{ "cells": [ { "cell_type": "markdown", "id": "aae3bd80-16e5-4bed-b09e-5ee84f25d33e", "metadata": {}, "source": [ "# Statistics using SimpleITK\n", "We can use [SimpleITK](https://simpleitk.readthedocs.io/) for extracting features from 3D label images. For convenience reasons we use the [napari-simpleitk-image-processing](https://github.com/haesleinhuepf/napari-simpleitk-image-processing) library." ] }, { "cell_type": "code", "execution_count": 1, "id": "36ad16e3", "metadata": { "tags": [] }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from skimage.data import cells3d\n", "import stackview\n", "from skimage import measure\n", "from skimage import filters\n", "import napari_simpleitk_image_processing as nsitk" ] }, { "cell_type": "code", "execution_count": 2, "id": "e5acbb57-8f00-4f01-a968-8c57e6098c50", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
shape(60, 256, 256)
dtypeuint16
size7.5 MB
min0
max65535
\n", "\n", "
" ], "text/plain": [ "StackViewNDArray([[[5311, 4458, 5880, ..., 4220, 6497, 4932],\n", " [4078, 4552, 3557, ..., 4552, 4884, 5169],\n", " [3414, 5074, 4363, ..., 4078, 6117, 5406],\n", " ...,\n", " [3983, 3983, 2750, ..., 4410, 4600, 5880],\n", " [3604, 4173, 4600, ..., 5548, 5690, 4268],\n", " [4078, 4268, 4220, ..., 5359, 6686, 7492]],\n", "\n", " [[4220, 4932, 5074, ..., 6449, 6117, 4932],\n", " [3082, 4979, 4505, ..., 4505, 6971, 5880],\n", " [4695, 4315, 4695, ..., 4742, 5785, 5074],\n", " ...,\n", " [3414, 3841, 3462, ..., 5169, 4742, 5027],\n", " [4173, 3319, 2798, ..., 5406, 4600, 4837],\n", " [3225, 3557, 3651, ..., 5453, 5406, 4837]],\n", "\n", " [[4552, 4505, 4742, ..., 4458, 4979, 4647],\n", " [3367, 4268, 4647, ..., 6402, 6022, 5738],\n", " [3509, 3272, 4647, ..., 5264, 4220, 6259],\n", " ...,\n", " [3841, 3936, 3888, ..., 4884, 5169, 4789],\n", " [3604, 3225, 2798, ..., 4552, 6117, 5690],\n", " [4126, 4458, 4837, ..., 5928, 6734, 5027]],\n", "\n", " ...,\n", "\n", " [[5121, 5833, 5738, ..., 5406, 3983, 4220],\n", " [5311, 5453, 5453, ..., 5169, 5027, 4884],\n", " [6544, 5121, 5264, ..., 4363, 5406, 4695],\n", " ...,\n", " [4884, 2324, 3130, ..., 4552, 4979, 6070],\n", " [3225, 2513, 2513, ..., 4742, 3035, 2418],\n", " [3272, 2798, 3888, ..., 3604, 4268, 3746]],\n", "\n", " [[5359, 5928, 5738, ..., 4268, 4505, 4031],\n", " [7445, 6829, 4268, ..., 4884, 5596, 3082],\n", " [6497, 6544, 4932, ..., 3604, 5359, 4410],\n", " ...,\n", " [5833, 2703, 3367, ..., 3177, 4220, 4220],\n", " [4031, 3509, 5216, ..., 4268, 3604, 4932],\n", " [3557, 2987, 3936, ..., 3604, 3462, 5264]],\n", "\n", " [[6402, 6781, 4789, ..., 5928, 6022, 3651],\n", " [4363, 4837, 6307, ..., 4315, 3651, 4126],\n", " [4979, 4695, 4742, ..., 4789, 4837, 4552],\n", " ...,\n", " [4078, 2513, 3035, ..., 1849, 3888, 4363],\n", " [3936, 3888, 4315, ..., 5216, 4031, 5643],\n", " [4173, 4505, 3794, ..., 4979, 6781, 4031]]],\n", " dtype=uint16)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# load image\n", "image = cells3d()[:,1]\n", "stackview.insight(image)" ] }, { "cell_type": "code", "execution_count": 3, "id": "7362ebaf-b4ca-492f-9d38-15c00633b334", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
shape(60, 256, 256)
dtypeint32
size15.0 MB
min0
max38
\n", "\n", "
