05c_feature_extraction/statistics_with_scikit_image.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2e3ef9ca-39d2-4c2b-b742-6d287698ccb9",
   "metadata": {},
   "source": [
    "# Statistics using Scikit-image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e582f6-d043-46f9-84c7-043817d71c58",
   "metadata": {},
   "source": [
    "We can use scikit-image for extracting features from label images. For convenience reasons we use the [napari-skimage-regionprops](https://github.com/haesleinhuepf/napari-skimage-regionprops) library. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c6ba347-663f-42f8-b42a-e7b03a4e5de6",
   "metadata": {},
   "source": [
    "Before we can do measurements, we need an `image` and a corresponding `label_image`. Therefore, we recapitulate filtering, thresholding and labeling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c73c156-0d06-4321-98d6-e1c49fafb596",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from skimage.io import imread\n",
    "from skimage import filters\n",
    "from skimage import measure\n",
    "from napari_skimage_regionprops import regionprops_table\n",
    "from pyclesperanto_prototype import imshow\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import pyclesperanto_prototype as cle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ccede3f1-4f7e-4b41-b22e-1185b6633c47",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load image\n",
    "image = imread(\"data/blobs.tif\")\n",
    "\n",
    "imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2de0ee95-1bc5-46fb-9e66-8b59efa7549e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# denoising\n",
    "blurred_image = filters.gaussian(image, sigma=1)\n",
    "\n",
    "# binarization\n",
    "threshold = filters.threshold_otsu(blurred_image)\n",
    "thresholded_image = blurred_image >= threshold\n",
    "\n",
    "# labeling\n",
    "label_image = measure.label(thresholded_image)\n",
    "\n",
    "# visualization\n",
    "imshow(label_image, labels=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6854864d-4a5b-4360-ac2a-6372c4a3e985",
   "metadata": {},
   "source": [
    "## Measurements / region properties"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a635e37-dc47-4db8-bced-1548818da3f6",
   "metadata": {},
   "source": [
    "We are now using the very handy function `regionprops_table`. It provides features based on the scikit-image [regionprops list of measurements](https://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.regionprops) library. Let us check first what we need to provide for this function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "60132254-3109-4d4c-9b40-fa4548ee9b8f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mSignature:\u001b[0m\n",
       "\u001b[0mregionprops_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mimage\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'napari.types.ImageData'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mlabels\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'napari.types.LabelsData'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0msize\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mintensity\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mperimeter\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mshape\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mposition\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mmoments\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mnapari_viewer\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'napari.Viewer'\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;34m'pandas.DataFrame'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mDocstring:\u001b[0m Adds a table widget to a given napari viewer with quantitative analysis results derived from an image-label pair.\n",
       "\u001b[1;31mFile:\u001b[0m      c:\\users\\haase\\mambaforge\\envs\\tea2024\\lib\\site-packages\\napari_skimage_regionprops\\_regionprops.py\n",
       "\u001b[1;31mType:\u001b[0m      function"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "regionprops_table?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8d70c8cf-a622-4c78-ac13-85851dead81b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>area</th>\n",
       "      <th>bbox_area</th>\n",
       "      <th>equivalent_diameter</th>\n",
       "      <th>convex_area</th>\n",
       "      <th>max_intensity</th>\n",
       "      <th>mean_intensity</th>\n",
       "      <th>min_intensity</th>\n",
       "      <th>perimeter</th>\n",
