讲解：algorithm、Java、Python/C++、ret

Coursework 2018-2019Task Description and GuidelinesSegment the retina blood vessel. (16 marks)Retina blood vessel segmentation is challenging. Given the image on the left, it is very difficultto accurately segment the blood vessel as shown on the right, especially the thin ones. You arefree to design your own algorithm to segment the blood vessel, down to per pixel level. NOTE:You are not required to achieve the state-of-the-art results, but you should achievesatisfactory results.Requirements & Guidelines 40 retina images are provided, together with the label images indicating where the bloodvessel is, and the mask image masking out the background of retina image. You should test your algorithm on retina_images/1.tif only, and show the segmentationresults for this image. You should never use label_images/1.tif when you design youralgorithm. All other images being provided can be used as training images when you designyour algorithm. When test your algorithm, you should compare the segmentation results of your algorithmwith the ground-truth image (label_images/1.tif). You should report the following:The percentage of blood vessel pixels that is being correctly classified as blood vessel.Denoted as P. The percentage of background pixels (only consider the region in the mask.) that is beingcorrectly classified as background. Denoted as N. The percentages of pixels are being correctly classified. (only consider the region in themask). Denoted as T. For example, assume in the mask we have 100 pixels, 10 pixels are retina and 90 pixelsare background. The segmentation result is that 12 pixels are retina (only 8 are true retinaand 4 are false retina) and 88 pixels are background. (only 86 are true代写algorithm留学生作业、Java编程设计作业代做、代写Python/C++实验作业、代做retina imag background and 2are false background.) Then P = 8/10 = 80%; N = 86/90 = 95.56%; T = (8+86)/100=94%. You may consider the following filters: (It is up to you to choose which one to use and howmany you may use. You may use other filters if appropriate. You may use FILTERS frominternet resources, with proper acknowledgement. Laplacian of Gaussian filter. Difference of Gaussian filter. Canny filter. Match filter. Gabor filters. You may directly process the filtered images to obtain the per-pixel classification results ofthe retina image. Or you may try to combine the results from several filters, e.g. to treat thefiltered output of other images as the training samples, and train a per-pixel classifier toclassify the test image retina_images/1.tif. You should implement one main function that calls other functions to generate all the results,e.g. task3main().Marking CriteriaYou should include the explanation of your algorithm, the intermediate results and the finalresults in your report. Marks will be given by considering: The quality of your per-pixel level segmentation results for the first image. You should try tomaximize both P and N. (6 marks) The explanation and justification of your algorithm. (6 marks) Discuss advantages and disadvantages of your algorithm. (2 marks) Whether your codes are well documented, including comments, and explanation of variablesat the beginning of each function. (2 marks)ReportAll images on the report should be large enough for visual inspection of the image quality.How to submitYou should zip all the matlab files, the result images (only upload the segmentation result ofthe test image) and the PDF file for the report in ONE zipped file. 转自：http://www.7daixie.com/2019042913633651.html