '15

@Stefan, good idea! I am curious to know whether this solves your problem. > On Mar 10, 2015, at 2:49 PM, Stéfan van der Walt <stefanv(a)berkeley.edu> wrote: > > It may be a good idea to upsample your image before doing canny, > because edges lie in between pixels, and can only be accurately marked > with enough resolution. > > -- > You received this message because you are subscribed to the Google Groups "scikit-image" group. > To unsubscribe from this group and stop receiving emails from it, send an email to scikit-image+unsubscribe(a)googlegroups.com. > For more options, visit https://groups.google.com/d/optout.

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extract_edges_after_segmentation
by GiorgosR 10 Mar '15

10 Mar '15

<https://lh5.googleusercontent.com/-HJwNDOP7D1U/VP7KITX1mFI/AAAAAAAACXQ/zn1P…> Hi there, First of all, many thanks for this very nice and useful package! I just started using the skimage library and I do some image processing in very noisy angiographic data. After some image enhancement in the obtained images, I applied a canny edge detection algorithm implemented in skimage and I came up with the attached image (after dilation and erosion). Do you have any advice on how could I extract only the vessel edges automatically from the image? I looked in examples with labels or binary hole filling but it does not work well for my problem. Many thanks in advance, Giorgos

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Re: hough ellipse fit inaccurate?
by Johannes Schoenberger 10 Mar '15

10 Mar '15

Well, there is not reason to use `fit ellipse` from OpenCV, you can use `skimage.measure.EllipseModel`. > On Mar 5, 2015, at 5:38 PM, Kevin Keraudren <kevin.keraudren(a)googlemail.com> wrote: > > Hi Arno, > > Looking at the code, I would ask: Did your score improve by setting accuracy=1 ? > > https://github.com/scikit-image/scikit-image/blob/master/skimage/transform/… > > Considering that you are asking for accuracy below half a pixel, I would not be surprised if the voting process of the Hough transform is not that accurate. A least-square fitting (Opencv fitellipse) might be more accurate than a voting process for a perfect ellipse. > > Aren't the eyeball and the pupil both balls? If you slice them in any way, wouldn't you obtain disks? So why detecting elllipses and not circles? Maybe hough_circle will be more accurate. > > Sorry I cannot provide any proof or certitude on how accurate hough_ellipse is. > > Kind regards, > > Kevin > > > -- > You received this message because you are subscribed to the Google Groups "scikit-image" group. > To unsubscribe from this group and stop receiving emails from it, send an email to scikit-image+unsubscribe(a)googlegroups.com. > For more options, visit https://groups.google.com/d/optout.

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SciPy 2015 Conference: Call for proposals
by Tony Yu 10 Mar '15

10 Mar '15

SciPy 2015 is requesting proposals for tutorials, talks, and posters for this year's conference. Note that there's a mini-symposium on "Visualization, Vision and Imaging", so this year will be particularly relevant for people here. Cheers! -Tony ---- *SciPy 2015 Conference (Scientific Computing with Python) Call for Proposals is Open: Submit Your Tutorial and Talk Ideas ASAP <http://scipy2015.scipy.org/>!* SciPy 2015, the fourteenth annual Scientific Computing with Python conference, will be held July 6-12, 2015 in Austin, Texas. SciPy is a community dedicated to the advancement of scientific computing through open source Python software for mathematics, science, and engineering. The annual SciPy Conference brings together over 500 participants from industry, academia, and government to showcase their latest projects, learn from skilled users and developers, and collaborate on code development. The full program will consist of two days of tutorials by followed by three days of presentations, and concludes with two days of developer sprints. More info available on the conference website:http://www.scipy2015.scipy.org <http://www.scipy2015.scipy.org/ehome/115969/259266/?&>; you can also sign up for mailing list updates <http://scipy2015.scipy.org/>. Registration is expected to open at the end of March. We encourage you to submit tutorial or talk proposals in the categories below <http://scipy2015.scipy.org/>; please also share with others who you’d like to see participate! *SCIPY TUTORIAL SESSION PROPOSALS – requested by March 16, 2015* The SciPy experience kicks off with two days of tutorials. These sessions provide extremely affordable access to expert training, and consistently receive fantastic feedback from participants. We're looking for submissions on topics from introductory to advanced - we'll have attendees across the gamut looking to learn. Whether you are a major contributor to a scientific Python library or an expert-level user, this is a great opportunity to share your knowledge and stipends are available. *Submit Your Tutorial Proposal <http://www.scipy2015.scipy.org/eselectv2/frontend/index/115969>*on the SciPy 2015 website:http://scipy2015.scipy.org *SCIPY TALK AND POSTER SUBMISSIONS – requested by April 1, 2015* SciPy 2015 will include 3 major topic tracks and 7 mini-symposia tracks. *Submit Your Talk Proposal <http://submit%20your%20talk%20proposal%20/>*on the SciPy 2015 website: http://scipy2015.scipy.org Major topic tracks include: · Scientific Computing in Python (General track) · Python in Data Science · and Quantitative and Computational Social Sciences. Mini-symposia will include the applications of Python in: · Astronomy and astrophysics · Computational life and medical sciences · Engineering · Geographic information systems (GIS) · Geophysics · Oceanography and meteorology · Visualization, vision and imaging

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Extracting/Isolating Segmented Objects in scikit-image
by Sumesh K.C. 09 Mar '15

09 Mar '15

Hello Everyone, I've just started working with scikit-image python package for image processing and segmentation. Its cool to learn. I'm getting problem in extracting or simply isolating the segmented objects after running segmentation like quickshift, watershed, slic. How can i do this? Need your help! Thank you, in advance!

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Re: Extracting/Isolating Segmented Objects in scikit-image
by Emmanuelle Gouillart 09 Mar '15

09 Mar '15

Hi Sumesh, also see http://scikit-image.org/docs/dev/auto_examples/plot_label.html#example-plot… with the use of "label". Cheers, Emma On Sun, Mar 08, 2015 at 04:52:32PM -0700, StÃ©fan van der Walt wrote: > Hi Sumesh > On Sun, Mar 8, 2015 at 8:17 AM, Sumesh K.C. <rockingsumes(a)gmail.com> wrote: > > I'm getting problem in extracting or simply isolating the segmented objects after running segmentation like quickshift, watershed, slic. > Please have a look at the tutorial at > https://github.com/scikit-image/skimage-tutorials > Regards > StÃ©fan

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