       "      <th>perimeter_crofton</th>\n",
       "      <th>...</th>\n",
       "      <th>moments_hu-1</th>\n",
       "      <th>moments_hu-2</th>\n",
       "      <th>moments_hu-3</th>\n",
       "      <th>moments_hu-4</th>\n",
       "      <th>moments_hu-5</th>\n",
       "      <th>moments_hu-6</th>\n",
       "      <th>standard_deviation_intensity</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>roundness</th>\n",
       "      <th>circularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1</td>\n",
       "      <td>429.0</td>\n",
       "      <td>750.0</td>\n",
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       "      <td>479.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>191.440559</td>\n",
       "      <td>128.0</td>\n",
       "      <td>89.012193</td>\n",
       "      <td>87.070368</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018445</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>1.133411e-04</td>\n",
       "      <td>1.205336e-08</td>\n",
       "      <td>-2.714451e-06</td>\n",
       "      <td>3.298765e-08</td>\n",
       "      <td>29.793138</td>\n",
       "      <td>2.088249</td>\n",
       "      <td>0.451572</td>\n",
       "      <td>0.680406</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>183.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>15.264430</td>\n",
       "      <td>190.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>179.846995</td>\n",
       "      <td>128.0</td>\n",
       "      <td>53.556349</td>\n",
       "      <td>53.456120</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010549</td>\n",
       "      <td>0.001177</td>\n",
       "      <td>6.734903e-05</td>\n",
       "      <td>-1.483503e-08</td>\n",
       "      <td>-5.401836e-06</td>\n",
       "      <td>-1.180484e-08</td>\n",
       "      <td>21.270534</td>\n",
       "      <td>1.782168</td>\n",
       "      <td>0.530849</td>\n",
       "      <td>0.801750</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>658.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>28.944630</td>\n",
       "      <td>673.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>205.604863</td>\n",
       "      <td>120.0</td>\n",
       "      <td>95.698485</td>\n",
       "      <td>93.409370</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>1.146642e-06</td>\n",
       "      <td>4.801450e-13</td>\n",
       "      <td>9.889775e-09</td>\n",
       "      <td>-1.248247e-11</td>\n",
       "      <td>29.392255</td>\n",
       "      <td>1.067734</td>\n",
       "      <td>0.918683</td>\n",
       "      <td>0.902871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>433.0</td>\n",
       "      <td>529.0</td>\n",
       "      <td>23.480049</td>\n",
       "      <td>445.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>217.515012</td>\n",
       "      <td>120.0</td>\n",
       "      <td>77.455844</td>\n",
       "      <td>76.114262</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>3.349309e-07</td>\n",
       "      <td>-3.612652e-12</td>\n",
       "      <td>2.526209e-09</td>\n",
       "      <td>-9.198337e-14</td>\n",
       "      <td>35.852345</td>\n",
       "      <td>1.061942</td>\n",
       "      <td>0.917813</td>\n",
       "      <td>0.906963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>472.0</td>\n",
       "      <td>551.0</td>\n",
       "      <td>24.514670</td>\n",
       "      <td>486.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>213.033898</td>\n",
       "      <td>128.0</td>\n",
       "      <td>83.798990</td>\n",
       "      <td>82.127941</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005876</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>4.356690e-06</td>\n",
       "      <td>-6.223212e-11</td>\n",
       "      <td>-3.178823e-07</td>\n",
       "      <td>4.522442e-11</td>\n",
       "      <td>28.741080</td>\n",
       "      <td>1.579415</td>\n",
       "      <td>0.621952</td>\n",
       "      <td>0.844645</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>213.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>16.468152</td>\n",
       "      <td>221.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>184.525822</td>\n",
       "      <td>120.0</td>\n",
       "      <td>52.284271</td>\n",
       "      <td>52.250114</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001745</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>1.126452e-07</td>\n",
       "      <td>5.814575e-14</td>\n",
       "      <td>4.625041e-09</td>\n",
       "      <td>-6.410600e-15</td>\n",
