jpeoples

import fitz  # PyMuPDF
import re
import sys
import argparse
import os

def chapter_titles_and_page_ranges(pdf_document: fitz.Document):
    # Get the outline information (top-level bookmarks)
    toc = pdf_document.get_toc(simple=False)
    labels = pdf_document.get_page_labels()

jpeoples

import SimpleITK as sitk
import numpy

def load_ct(p):
    """Load CT from path p"""
    return sitk.ReadImage(p)
  
def load_segment(p, ct, bgval=0):
    """Load mask from path p and ensure same size of underlying array as CT image ct"""
    m = sitk.ReadImage(p) != bgval

jpeoples

Using the same scan object there are two methods: dicom_dump and read_dicom.

If you do

scan.read_dicom()

you get a pydicom object with all the headers (and there is an argument for whether to include pixel data or not) https://xnat.readthedocs.io/en/latest/xnat.html#xnat.mixin.ImageScanData.read_dicom

jpeoples

import SimpleITK as sitk

def wl_to_range(wl):
  """Convert window, level, to range"""
  w, l = wl
  lower, upper = l-w/2, l+w/2
  return lower, upper

def remap_range(img, range):
  """Remap image intensities so that the range is transformed to [0,1]"""

jpeoples

The SimpleITK library has an easy to use class for computing connected components in a segmentation mask: sitk.ConnectedComponentImageFilter.

Here is a sample usage (here the “mask” argument would be a binary mask (0 = background, 1 = foreground)

def get_components(mask):
    f = sitk.ConnectedComponentImageFilter()
 labeled = f.Execute(mask)

jpeoples

function [q1,q2,q3,w0,w1,outliers] = boxplot_statistics(data, whisker)
    if ~exist('whisker', 'var')
        % whisker is 1.5 by default
        whisker = 1.5;
    end

    % quantile(data,3) will return the 25th, 50th, and 75th percentile
    % for each column
    quants = quantile(data, 3);
    q1 = quants(1,:);

jpeoples

#include <stdio.h>
#include <stdlib.h>
#include <string.h>


enum error_codes {
    SUCCESS = 0,
    ALLOCATION_ERROR,
    INVALID_DATA_POINTER_ERROR,
    STREAM_OUT_ERROR,