Beata Wereszczyńska

Graduate k-space masking for MRI image denoising and blurring (on the example of Agilent FID data). (Python 3)

Calculating theoretical MRI images with both TI (T1-weighting) and TE (T2-weighting) of choice, from separate T1-weighted and T2-weighted sets of images. (Python 3)

Simulating T1-weighted saturation recovery MRI images for arbitrary values of TR from a set of T1-weighted inversion recovery MRI images. (Python 3)

Simulating T1- and T2-weighted MRI images with arbitrary values of TI or TE, respectively. (Python 3)

UML dimensionality reduction and clustering models for predicting if a banknote is genuine or not based on the dataset from OpenML containing wavelet analysis results for genuine and forged banknotes - practical exercise. (Python 3)

Comparison of numerous supervised machine learning classifier models (Logistic Regression, K-Nearest Neighbors, Support Vector Machines and Decision Trees) predicting if a banknote is genuine or not based on the dataset from OpenML containing wavelet analysis results for genuine and forged banknotes. (Python 3)

Tool for calculating swelling tablet eroding front's diffusion rate D and the rate of the swelling k from time series of either T2-maps or MRI images in FDF or Text Image format. (Python 3)

k-space based details/edges detection in MRI images with optional k-space based denoising and detail control (on the example of Agilent FID data). (Python 3)

k-space weighting and masking for denoising of MRI image without blurring or losing contrast, as well as for brightening of the objects in the image with simultaneous noise reduction (on the example of Agilent FID data). (Python 3)

Saving random unique square fragments of an image (located inside or outside defined bounding box) as jpg. (Python 3)

This is repository created in the frame of IBM's online training 'Applied Data Science Capstone' available on Coursera.org

flask_enf_fr for IBM course "Python Project for AI & Application Development" on coursera.org