       "      <td>28.255467</td>\n",
       "      <td>1.296143</td>\n",
       "      <td>0.771094</td>\n",
       "      <td>0.979146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>79.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>10.029253</td>\n",
       "      <td>84.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>184.810127</td>\n",
       "      <td>128.0</td>\n",
       "      <td>39.313708</td>\n",
       "      <td>39.953250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.056792</td>\n",
       "      <td>0.001777</td>\n",
       "      <td>1.725200e-04</td>\n",
       "      <td>-5.364882e-08</td>\n",
       "      <td>-2.906455e-05</td>\n",
       "      <td>-7.903632e-08</td>\n",
       "      <td>33.739912</td>\n",
       "      <td>3.173540</td>\n",
       "      <td>0.300766</td>\n",
       "      <td>0.642316</td>\n",
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       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>88.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>10.585135</td>\n",
       "      <td>92.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>182.727273</td>\n",
       "      <td>128.0</td>\n",
       "      <td>45.692388</td>\n",
       "      <td>46.196967</td>\n",
       "      <td>...</td>\n",
       "      <td>0.097966</td>\n",
       "      <td>0.002268</td>\n",
       "      <td>2.994111e-04</td>\n",
       "      <td>-1.724577e-08</td>\n",
       "      <td>-3.731912e-05</td>\n",
       "      <td>-2.461160e-07</td>\n",
       "      <td>24.417173</td>\n",
       "      <td>4.021193</td>\n",
       "      <td>0.238521</td>\n",
       "      <td>0.529669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61</td>\n",
       "      <td>52.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>8.136858</td>\n",
       "      <td>56.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>189.538462</td>\n",
       "      <td>128.0</td>\n",
       "      <td>30.692388</td>\n",
       "      <td>32.924135</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046813</td>\n",
       "      <td>0.003694</td>\n",
       "      <td>3.041106e-04</td>\n",
       "      <td>-2.539652e-07</td>\n",
       "      <td>-5.698262e-05</td>\n",
       "      <td>-1.984555e-07</td>\n",
       "      <td>37.867411</td>\n",
       "      <td>2.839825</td>\n",
       "      <td>0.322190</td>\n",
       "      <td>0.693668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62</td>\n",
       "      <td>48.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>7.817640</td>\n",
       "      <td>53.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>173.833333</td>\n",
       "      <td>128.0</td>\n",
       "      <td>33.071068</td>\n",
       "      <td>35.375614</td>\n",
       "      <td>...</td>\n",
       "      <td>0.125246</td>\n",
       "      <td>0.004295</td>\n",
       "      <td>1.300612e-03</td>\n",
       "      <td>2.775191e-06</td>\n",
       "      <td>3.027878e-04</td>\n",
       "      <td>1.321910e-06</td>\n",
       "      <td>27.987596</td>\n",
       "      <td>4.417297</td>\n",
       "      <td>0.213334</td>\n",
       "      <td>0.551512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 86 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    label   area  bbox_area  equivalent_diameter  convex_area  max_intensity  \\\n",
       "0       1  429.0      750.0            23.371345        479.0          232.0   \n",
       "1       2  183.0      231.0            15.264430        190.0          224.0   \n",
       "2       3  658.0      756.0            28.944630        673.0          248.0   \n",
       "3       4  433.0      529.0            23.480049        445.0          248.0   \n",
       "4       5  472.0      551.0            24.514670        486.0          248.0   \n",
       "..    ...    ...        ...                  ...          ...            ...   \n",
       "57     58  213.0      285.0            16.468152        221.0          224.0   \n",
       "58     59   79.0      108.0            10.029253         84.0          248.0   \n",
       "59     60   88.0      110.0            10.585135         92.0          216.0   \n",
       "60     61   52.0       75.0             8.136858         56.0          248.0   \n",
       "61     62   48.0       68.0             7.817640         53.0          224.0   \n",
       "\n",
       "    mean_intensity  min_intensity  perimeter  perimeter_crofton  ...  \\\n",
       "0       191.440559          128.0  89.012193          87.070368  ...   \n",
