home/…/Tube-Segmentation-Framework/parallelCenterlineExtraction.cpp:727:46: note: candidates are: home/…/Tube-Segmentation-Framework/parallelCenterlineExtraction.cpp:727:46: error: no matching function for call to ‘oul::HistogramPyramid3DBuffer::HistogramPyramid3DBuffer(OpenCL&)’
#Boinc no usable gpus found code
I’m trying to compile the Tube-Segmentation-Framework code and I’m getting some errors like these below: NewLabeledImage = bwlabel(keeperBlobsImage,4) % Label each blob so we can make measurements of it % % Re-label with only the keeper blobs kept. KeeperBlobsImage = ismember(labeledImage, keeperIndexes) KeeperIndexes = find(allowableAreaIndexes) Text(thisCentroid(1), thisCentroid(2), message, ‘Color’, ‘r’) ĪllowableAreaIndexes = (allAreas>300) & ( allAreas <1300 ) Message = sprintf(‘Area = %d’, allAreas(k)) MenuOptions = ‘0’ % Add option to extract no blobs.įor k = 1 : numberOfBlobs % Loop through all blobs. = bwlabel(binaryImage) īlobMeasurements = regionprops(labeledImage, ‘area’, ‘Centroid’) ĪllAreas = % No semicolon so it will print to the command window. Can you suggest that what should i do for automatically detecting largest blobs and can show my desires coronary arteries.īinaryImage=im2bw(originalImage,graythresh(originalImage)) īinaryImage = imfill(binaryImage, ‘holes’) But i have to hard code in this code for each image. But for removing arota i apply code for detecting largest Blob area. I have segmented coronary arteries after using Fuzzy C-means clustering Algorithm in Matlab. I am doing my PhD in computer Sciences on bio medical imaging. FAST (Framework for heterogeneous medical image computing and visualization).Ubuntu Linux as my main operating system.If you are interested in the same topics, please don’t hesitate to contact me at GitHub Twitter Linkedin ResearchGate Tools I use Segmentation and centerline extraction of tubular structures, such as airways and blood vessels.Deep neural networks for medical image segmentation, object detection and classification.Segmentation and tracking of structures in ultrasound images.Currently, I am working as a research scientist at SINTEF Medical Technology and as a post doc at the Norwegian University of Science and Technology (NTNU). My name is Erik Smistad and my research is focused on programs and algorithms that can automatically and quickly locate organs and other anatomical structures in medical images (CT, MR, Ultrasound etc.) for the purpose of helping physicians interpret the images and navigate inside the body.
0 kommentar(er)
