jcreinhold

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
rpca_gpu

implementations of RPCA on the GPU (leveraging pytorch) 
for low-rank and sparse matrix decomposition as well as 
a nuclear-norm minimization routine via singular value 
thresholding for matrix completion

jcreinhold

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
nii_to_tif

command line executable to convert 3d nifti images to 
individual tiff images along a user-specified axis

call as: python nii_to_tif.py /path/to/nifti /path/to/tif
(append optional arguments to the call as desired)

jcreinhold

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
tif_to_nii

command line executable to convert a directory of tif images
(from one image) to a nifti image stacked along a user-specified axis

call as: python tif_to_nii.py /path/to/tif/ /path/to/nifti
(append optional arguments to the call as desired)

jcreinhold

#/usr/bin/env python
# -*- coding: utf-8 -*-
"""
nii_to_tif

convert all mha files in a directory to nifti

Author: Jacob Reinhold (jacob.reinhold@jhu.edu)
"""

jcreinhold

jcreinhold

---
title: "On the distribution of p-values"
output: html_notebook
---

```{r}
sample.sizes <- seq(5, 100, 10)
num.trials <- 1000
num.experiments <- 1
null.mu <- 0.0

jcreinhold

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Normalize the intensity of an image by 
finding a tissue mean in the foreground and 
voxel-wise dividing the image by that value
Author: Jacob Reinhold
"""

import sys
from argparse import ArgumentParser

jcreinhold

let swap arr i j =
  let tmp = arr.(j) in
  arr.(j) <- arr.(i);
  arr.(i) <- tmp

let partition arr l h =
  let pivot = arr.(h) in
  let i = ref @@ (l - 1) in
  for j = l to h - 1 do
    if arr.(j) <= pivot then begin

jcreinhold

let swap arr i j =
  let tmp = arr.(j) in
  arr.(j) <- arr.(i);
  arr.(i) <- tmp

let partition arr l h =
  let pivot = arr.(h) in
  let i = ref @@ (l - 1) in
  for j = l to h - 1 do
    if arr.(j) <= pivot then begin

jcreinhold

# pymedio is used here because it opens a wide variety of
# medical image formats but you can use 'nibabel', 'pydicom',
# or 'SimpleITK' and extract the pixel data as an array and
# use the same functions to test equality
from pymedio.image import Image

# 'query_image' and 'target_image' can be most common medical
# image formats, e.g., NIfTI, directory of DICOM frames, etc.
query = Image.from_path("path/to/query_image")
target = Image.from_path("path/to/target_image")