       "1       179.846995          128.0  53.556349          53.456120  ...   \n",
       "2       205.604863          120.0  95.698485          93.409370  ...   \n",
       "3       217.515012          120.0  77.455844          76.114262  ...   \n",
       "4       213.033898          128.0  83.798990          82.127941  ...   \n",
       "..             ...            ...        ...                ...  ...   \n",
       "57      184.525822          120.0  52.284271          52.250114  ...   \n",
       "58      184.810127          128.0  39.313708          39.953250  ...   \n",
       "59      182.727273          128.0  45.692388          46.196967  ...   \n",
       "60      189.538462          128.0  30.692388          32.924135  ...   \n",
       "61      173.833333          128.0  33.071068          35.375614  ...   \n",
       "\n",
       "    moments_hu-1  moments_hu-2  moments_hu-3  moments_hu-4  moments_hu-5  \\\n",
       "0       0.018445      0.000847  1.133411e-04  1.205336e-08 -2.714451e-06   \n",
       "1       0.010549      0.001177  6.734903e-05 -1.483503e-08 -5.401836e-06   \n",
       "2       0.000113      0.000104  1.146642e-06  4.801450e-13  9.889775e-09   \n",
       "3       0.000096      0.000348  3.349309e-07 -3.612652e-12  2.526209e-09   \n",
       "4       0.005876      0.000072  4.356690e-06 -6.223212e-11 -3.178823e-07   \n",
       "..           ...           ...           ...           ...           ...   \n",
       "57      0.001745      0.000002  1.126452e-07  5.814575e-14  4.625041e-09   \n",
       "58      0.056792      0.001777  1.725200e-04 -5.364882e-08 -2.906455e-05   \n",
       "59      0.097966      0.002268  2.994111e-04 -1.724577e-08 -3.731912e-05   \n",
       "60      0.046813      0.003694  3.041106e-04 -2.539652e-07 -5.698262e-05   \n",
       "61      0.125246      0.004295  1.300612e-03  2.775191e-06  3.027878e-04   \n",
       "\n",
       "    moments_hu-6  standard_deviation_intensity  aspect_ratio  roundness  \\\n",
       "0   3.298765e-08                     29.793138      2.088249   0.451572   \n",
       "1  -1.180484e-08                     21.270534      1.782168   0.530849   \n",
       "2  -1.248247e-11                     29.392255      1.067734   0.918683   \n",
       "3  -9.198337e-14                     35.852345      1.061942   0.917813   \n",
       "4   4.522442e-11                     28.741080      1.579415   0.621952   \n",
       "..           ...                           ...           ...        ...   \n",
       "57 -6.410600e-15                     28.255467      1.296143   0.771094   \n",
       "58 -7.903632e-08                     33.739912      3.173540   0.300766   \n",
       "59 -2.461160e-07                     24.417173      4.021193   0.238521   \n",
       "60 -1.984555e-07                     37.867411      2.839825   0.322190   \n",
       "61  1.321910e-06                     27.987596      4.417297   0.213334   \n",
       "\n",
       "    circularity  \n",
       "0      0.680406  \n",
       "1      0.801750  \n",
       "2      0.902871  \n",
       "3      0.906963  \n",
       "4      0.844645  \n",
       "..          ...  \n",
       "57     0.979146  \n",
       "58     0.642316  \n",
       "59     0.529669  \n",
       "60     0.693668  \n",
       "61     0.551512  \n",
       "\n",
       "[62 rows x 86 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(regionprops_table(image , label_image, \n",
    "                                           perimeter = True, \n",
    "                                           shape = True, \n",
    "                                           position=True,\n",
    "                                           moments=True))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd93772c-adec-4492-a267-95005e32ca29",
   "metadata": {},
   "source": [
    "As you can see, we have now plenty of features to investigate. We can print out all feature names with the `keys` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "13f4b644-0e01-4f4e-8601-42e5ee5a07d9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['label', 'area', 'bbox_area', 'equivalent_diameter', 'convex_area',\n",
      "       'max_intensity', 'mean_intensity', 'min_intensity', 'perimeter',\n",
      "       'perimeter_crofton', 'extent', 'local_centroid-0', 'local_centroid-1',\n",
      "       'solidity', 'feret_diameter_max', 'major_axis_length',\n",
      "       'minor_axis_length', 'orientation', 'eccentricity', 'centroid-0',\n",
      "       'centroid-1', 'bbox-0', 'bbox-1', 'bbox-2', 'bbox-3',\n",
