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79 changes: 77 additions & 2 deletions README.md
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Expand Up @@ -68,11 +68,86 @@ from PVBM.DiscSegmenter import DiscSegmenter
segmenter = DiscSegmenter()

# Define the segmentation path and replace specific parts of the path
segmentation_path = '../PVBM_datasets/INSPIRE/artery/image13.png'
optic_disc = segmenter.segment(str(segmentation_path))
image_path = '../PVBM_datasets/INSPIRE/images/image13.png'
# Extract the segmentation
optic_disc = segmenter.segment(image_path)
#Extract the optic disc features
center, radius, roi, zones_ABC = segmenter.post_processing(optic_disc, max_roi_size = 600)
```

### Geometrical VBMs
```python
### First run the optic disc segmentation snippet to extract center, radius, roi, zones_ABC

from PVBM.GeometryAnalysis import GeometricalVBMs #Import the geometry analysis module
#Preprocessing and roi extraction
blood_vessel_segmentation_path = '../PVBM_datasets/INSPIRE/artery/image13.png'
segmentation = np.array(Image.open(blood_vessel_segmentation_path))/255 #Open the segmentation
skeleton = skeletonize(segmentation)*1
segmentation_roi = segmentation * (1-zones_ABC[:,:,1]/255)
segmentation_roi = segmentation_roi * roi[:,:,1]/255
skeleton_roi = skeleton * (1-zones_ABC[:,:,1]/255)
skeleton_roi = skeleton_roi * roi[:,:,1]/255

geometricalVBMs = GeometricalVBMs() #Instanciate a geometrical VBM object
vbms, visual = geometricalVBMs.compute_geomVBMs(segmentation_roi, skeleton_roi, center[0], center[1], radius)
area, TI, medTor, ovlen, medianba, startp, endp, interp = vbms
```

### Fractal Analysis
```python
### First run the optic disc segmentation snippet to extract center, radius, roi, zones_ABC

from PVBM.FractalAnalysis import MultifractalVBMs
#Preprocessing and roi extraction
blood_vessel_segmentation_path = '../PVBM_datasets/INSPIRE/artery/image13.png'
segmentation = np.array(Image.open(blood_vessel_segmentation_path))/255 #Open the segmentation
segmentation_roi = segmentation * (1-zones_ABC[:,:,1]/255)
segmentation_roi = segmentation_roi * roi[:,:,1]/255

fractalVBMs = MultifractalVBMs(n_rotations = 25,optimize = True, min_proba = 0.0001, maxproba = 0.9999)
D0,D1,D2,SL = fractalVBMs.compute_multifractals(segmentation_roi.copy())
```

### Central Retinal Equivalent Analysis

```python
### First run the optic disc segmentation snippet to extract center, radius, roi, zones_ABC

from PVBM.CentralRetinalAnalysis import CREVBMs
#Preprocessing and roi extraction
zone_A_ = zones_ABC[:,:,1]/255
zone_B_ = zones_ABC[:,:,0]/255
zone_C_ = zones_ABC[:,:,2]/255
roi = (zone_C_ - zone_B_)
creVBMs = CREVBMs()

####Artery
blood_vessel_segmentation_path = '../PVBM_datasets/INSPIRE/artery/image13.png'
segmentation = np.array(Image.open(blood_vessel_segmentation_path))/255 #Open the segmentation
skeleton = skeletonize(segmentation)*1
segmentation_roi = (segmentation * roi)
skeleton_roi = (skeleton * roi)
out = creVBMs.compute_central_retinal_equivalents(segmentation_roi.copy(), skeleton_roi.copy(),center[0],center[1], radius, artery = True, Toplot = True )
craek, craeh = out["craek"], out["craeh"]

####Veins
blood_vessel_segmentation_path = '../PVBM_datasets/INSPIRE/veins/image13.png'
segmentation = np.array(Image.open(blood_vessel_segmentation_path))/255 #Open the segmentation
skeleton = skeletonize(segmentation)*1
segmentation_roi = (segmentation * roi)
skeleton_roi = (skeleton * roi)
out = creVBMs.compute_central_retinal_equivalents(segmentation_roi.copy(), skeleton_roi.copy(),center[0],center[1], radius, artery = False, Toplot = True )
crvek, crveh = out["crvek"], out["crveh"]