" ], "text/plain": [ "StackViewNDArray([[[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]],\n", "\n", " [[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]],\n", "\n", " [[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]],\n", "\n", " ...,\n", "\n", " [[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]],\n", "\n", " [[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]],\n", "\n", " [[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]]])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "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", "stackview.insight(label_image)" ] }, { "cell_type": "markdown", "id": "2b76cf8f", "metadata": {}, "source": [ "## Measurements/ region properties" ] }, { "cell_type": "markdown", "id": "cd9b8b7c-d2d2-4fbf-9b9f-946b1a585225", "metadata": {}, "source": [ "We are now using the very handy function `label_statistics` which provides a table of features. Let us check first what we need to provide for this function:" ] }, { "cell_type": "code", "execution_count": 4, "id": "720b6ce2-e330-4127-9c45-1073502312f4", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "\u001b[1;31mSignature:\u001b[0m\n", "\u001b[0mnsitk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel_statistics\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", "\u001b[0m \u001b[0mintensity_image\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[0mlabel_image\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\n", "Measure intensity/shape/... statistics per label\n", "\n", "Parameters\n", "----------\n", "intensity_image: ndarray, optional\n", " Can be None\n", "label_image: ndarray\n", " Must be subsequently labeled\n", "size: bool, optional\n", "intensity: bool, optional\n", "perimeter: bool, optional\n", "shape: bool, optional\n", "position: bool, optional\n", "moments: bool, optional\n", "napari_viewer: napari.Viewer, optional\n", "\n", "Returns\n", "-------\n", "pandas DataFrame, in case napari_viewr is None, otherwise the DataFrame will be added to\n", "the passed label_image's layer.features\n", "\n", "\n", "See Also\n", "--------\n", "..[0] https://simpleitk.org/doxygen/latest/html/classitk_1_1simple_1_1LabelShapeStatisticsImageFilter\n", "..[1] http://insightsoftwareconsortium.github.io/SimpleITK-Notebooks/Python_html/35_Segmentation_Shape_Analysis.html\n", "\u001b[1;31mFile:\u001b[0m c:\\users\\haase\\mambaforge\\envs\\tea2024\\lib\\site-packages\\napari_simpleitk_image_processing\\_simpleitk_image_processing.py\n", "\u001b[1;31mType:\u001b[0m function" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nsitk.label_statistics?" ] }, { "cell_type": "markdown", "id": "561451f5-67d3-4fa6-824f-e061980fc597", "metadata": { "tags": [] }, "source": [ "Feature categories which have are set to `True` are measured by default. In this case, the categories are `size` and `intensity`. But the rest might be also interesting to investigate. So we need them to `True` as well:" ] }, { "cell_type": "code", "execution_count": 5, "id": "267088d1", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
labelmaximummeanmedianminimumsigmasumvariancebbox_0bbox_1...equivalent_ellipsoid_diameter_0equivalent_ellipsoid_diameter_1equivalent_ellipsoid_diameter_2equivalent_spherical_perimeterequivalent_spherical_radiusnumber_of_pixelsnumber_of_pixels_on_borderperimeterperimeter_on_borderperimeter_on_border_ratio
0165535.022623.94031619071.7089844126.012083.0179617.471330e+081.459993e+083358...24.61081846.27412055.3817704977.79768719.902778330245237186.004933523.00.072780
1247610.016500.85011416255.7519534837.04418.2716696.720961e+081.952112e+0787133...29.88841149.59718252.4767135724.89474421.3441484073106172.2369820.00.000000
2355766.015183.66595514463.7792974932.04399.1263771.414799e+091.935231e+0751192...28.53470751.817058120.3575259939.46071028.12396093179104915144.1191801049.00.069268
