Multi-nucleated Muscle Cell Image Analysis
This project is using bio-medical image anaylsis techniques to help determine features of multi-nucleated muscle cells. All images were provided in the /Cell_images folder from Prof. Eric Folker's Lab at Boston College. There are two main parts of this project with functions included:
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Density of Microtubules at ranging radii from nuclei in image
- Python function which will determine the density of microtubules at various radii away from nucleus center
- Code Contribution(Alec Lobanov)
Packages Required: Numpy, Imageio, cv2, skimage
density_of_nuclei(nuclei_image,microtubule_image,list_of_radii,pixel_threshold)
- nuclei_image: has to be upload as .png and read into function
- Microtubule_image: has to be upload as .png and read into function
- list_of_radii: radii in list for for which density of microtubules is calculated ex. [10 20 30 40 50]
- pixel_threshold: integer pixel lower-bound threshold value for what is considered a microtubule
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Deep-Learning Model to remove noise from input images.
- Current updated model that is train on image data from /Cell_images folder to recognize noise in image which can be removed in later stage
- Code Contribution(Alec Lobanov)
Packages Required: Numpy, Torch, cv2, skimage, Matplotlib, Imageio, Sklearn, Scipy