      "       'weighted_centroid-0', 'weighted_centroid-1', 'moments-0-0',\n",
      "       'moments-0-1', 'moments-0-2', 'moments-0-3', 'moments-1-0',\n",
      "       'moments-1-1', 'moments-1-2', 'moments-1-3', 'moments-2-0',\n",
      "       'moments-2-1', 'moments-2-2', 'moments-2-3', 'moments-3-0',\n",
      "       'moments-3-1', 'moments-3-2', 'moments-3-3', 'moments_normalized-0-0',\n",
      "       'moments_normalized-0-1', 'moments_normalized-0-2',\n",
      "       'moments_normalized-0-3', 'moments_normalized-1-0',\n",
      "       'moments_normalized-1-1', 'moments_normalized-1-2',\n",
      "       'moments_normalized-1-3', 'moments_normalized-2-0',\n",
      "       'moments_normalized-2-1', 'moments_normalized-2-2',\n",
      "       'moments_normalized-2-3', 'moments_normalized-3-0',\n",
      "       'moments_normalized-3-1', 'moments_normalized-3-2',\n",
      "       'moments_normalized-3-3', 'moments_central-0-0', 'moments_central-0-1',\n",
      "       'moments_central-0-2', 'moments_central-0-3', 'moments_central-1-0',\n",
      "       'moments_central-1-1', 'moments_central-1-2', 'moments_central-1-3',\n",
      "       'moments_central-2-0', 'moments_central-2-1', 'moments_central-2-2',\n",
      "       'moments_central-2-3', 'moments_central-3-0', 'moments_central-3-1',\n",
      "       'moments_central-3-2', 'moments_central-3-3', 'moments_hu-0',\n",
      "       'moments_hu-1', 'moments_hu-2', 'moments_hu-3', 'moments_hu-4',\n",
      "       'moments_hu-5', 'moments_hu-6', 'standard_deviation_intensity',\n",
      "       'aspect_ratio', 'roundness', 'circularity'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1eedb4d-c27d-41ae-bc73-ef5273d48caf",
   "metadata": {},
   "source": [
    "And `describe` gives us basic statistics like `max`, `mean`, `min` and `std` of each feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "98aeb67d-206d-4749-85e7-e6038fa5ac79",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>area</th>\n",
       "      <th>bbox_area</th>\n",
       "      <th>equivalent_diameter</th>\n",
       "      <th>convex_area</th>\n",
       "      <th>max_intensity</th>\n",
       "      <th>mean_intensity</th>\n",
       "      <th>min_intensity</th>\n",
       "      <th>perimeter</th>\n",
       "      <th>perimeter_crofton</th>\n",
       "      <th>...</th>\n",
       "      <th>moments_hu-1</th>\n",
       "      <th>moments_hu-2</th>\n",
       "      <th>moments_hu-3</th>\n",
       "      <th>moments_hu-4</th>\n",
       "      <th>moments_hu-5</th>\n",
       "      <th>moments_hu-6</th>\n",
       "      <th>standard_deviation_intensity</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>roundness</th>\n",
       "      <th>circularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>6.200000e+01</td>\n",
       "      <td>6.200000e+01</td>\n",
       "      <td>6.200000e+01</td>\n",
       "      <td>6.200000e+01</td>\n",
       "      <td>6.200000e+01</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>31.500000</td>\n",
       "      <td>355.370968</td>\n",
       "      <td>475.677419</td>\n",
       "      <td>20.074583</td>\n",
       "      <td>372.790323</td>\n",
       "      <td>233.548387</td>\n",
       "      <td>190.429888</td>\n",
       "      <td>125.161290</td>\n",
       "      <td>67.787235</td>\n",
       "      <td>67.071263</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012854</td>\n",
       "      <td>5.166382e-04</td>\n",
       "      <td>7.053581e-05</td>\n",
       "      <td>5.855846e-08</td>\n",
       "      <td>3.633047e-06</td>\n",
       "      <td>-8.703715e-09</td>\n",
       "      <td>28.689171</td>\n",
       "      <td>1.637991</td>\n",
       "      <td>0.692418</td>\n",
       "      <td>0.894101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18.041619</td>\n",
       "      <td>211.367385</td>\n",
       "      <td>300.328169</td>\n",
       "      <td>7.091876</td>\n",
       "      <td>223.801078</td>\n",
       "      <td>19.371838</td>\n",
       "      <td>15.382559</td>\n",
       "      <td>4.602898</td>\n",
       "      <td>25.008581</td>\n",
       "      <td>23.507575</td>\n",
       "      <td>...</td>\n",
       "      <td>0.027066</td>\n",
       "      <td>1.077748e-03</td>\n",
       "      <td>2.093454e-04</td>\n",
       "      <td>3.687535e-07</td>\n",
       "      <td>3.998053e-05</td>\n",
       "      <td>2.897240e-07</td>\n",
       "      <td>6.127700</td>\n",
       "      <td>0.794366</td>\n",
       "      <td>0.210973</td>\n",