3465298.019238.18721018559.7167974458.06757.6795574.229708e+084.566623e+07228195...29.20705930.20376147.5991013795.31890417.3787772198611574286.5339411157.00.269915
4536134.013131.88795912927.8027345453.02797.9623301.357010e+097.828593e+061260...27.94018351.292200137.71354310649.30725129.11090810333778521514.657299785.00.036487
5634332.012687.76657012415.8105475833.02613.2575754.404812e+086.829115e+068648...28.52044440.16473957.8818415146.50324320.2372363471708387.4138620.00.000000
6741113.015498.46540215231.7675785643.03879.8075007.140453e+081.505291e+0710120...30.02933041.50411670.5995016215.01481422.2390464607207250.5310280.00.000000
7842394.013376.21649812927.8027345880.03092.7797054.901982e+089.565286e+0627158...29.60540044.62757952.9743395335.51579520.6055053664707516.2598680.00.000000
8955624.015445.16212214975.7714843983.04369.2055857.239456e+081.908996e+07144176...27.89077251.60138662.2003686286.75372522.3670294687207703.2610280.00.000000
91045903.012296.04911112159.8144535169.02351.1302393.923300e+085.527813e+0600...27.58954342.66787351.7656704864.90957919.675802319079108026.035532926.00.115375
101134617.012775.33621812415.8105476022.02542.6629524.295962e+086.465135e+065413...29.70970037.35897257.8623845038.20932120.0231863362706971.4198280.00.000000
111239406.014132.81130613695.7910165596.03229.6070775.340224e+081.043036e+0713773...31.07514846.90881749.5067725445.50387420.8168063778606298.7126270.00.000000
121339738.014336.41219313951.7871094837.03421.7969266.751303e+081.170869e+0722078...26.93909734.08424197.9516706306.41018222.4019684709217829251.0467571782.00.192627
131432910.014267.06530713951.7871095027.03382.2115676.359402e+081.143936e+07160115...27.65419049.88874661.7048736079.55600621.9953564457407637.8527410.00.000000
141525560.012693.64364512159.8144535975.02712.3618251.668833e+087.356907e+06184233...22.38520723.32434548.0902982693.81996014.641290131478145034.421312814.00.161687
151623378.011729.89840511647.8222666971.01929.3401142.794062e+073.722353e+060165...8.03522615.88867035.633420862.5387788.28484523824801533.882378480.00.312931
161721624.012051.44049211903.8183596829.01909.3103424.506034e+073.645466e+06610...9.11914621.32111836.7276111164.9870599.62843337396461806.519762646.00.357594
171818067.012642.77505612671.8066417113.01887.7219745.676606e+063.563494e+0642252...3.42116912.45705120.121389283.5622104.750280449201467.703703201.00.429759
181918494.013171.95454513183.7988287777.02080.4400884.346745e+064.328231e+062470...4.0633509.27431616.724348230.9369464.286884330172326.481063189.00.578900
192012993.011074.95000010879.8339849626.0866.0093272.214990e+057.499722e+05041...1.5000343.8033076.69528135.6317751.68389020841.6252998.00.192191
202115412.011762.40000011647.8222668536.01432.6747465.293080e+052.052557e+06054...2.5901664.2440047.81826861.1823002.206521452668.98293726.00.376905
212225607.012046.94265811903.8183596212.01987.2482595.882522e+073.949156e+061910...12.17730722.23053734.4498131391.90183510.52444948835312100.978140531.00.252739
222322667.014137.54226513951.7871098014.02564.7944768.864239e+066.578171e+0625214...3.93173214.77942020.607609354.2657375.309574627258555.992667258.00.464035
232413278.012313.33333312671.80664111096.01112.7269813.694000e+041.238161e+06075...0.0000000.0000000.00000010.0592350.894700338.0706013.00.371720
242515933.011565.90277811391.8261728868.01472.3008848.327450e+052.167670e+060206...3.2954604.8959388.52279483.6962092.580762722894.93021828.00.294953
