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Cross sectional images of plant's root has been supplied into this algorithm. A red dye has been applied onto the cell walls of these roots, whereas a green dye has been applied onto its cell walls. The motive of this algorithm is to have a piece of automated Matlab code that has the capability to implement image segmentation onto these images a…

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liewyihseng/Introduction-to-Image-Processing

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Introduction-to-Image-Processing

Name : Liew Yih Seng

Student ID : 20090325

Module : COMP2032 - Introduction to Image Processing

Module Convenor : Dr. Amr Ahmed

To run the Matlab Code:

  1. Unzip and extract the folder into your desired location.

  2. Make sure the directory in Matlab has been path into COMP2032-LIEW-20090325 folder.

  3. There are two ways to run the code: a) You can call the function extract_analyse_nucleus_hsv() with image name along with its file extension being the input argument in the Command Window. For instance:

     	[im_output, numNucleus] = extract_analyse_nucleus_hsv("StackNinja1.bmp");
    

    where it will generate two variables in the workspace, im_output representing the Final Binary Image Marking Regions Corresponding to Nuclei and numNucleus representing the total count of nuclei detected within the input image.

    b) Or you can run the script file I have prepared for you, run_extract_analyse_nucleus_hsv.m for your convenience, where it contains extract_analyse_nucleus_hsv function calls for all three images provided. So you only have to comment the lines that you do not want to run. All the outputs of this script file will be stored in the workspace with proper naming. For instance:

     	[im_output1, number_of_nucleus_hsv_1] = extract_analyse_nucleus_hsv("StackNinja1.bmp");
    

    The line above is supplied with input argument of image "StackNinja1.bmp", hence resulting in variables being tagged with "1" behind every output of this function call within the workspace. This concept applies to other images and their outputs also.

  4. Both the methods stated above will return a total of two outputs per function call. To view the output of binary image after the extraction process, you can use imshow() to have them displayed. Whereas, the other output in the form of scalar value represents the total number of nucleus detected by the algorithm that I have created. However, I have shown all the step-by-step figures that leads the algorithm to the solution once the function has been executed. So there might be chances to not needing to once again use imshow to display the output image.

Files included in the zip folder:

  1. COMP2032-LIEW-20090325-REPORT.pdf
  2. COMP2032-LIEW-20090325-VIDEO.mp4
  3. extract_analyse_nucleus_hsv.m
  4. run_extract_analyse_nucleus_hsv.m
  5. README.txt
  6. StackNinja1.bmp
  7. StackNinja2.bmp
  8. StackNinja3.bmp

Files that serves as input argument in function extract_analyse_nucleus_hsv():

  1. "StackNinja1.bmp"
  2. "StackNinja2.bmp"
  3. "StackNinja3.bmp"

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Cross sectional images of plant's root has been supplied into this algorithm. A red dye has been applied onto the cell walls of these roots, whereas a green dye has been applied onto its cell walls. The motive of this algorithm is to have a piece of automated Matlab code that has the capability to implement image segmentation onto these images a…

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