       "      <td>0.183024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.985411</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>146.285714</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>6.828427</td>\n",
       "      <td>9.155272</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>2.514436e-07</td>\n",
       "      <td>6.992073e-09</td>\n",
       "      <td>-2.539652e-07</td>\n",
       "      <td>-5.698262e-05</td>\n",
       "      <td>-1.757113e-06</td>\n",
       "      <td>5.598834</td>\n",
       "      <td>1.048053</td>\n",
       "      <td>0.213334</td>\n",
       "      <td>0.529669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16.250000</td>\n",
       "      <td>194.750000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>15.745692</td>\n",
       "      <td>204.750000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>182.969505</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>52.602291</td>\n",
       "      <td>52.551616</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000627</td>\n",
       "      <td>3.032838e-05</td>\n",
       "      <td>1.242304e-07</td>\n",
       "      <td>-1.881559e-13</td>\n",
       "      <td>-1.039790e-08</td>\n",
       "      <td>-7.811552e-13</td>\n",
       "      <td>26.514258</td>\n",
       "      <td>1.168451</td>\n",
       "      <td>0.538616</td>\n",
       "      <td>0.805774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.500000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>448.500000</td>\n",
       "      <td>21.585875</td>\n",
       "      <td>376.500000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>190.749492</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>69.112698</td>\n",
       "      <td>68.204464</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001971</td>\n",
       "      <td>6.198102e-05</td>\n",
       "      <td>5.957846e-07</td>\n",
       "      <td>6.176303e-14</td>\n",
       "      <td>5.408146e-10</td>\n",
       "      <td>-9.288753e-15</td>\n",
       "      <td>29.017801</td>\n",
       "      <td>1.316003</td>\n",
       "      <td>0.757485</td>\n",
       "      <td>0.925560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>46.750000</td>\n",
       "      <td>500.750000</td>\n",
       "      <td>685.500000</td>\n",
       "      <td>25.250050</td>\n",
       "      <td>516.500000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>199.725305</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>86.097980</td>\n",
       "      <td>84.307520</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010151</td>\n",
       "      <td>3.475818e-04</td>\n",
       "      <td>3.799021e-05</td>\n",
       "      <td>1.135209e-11</td>\n",
       "      <td>1.484276e-08</td>\n",
       "      <td>1.902152e-12</td>\n",
       "      <td>32.534123</td>\n",
       "      <td>1.769976</td>\n",
       "      <td>0.851463</td>\n",
       "      <td>0.966037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>896.000000</td>\n",
       "      <td>1350.000000</td>\n",
       "      <td>33.776066</td>\n",
       "      <td>977.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>220.026144</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>129.982756</td>\n",
       "      <td>125.912897</td>\n",
       "      <td>...</td>\n",
       "      <td>0.125246</td>\n",
       "      <td>5.440067e-03</td>\n",
       "      <td>1.300612e-03</td>\n",
       "      <td>2.775191e-06</td>\n",
       "      <td>3.027878e-04</td>\n",
       "      <td>1.321910e-06</td>\n",
       "      <td>38.323999</td>\n",
       "      <td>4.417297</td>\n",
       "      <td>0.974824</td>\n",
       "      <td>1.886542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 86 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           label        area    bbox_area  equivalent_diameter  convex_area  \\\n",
       "count  62.000000   62.000000    62.000000            62.000000    62.000000   \n",
       "mean   31.500000  355.370968   475.677419            20.074583   372.790323   \n",
       "std    18.041619  211.367385   300.328169             7.091876   223.801078   \n",
       "min     1.000000    7.000000     9.000000             2.985411     7.000000   \n",
       "25%    16.250000  194.750000   260.000000            15.745692   204.750000   \n",
       "50%    31.500000  366.000000   448.500000            21.585875   376.500000   \n",
       "75%    46.750000  500.750000   685.500000            25.250050   516.500000   \n",