252615933.011984.94202911903.8183598962.01716.5079118.269610e+052.946399e+06061...2.8106714.7417849.88777981.3548632.544408693899.58388338.00.381588
262711333.011333.00000011391.82617211333.00.0000001.133300e+040.000000e+00651...0.0000000.0000000.0000004.8359760.620350103.0040800.00.000000
272822288.015056.16666714975.7714849247.03374.2795281.806740e+051.138576e+0716372...2.2039162.5212464.12451025.3476841.42024812024.5424580.00.000000
282916977.014581.75000016255.75195311144.02606.1292345.832700e+046.791910e+06164179...0.0000000.0000000.00000012.1858960.9847454010.4992640.00.000000
293037747.016443.56716413951.7871096639.07418.6312901.101719e+065.503609e+0775163...3.5954634.8441377.34691479.7750942.51958367082.9739660.00.000000
303116834.014415.50000016767.74414111997.03420.2755012.883100e+041.169828e+07184186...0.0000000.0000000.0000007.6766330.781593205.6419370.00.000000
313222430.015296.42857116511.74804710764.03278.6866442.141500e+051.074979e+0769159...2.1607993.1860803.88381328.0911481.49513314026.1975750.00.000000
323326271.015447.66346215487.7636728346.03648.5685571.606557e+061.331205e+0719990...5.0985515.8077026.707854106.9480862.9173031040105.0577260.00.000000
333418873.015003.80000014719.77539112661.02099.3125221.500380e+054.407113e+0612872...2.1445122.3976383.71440522.4466121.33650510020.2249820.00.000000
343522667.015211.44444414719.7753917540.04278.2619614.107090e+051.830353e+074238...2.1964273.7384606.27994143.5237831.86105127048.5094990.00.000000
353616834.014494.66666714975.77148412282.01758.6261308.696800e+043.092766e+060132...0.0000000.0000000.00000015.9680401.1272526614.6241446.00.410280
363719585.019300.50000019583.70117219016.0402.3437583.860100e+041.618805e+0524369...0.0000000.0000000.0000007.6766330.781593205.6419370.00.000000
373814321.012139.50000014207.7832039958.03085.1068862.427900e+049.517884e+06590...0.0000000.0000000.0000007.6766330.781593225.6419372.00.354488
\n", "

38 rows × 31 columns

\n", "
" ], "text/plain": [ " label maximum mean median minimum sigma \\\n", "0 1 65535.0 22623.940316 19071.708984 4126.0 12083.017961 \n", "1 2 47610.0 16500.850114 16255.751953 4837.0 4418.271669 \n", "2 3 55766.0 15183.665955 14463.779297 4932.0 4399.126377 \n", "3 4 65298.0 19238.187210 18559.716797 4458.0 6757.679557 \n", "4 5 36134.0 13131.887959 12927.802734 5453.0 2797.962330 \n", "5 6 34332.0 12687.766570 12415.810547 5833.0 2613.257575 \n", "6 7 41113.0 15498.465402 15231.767578 5643.0 3879.807500 \n", "7 8 42394.0 13376.216498 12927.802734 5880.0 3092.779705 \n", "8 9 55624.0 15445.162122 14975.771484 3983.0 4369.205585 \n", "9 10 45903.0 12296.049111 12159.814453 5169.0 2351.130239 \n", "10 11 34617.0 12775.336218 12415.810547 6022.0 2542.662952 \n", "11 12 39406.0 14132.811306 13695.791016 5596.0 3229.607077 \n", "12 13 39738.0 14336.412193 13951.787109 4837.0 3421.796926 \n", "13 14 32910.0 14267.065307 13951.787109 5027.0 3382.211567 \n", "14 15 25560.0 12693.643645 12159.814453 5975.0 2712.361825 \n", "15 16 23378.0 11729.898405 11647.822266 6971.0 1929.340114 \n", "16 17 21624.0 12051.440492 11903.818359 6829.0 1909.310342 \n", "17 18 18067.0 12642.775056 12671.806641 7113.0 1887.721974 \n", "18 19 18494.0 13171.954545 13183.798828 7777.0 2080.440088 \n", "19 20 12993.0 11074.950000 10879.833984 9626.0 866.009327 \n", "20 21 15412.0 11762.400000 11647.822266 8536.0 1432.674746 \n", "21 22 25607.0 12046.942658 11903.818359 6212.0 1987.248259 \n", "22 23 22667.0 14137.542265 