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EGhamgui/Segmentation-of-Skin-Lesions
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1. The project directory contains 7 files .py representing the different functions used to implement the segmentation algorithm: Canal.py : Color space transformation Pillbox_Filter.py : Noise filtering function Intensity_Adjustement.py : Histogram stretching function Histogram_Thresholding.py : Otsu's thresholding CCL_RLE.py / CCL.py : two methods for Connected Component Labeling (CCL) Morphological.py : Filling function 2. All these functions are combined in Main.py for the test step on all provided images. The main.py uses also Filelist.py to read all data. 3. In order to evaluate the segmentation algorithm, we used multiple performance indexes defined in the file Metrics.py. 4. The notebook Projet IMA201.ipynb shows all the results.
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Detecting cancerous lesions by implementing a segmentation method based on histogram thresholding and color space optimization.
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