       "max    62.000000  896.000000  1350.000000            33.776066   977.000000   \n",
       "\n",
       "       max_intensity  mean_intensity  min_intensity   perimeter  \\\n",
       "count      62.000000       62.000000      62.000000   62.000000   \n",
       "mean      233.548387      190.429888     125.161290   67.787235   \n",
       "std        19.371838       15.382559       4.602898   25.008581   \n",
       "min       152.000000      146.285714     112.000000    6.828427   \n",
       "25%       232.000000      182.969505     120.000000   52.602291   \n",
       "50%       240.000000      190.749492     128.000000   69.112698   \n",
       "75%       248.000000      199.725305     128.000000   86.097980   \n",
       "max       248.000000      220.026144     136.000000  129.982756   \n",
       "\n",
       "       perimeter_crofton  ...  moments_hu-1  moments_hu-2  moments_hu-3  \\\n",
       "count          62.000000  ...     62.000000  6.200000e+01  6.200000e+01   \n",
       "mean           67.071263  ...      0.012854  5.166382e-04  7.053581e-05   \n",
       "std            23.507575  ...      0.027066  1.077748e-03  2.093454e-04   \n",
       "min             9.155272  ...      0.000056  2.514436e-07  6.992073e-09   \n",
       "25%            52.551616  ...      0.000627  3.032838e-05  1.242304e-07   \n",
       "50%            68.204464  ...      0.001971  6.198102e-05  5.957846e-07   \n",
       "75%            84.307520  ...      0.010151  3.475818e-04  3.799021e-05   \n",
       "max           125.912897  ...      0.125246  5.440067e-03  1.300612e-03   \n",
       "\n",
       "       moments_hu-4  moments_hu-5  moments_hu-6  standard_deviation_intensity  \\\n",
       "count  6.200000e+01  6.200000e+01  6.200000e+01                     62.000000   \n",
       "mean   5.855846e-08  3.633047e-06 -8.703715e-09                     28.689171   \n",
       "std    3.687535e-07  3.998053e-05  2.897240e-07                      6.127700   \n",
       "min   -2.539652e-07 -5.698262e-05 -1.757113e-06                      5.598834   \n",
       "25%   -1.881559e-13 -1.039790e-08 -7.811552e-13                     26.514258   \n",
       "50%    6.176303e-14  5.408146e-10 -9.288753e-15                     29.017801   \n",
       "75%    1.135209e-11  1.484276e-08  1.902152e-12                     32.534123   \n",
       "max    2.775191e-06  3.027878e-04  1.321910e-06                     38.323999   \n",
       "\n",
       "       aspect_ratio  roundness  circularity  \n",
       "count     62.000000  62.000000    62.000000  \n",
       "mean       1.637991   0.692418     0.894101  \n",
       "std        0.794366   0.210973     0.183024  \n",
       "min        1.048053   0.213334     0.529669  \n",
       "25%        1.168451   0.538616     0.805774  \n",
       "50%        1.316003   0.757485     0.925560  \n",
       "75%        1.769976   0.851463     0.966037  \n",
       "max        4.417297   0.974824     1.886542  \n",
       "\n",
       "[8 rows x 86 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f9062ac-9397-4542-8847-18ef303bccf1",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8ea0021-8273-4b38-af0a-afea8ccace53",
   "metadata": {},
   "source": [
    "Make a table with only `area`, `mean_intensity`, `standard_deviation_intensity` and `label`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b26716e9-4b78-46d6-b0ec-77cfc28188e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1f3c93d1-2488-4e0d-8d83-f5c85ac19c63",
   "metadata": {},
   "source": [
    "How many object are in the dataframe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6047d3af-adcc-4424-9da1-2da52b4030b9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "069ce8f5-c157-4301-a220-2ee812520a03",
   "metadata": {},
   "source": [
    "How large is the largest object?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32856292-6fea-49e2-a024-a71a320291c8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f08e83ff-46bd-4763-96b5-47d761c7f2c2",
   "metadata": {},
   "source": [
    "What is the mean intensity of the brightest object?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2831915-9c91-459f-8679-3dc6543048c3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0dd1865b-fff2-40d5-a902-0401d934ea08",
   "metadata": {
    "tags": []
   },
   "source": [
    "What are mean and standard deviation intensity of the image?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c6de947-8b2e-466d-a4a1-a65467057674",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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