13951.787109 8014.0 2564.794476 \n", "23 24 13278.0 12313.333333 12671.806641 11096.0 1112.726981 \n", "24 25 15933.0 11565.902778 11391.826172 8868.0 1472.300884 \n", "25 26 15933.0 11984.942029 11903.818359 8962.0 1716.507911 \n", "26 27 11333.0 11333.000000 11391.826172 11333.0 0.000000 \n", "27 28 22288.0 15056.166667 14975.771484 9247.0 3374.279528 \n", "28 29 16977.0 14581.750000 16255.751953 11144.0 2606.129234 \n", "29 30 37747.0 16443.567164 13951.787109 6639.0 7418.631290 \n", "30 31 16834.0 14415.500000 16767.744141 11997.0 3420.275501 \n", "31 32 22430.0 15296.428571 16511.748047 10764.0 3278.686644 \n", "32 33 26271.0 15447.663462 15487.763672 8346.0 3648.568557 \n", "33 34 18873.0 15003.800000 14719.775391 12661.0 2099.312522 \n", "34 35 22667.0 15211.444444 14719.775391 7540.0 4278.261961 \n", "35 36 16834.0 14494.666667 14975.771484 12282.0 1758.626130 \n", "36 37 19585.0 19300.500000 19583.701172 19016.0 402.343758 \n", "37 38 14321.0 12139.500000 14207.783203 9958.0 3085.106886 \n", "\n", " sum variance bbox_0 bbox_1 ... \\\n", "0 7.471330e+08 1.459993e+08 33 58 ... \n", "1 6.720961e+08 1.952112e+07 87 133 ... \n", "2 1.414799e+09 1.935231e+07 51 192 ... \n", "3 4.229708e+08 4.566623e+07 228 195 ... \n", "4 1.357010e+09 7.828593e+06 126 0 ... \n", "5 4.404812e+08 6.829115e+06 86 48 ... \n", "6 7.140453e+08 1.505291e+07 10 120 ... \n", "7 4.901982e+08 9.565286e+06 27 158 ... \n", "8 7.239456e+08 1.908996e+07 144 176 ... \n", "9 3.923300e+08 5.527813e+06 0 0 ... \n", "10 4.295962e+08 6.465135e+06 54 13 ... \n", "11 5.340224e+08 1.043036e+07 137 73 ... \n", "12 6.751303e+08 1.170869e+07 220 78 ... \n", "13 6.359402e+08 1.143936e+07 160 115 ... \n", "14 1.668833e+08 7.356907e+06 184 233 ... \n", "15 2.794062e+07 3.722353e+06 0 165 ... \n", "16 4.506034e+07 3.645466e+06 61 0 ... \n", "17 5.676606e+06 3.563494e+06 42 252 ... \n", "18 4.346745e+06 4.328231e+06 247 0 ... \n", "19 2.214990e+05 7.499722e+05 0 41 ... \n", "20 5.293080e+05 2.052557e+06 0 54 ... \n", "21 5.882522e+07 3.949156e+06 191 0 ... \n", "22 8.864239e+06 6.578171e+06 252 14 ... \n", "23 3.694000e+04 1.238161e+06 0 75 ... \n", "24 8.327450e+05 2.167670e+06 0 206 ... \n", "25 8.269610e+05 2.946399e+06 0 61 ... \n", "26 1.133300e+04 0.000000e+00 6 51 ... \n", "27 1.806740e+05 1.138576e+07 163 72 ... \n", "28 5.832700e+04 6.791910e+06 164 179 ... \n", "29 1.101719e+06 5.503609e+07 75 163 ... \n", "30 2.883100e+04 1.169828e+07 184 186 ... \n", "31 2.141500e+05 1.074979e+07 69 159 ... \n", "32 1.606557e+06 1.331205e+07 199 90 ... \n", "33 1.500380e+05 4.407113e+06 128 72 ... \n", "34 4.107090e+05 1.830353e+07 42 38 ... \n", "35 8.696800e+04 3.092766e+06 0 132 ... \n", "36 3.860100e+04 1.618805e+05 243 69 ... \n", "37 2.427900e+04 9.517884e+06 59 0 ... \n", "\n", " equivalent_ellipsoid_diameter_0 equivalent_ellipsoid_diameter_1 \\\n", "0 24.610818 46.274120 \n", "1 29.888411 49.597182 \n", "2 28.534707 51.817058 \n", "3 29.207059 30.203761 \n", "4 27.940183 51.292200 \n", "5 28.520444 40.164739 \n", "6 30.029330 41.504116 \n", "7 29.605400 44.627579 \n", "8 27.890772 51.601386 \n", "9 27.589543 42.667873 \n", "10 29.709700 37.358972 \n", "11 31.075148 46.908817 \n", "12 26.939097 34.084241 \n", "13 27.654190 49.888746 \n", "14 22.385207 23.324345 \n", "15 8.035226 15.888670 \n", "16 9.119146 21.321118 \n", "17 3.421169 12.457051 \n", "18 4.063350 9.274316 \n", "19 1.500034 3.803307 \n", "20 2.590166 4.244004 \n", "21 12.177307 22.230537 \n", "22 3.931732 14.779420 \n", "23 0.000000 0.000000 \n", "24 3.295460 4.895938 \n", "25 2.810671 4.741784 \n", "26 0.000000 0.000000 \n", "27 2.203916 2.521246 \n", "28 0.000000 0.000000 \n", "29 3.595463 4.844137 \n", "30 0.000000 0.000000 \n", "31 2.160799 3.186080 \n", "32 5.098551 5.807702 \n", "33 2.144512 2.397638 \n", "34 2.196427 3.738460 \n", "35 0.000000 0.000000 \n", "36 0.000000 0.000000 \n", "37 0.000000 0.000000 \n", "\n", " equivalent_ellipsoid_diameter_2 equivalent_spherical_perimeter \\\n", "0 55.381770 4977.797687 \n", "1 52.476713 5724.894744 \n", "2 120.357525 9939.460710 \n", "3 47.599101 3795.318904 \n", "4 137.713543 10649.307251 \n", "5 57.881841 5146.503243 \n", "6 70.599501 6215.014814 \n", "7 52.974339 5335.515795 \n", "8 62.200368 6286.753725 \n", "9 51.765670 4864.909579 \n", "10 57.862384 5038.209321 \n", "11 49.506772 5445.503874 \n", "12 97.951670 6306.410182 \n", "13 61.704873 6079.556006 \n", "14 48.090298 2693.819960 \n", "15 35.633420 862.538778 \n", "16 36.727611 1164.987059 \n", "17 20.121389 283.562210 \n", "18 16.724348 230.936946 \n", "19 6.695281 35.631775 \n", "20 7.818268 61.182300 \n", "21 34.449813 1391.901835 \n", "22 20.607609 354.265737 \n", "23 0.000000 10.059235 \n", "24 8.522794 83.696209 \n", "25 9.887779 81.354863 \n", "26 0.000000 4.835976 \n", "27 4.124510 25.347684 \n", "28 0.000000 12.185896 \n", "29 7.346914 79.775094 \n", "30 0.000000 7.676633 \n", "31 3.883813 28.091148 \n", "32 6.707854 106.948086 \n", "33 3.714405 22.446612 \n", "34 6.279941 43.523783 \n", "35 0.000000 15.968040 \n", "36 0.000000 7.676633 \n", "37 0.000000 7.676633 \n", "\n", " equivalent_spherical_radius number_of_pixels number_of_pixels_on_border \\\n", "0 19.902778 33024 523 \n", "1 21.344148 40731 0 \n", "2 28.123960 93179 1049 \n", "3 17.378777 21986 1157 \n", "4 29.110908 103337 785 \n", "5 20.237236 34717 0 \n", "6 22.239046 46072 0 \n", "7 20.605505 36647 0 \n", "8 22.367029 46872 0 \n", "9 19.675802 31907 910 \n", "10 20.023186 33627 0 \n", "11 20.816806 37786 0 \n", "12 22.401968 47092 1782 \n", "13 21.995356 44574 0 \n", "14 14.641290 13147 814 \n", "15 8.284845 2382 480 \n", "16 9.628433 3739 646 \n", "17 4.750280 449 201 \n", "18 4.286884 330 172 \n", "19 1.683890 20 8 \n", "20 2.206521 45 26 \n", "21 10.524449 4883 531 \n", "22 5.309574 627 258 \n", "23 0.894700 3 3 \n", "24 2.580762 72 28 \n", "25 2.544408 69 38 \n", "26 0.620350 1 0 \n", "27 1.420248 12 0 \n", "28 0.984745 4 0 \n", "29 2.519583 67 0 \n", "30 0.781593 2 0 \n", "31 1.495133 14 0 \n", "32 2.917303 104 0 \n", "33 1.336505 10 0 \n", "34 1.861051 27 0 \n", "35 1.127252 6 6 \n", "36 0.781593 2 0 \n", "37 0.781593 2 2 \n", "\n", " perimeter perimeter_on_border perimeter_on_border_ratio \n", "0 7186.004933 523.0 0.072780 \n", "1 6172.236982 0.0 0.000000 \n", "2 15144.119180 1049.0 0.069268 \n", "3 4286.533941 1157.0 0.269915 \n", "4 21514.657299 785.0 0.036487 \n", "5 8387.413862 0.0 0.000000 \n", "6 7250.531028 0.0 0.000000 \n", "7 7516.259868 0.0 0.000000 \n", "8 7703.261028 0.0 0.000000 \n", "9 8026.035532 926.0 0.115375 \n", "10 6971.419828 0.0 0.000000 \n", "11 6298.712627 0.0 0.000000 \n", "12 9251.046757 1782.0 0.192627 \n", "13 7637.852741 0.0 0.000000 \n", "14 5034.421312 814.0 0.161687 \n", "15 1533.882378 480.0 0.312931 \n", "16 1806.519762 646.0 0.357594 \n", "17 467.703703 201.0 0.429759 \n", "18 326.481063 189.0 0.578900 \n", "19 41.625299 8.0 0.192191 \n", "20 68.982937 26.0 0.376905 \n", "21 2100.978140 531.0 0.252739 \n", "22 555.992667 258.0 0.464035 \n", "23 8.070601 3.0 0.371720 \n", "24 94.930218 28.0 0.294953 \n", "25 99.583883 38.0 0.381588 \n", "26 3.004080 0.0 0.000000 \n", "27 24.542458 0.0 0.000000 \n", "28 10.499264 0.0 0.000000 \n", "29 82.973966 0.0 0.000000 \n", "30 5.641937 0.0 0.000000 \n", "31 26.197575 0.0 0.000000 \n", "32 105.057726 0.0 0.000000 \n", "33 20.224982 0.0 0.000000 \n", "34 48.509499 0.0 0.000000 \n", "35 14.624144 6.0 0.410280 \n", "36 5.641937 0.0 0.000000 \n", "37 5.641937 2.0 0.354488 \n", "\n", "[38 rows x 31 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(nsitk.label_statistics(image, label_image, \n", " size=True,\n", " shape=True, \n", " perimeter=True, \n", " position=True))\n", "\n", "df" ] }, { "cell_type": "markdown", "id": "8294dbed-9684-4207-a904-da967f83c467", "metadata": {}, "source": [ "These are all columns that are available:" ] }, { "cell_type": "code", "execution_count": 6, "id": "fa083df5-9c61-4513-9723-bed43e8e20d3", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['label', 'maximum', 'mean', 'median', 'minimum', 'sigma', 'sum',\n", " 'variance', 'bbox_0', 'bbox_1', 'bbox_2', 'bbox_3', 'bbox_4', 'bbox_5',\n", " 'centroid_0', 'centroid_1', 'centroid_2', 'elongation',\n", " 'feret_diameter', 'flatness', 'roundness',\n", " 'equivalent_ellipsoid_diameter_0', 'equivalent_ellipsoid_diameter_1',\n", " 'equivalent_ellipsoid_diameter_2', 'equivalent_spherical_perimeter',\n", " 'equivalent_spherical_radius', 'number_of_pixels',\n", " 'number_of_pixels_on_border', 'perimeter', 'perimeter_on_border',\n", " 'perimeter_on_border_ratio'],\n", " dtype='object')\n" ] } ], "source": [ "print(df.keys())" ] }, { "cell_type": "code", "execution_count": 7, "id": "ea23a512", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
labelmaximummeanmedianminimumsigmasumvariancebbox_0bbox_1...equivalent_ellipsoid_diameter_0equivalent_ellipsoid_diameter_1equivalent_ellipsoid_diameter_2equivalent_spherical_perimeterequivalent_spherical_radiusnumber_of_pixelsnumber_of_pixels_on_borderperimeterperimeter_on_borderperimeter_on_border_ratio
count38.00000038.00000038.00000038.00000038.00000038.0000003.800000e+013.800000e+0138.00000038.000000...38.00000038.00000038.00000038.00000038.00000038.00000038.00000038.00000038.00000038.000000
mean19.50000029249.63157914141.93496014012.4177637860.5789473062.6362102.624646e+081.371663e+0796.63157996.605263...12.89273520.45912032.9818982458.45381510.26803917830.736842247.8684213574.679397248.7368420.149901
std11.11305514853.9857292419.9568772179.0652053112.7680272110.4736383.820512e+082.467465e+0784.99456974.168012...12.76226819.69917834.7887213095.8634669.62511726189.733760426.4750034893.955404427.0805790.178247
min1.00000011333.00000011074.95000010879.8339843983.0000000.0000001.133300e+040.000000e+000.0000000.000000...0.0000000.0000000.0000004.8359760.6203501.0000000.0000003.0040800.0000000.000000
25%10.25000017249.50000012300.37016712223.8134775607.7500001914.3177852.159872e+053.664688e+0614.25000042.750000...2.1697063.3241754.66336829.9763041.54232215.5000000.00000030.0545060.0000000.000000
50%19.50000023022.50000014135.17678513951.7871097042.0000002662.8097007.270422e+067.093011e+0672.00000074.000000...4.58095013.61823620.364499318.9139735.029927538.0000002.500000511.8481852.5000000.052877
75%28.75000038991.25000015204.49982214975.7714849531.2500003421.4165704.777690e+081.170609e+07163.750000162.000000...27.83162741.16927252.8499325119.42976320.18372334444.500000424.5000007132.358657424.5000000.308437
max38.00000065535.00000022623.94031619583.70117219016.00000012083.0179611.414799e+091.459993e+08252.000000252.000000...31.07514851.817058137.71354310649.30725129.110908103337.0000001782.00000021514.6572991782.0000000.578900
\n", "

8 rows × 31 columns

\n", "
" ], "text/plain": [ " label maximum mean median minimum \\\n", "count 38.000000 38.000000 38.000000 38.000000 38.000000 \n", "mean 19.500000 29249.631579 14141.934960 14012.417763 7860.578947 \n", "std 11.113055 14853.985729 2419.956877 2179.065205 3112.768027 \n", "min 1.000000 11333.000000 11074.950000 10879.833984 3983.000000 \n", "25% 10.250000 17249.500000 12300.370167 12223.813477 5607.750000 \n", "50% 19.500000 23022.500000 14135.176785 13951.787109 7042.000000 \n", "75% 28.750000 38991.250000 15204.499822 14975.771484 9531.250000 \n", "max 38.000000 65535.000000 22623.940316 19583.701172 19016.000000 \n", "\n", " sigma sum variance bbox_0 bbox_1 ... \\\n", "count 38.000000 3.800000e+01 3.800000e+01 38.000000 38.000000 ... \n", "mean 3062.636210 2.624646e+08 1.371663e+07 96.631579 96.605263 ... \n", "std 2110.473638 3.820512e+08 2.467465e+07 84.994569 74.168012 ... \n", "min 0.000000 1.133300e+04 0.000000e+00 0.000000 0.000000 ... \n", "25% 1914.317785 2.159872e+05 3.664688e+06 14.250000 42.750000 ... \n", "50% 2662.809700 7.270422e+06 7.093011e+06 72.000000 74.000000 ... \n", "75% 3421.416570 4.777690e+08 1.170609e+07 163.750000 162.000000 ... \n", "max 12083.017961 1.414799e+09 1.459993e+08 252.000000 252.000000 ... \n", "\n", " equivalent_ellipsoid_diameter_0 equivalent_ellipsoid_diameter_1 \\\n", "count 38.000000 38.000000 \n", "mean 12.892735 20.459120 \n", "std 12.762268 19.699178 \n", "min 0.000000 0.000000 \n", "25% 2.169706 3.324175 \n", "50% 4.580950 13.618236 \n", "75% 27.831627 41.169272 \n", "max 31.075148 51.817058 \n", "\n", " equivalent_ellipsoid_diameter_2 equivalent_spherical_perimeter \\\n", "count 38.000000 38.000000 \n", "mean 32.981898 2458.453815 \n", "std 34.788721 3095.863466 \n", "min 0.000000 4.835976 \n", "25% 4.663368 29.976304 \n", "50% 20.364499 318.913973 \n", "75% 52.849932 5119.429763 \n", "max 137.713543 10649.307251 \n", "\n", " equivalent_spherical_radius number_of_pixels \\\n", "count 38.000000 38.000000 \n", "mean 10.268039 17830.736842 \n", "std 9.625117 26189.733760 \n", "min 0.620350 1.000000 \n", "25% 1.542322 15.500000 \n", "50% 5.029927 538.000000 \n", "75% 20.183723 34444.500000 \n", "max 29.110908 103337.000000 \n", "\n", " number_of_pixels_on_border perimeter perimeter_on_border \\\n", "count 38.000000 38.000000 38.000000 \n", "mean 247.868421 3574.679397 248.736842 \n", "std 426.475003 4893.955404 427.080579 \n", "min 0.000000 3.004080 0.000000 \n", "25% 0.000000 30.054506 0.000000 \n", "50% 2.500000 511.848185 2.500000 \n", "75% 424.500000 7132.358657 424.500000 \n", "max 1782.000000 21514.657299 1782.000000 \n", "\n", " perimeter_on_border_ratio \n", "count 38.000000 \n", "mean 0.149901 \n", "std 0.178247 \n", "min 0.000000 \n", "25% 0.000000 \n", "50% 0.052877 \n", "75% 0.308437 \n", "max 0.578900 \n", "\n", "[8 rows x 31 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "id": "29c8e483-a292-4016-87cc-0ab23d182300", "metadata": { "tags": [] }, "source": [ "## Exercise\n", "Load `data/blobs.tif`, segment the objects and measure `feret_diameter`, `flatness`, `roundness` and `elongation`. Compare the measurements with scikit-image." ] }, { "cell_type": "code", "execution_count": null, "id": "5ae9170e-85e7-4e1f-92df-e71ab2796946", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 }