AVR_h = craeh/crveh
AVR_k = craek/crvek

print(f"CRAE_H: {craeh}, CRAE_K: {craek},CRVE_H: {crveh}, CRVE_K: {crvek}, AVR_H: {AVR_h}, AVR_K: {AVR_k} ")
```



### Artery/Veins Blood Vessel Segmentation Datasets

You can access the external test set used in the LUNet paper directly from PVBM:
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import os
import requests
import onnxruntime as ort
import numpy as np
import PIL
from PIL import Image
from torchvision import transforms
import os
import cv2

class DiscSegmenter:
Expand All @@ -14,6 +15,22 @@ def __init__(self):
self.img_size = 512
script_dir = os.path.dirname(os.path.abspath(__file__))
self.model_path = os.path.join(script_dir, "lunetv2_odc.onnx")
self.download_model()

def download_model(self):
"""Download the ONNX model if it does not exist."""
model_url = 'https://github.com/aim-lab/PVBM/raw/main/PVBM/lunetv2_odc.onnx'
print(f"Model path: {self.model_path}")
if not os.path.exists(self.model_path):
print(f"Downloading model from {model_url}...")
response = requests.get(model_url, stream=True)
response.raise_for_status()
with open(self.model_path, 'wb') as f:
for chunk in response.iter_content(chunk_size=8192):
f.write(chunk)
print(f'Model downloaded to {self.model_path}')
else:
print("Model already exists, skipping download.")

def find_biggest_contour(self, contours):
"""
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# Sphinx build info version 1
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
config: 2649240384b73af9860f06d91bf8c9db
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Datasets &mdash; PVBM 2.9.5 documentation</title>
<title>Datasets &mdash; PVBM 2.9.7 documentation</title>
<link rel="stylesheet" href="_static/pygments.css" type="text/css" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>DiscSegmenter &mdash; PVBM 2.9.7 documentation</title>
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<meta charset="utf-8" />
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<title>PVBM.DiscSegmenter &mdash; PVBM 2.9.5 documentation</title>
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<meta charset="utf-8" />
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<title>PVBM.GeometricalAnalysis &mdash; PVBM 2.9.5 documentation</title>
<title>PVBM.GeometricalAnalysis &mdash; PVBM 2.9.7 documentation</title>
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<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>PVBM.GeometryAnalysis &mdash; PVBM 2.9.5 documentation</title>
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<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Overview: module code &mdash; PVBM 2.9.5 documentation</title>
<title>Overview: module code &mdash; PVBM 2.9.7 documentation</title>
<link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
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Expand Up @@ -89,9 +89,82 @@ We have included an optic disc segmenter to perform more accurate VBM estimation
segmenter = DiscSegmenter()
# Define the segmentation path and replace specific parts of the path
segmentation_path = '../PVBM_datasets/INSPIRE/artery/image13.png'
segmentation_path = '../PVBM_datasets/INSPIRE/images/image13.png'
optic_disc = segmenter.segment(str(segmentation_path))
Geometrical VBMs
=================

.. code-block:: python
### First run the optic disc segmentation snippet to extract center, radius, roi, zones_ABC
from PVBM.GeometryAnalysis import GeometricalVBMs #Import the geometry analysis module
#Preprocessing and roi extraction
blood_vessel_segmentation_path = '../PVBM_datasets/INSPIRE/artery/image13.png'
segmentation = np.array(Image.open(blood_vessel_segmentation_path))/255 #Open the segmentation
skeleton = skeletonize(segmentation)*1
segmentation_roi = segmentation * (1-zones_ABC[:,:,1]/255)
segmentation_roi = segmentation_roi * roi[:,:,1]/255
skeleton_roi = skeleton * (1-zones_ABC[:,:,1]/255)
skeleton_roi = skeleton_roi * roi[:,:,1]/255
geometricalVBMs = GeometricalVBMs() #Instanciate a geometrical VBM object
vbms, visual = geometricalVBMs.compute_geomVBMs(segmentation_roi, skeleton_roi, center[0], center[1], radius)
area, TI, medTor, ovlen, medianba, startp, endp, interp = vbms
Fractal Analysis
=================

.. code-block:: python
### First run the optic disc segmentation snippet to extract center, radius, roi, zones_ABC
from PVBM.FractalAnalysis import MultifractalVBMs
#Preprocessing and roi extraction
blood_vessel_segmentation_path = '../PVBM_datasets/INSPIRE/artery/image13.png'
segmentation = np.array(Image.open(blood_vessel_segmentation_path))/255 #Open the segmentation
segmentation_roi = segmentation * (1-zones_ABC[:,:,1]/255)
segmentation_roi = segmentation_roi * roi[:,:,1]/255
fractalVBMs = MultifractalVBMs(n_rotations = 25,optimize = True, min_proba = 0.0001, maxproba = 0.9999)
D0,D1,D2,SL = fractalVBMs.compute_multifractals(segmentation_roi.copy())
Central Retinal Equivalent Analysis
===================================
.. code-block:: python
### First run the optic disc segmentation snippet to extract center, radius, roi, zones_ABC
from PVBM.CentralRetinalAnalysis import CREVBMs
#Preprocessing and roi extraction
zone_A_ = zones_ABC[:,:,1]/255
zone_B_ = zones_ABC[:,:,0]/255
zone_C_ = zones_ABC[:,:,2]/255
roi = (zone_C_ - zone_B_)
creVBMs = CREVBMs()
####Artery
blood_vessel_segmentation_path = '../PVBM_datasets/INSPIRE/artery/image13.png'
segmentation = np.array(Image.open(blood_vessel_segmentation_path))/255 #Open the segmentation
skeleton = skeletonize(segmentation)*1
segmentation_roi = (segmentation * roi)
skeleton_roi = (skeleton * roi)
out = creVBMs.compute_central_retinal_equivalents(segmentation_roi.copy(), skeleton_roi.copy(),center[0],center[1], radius, artery = True, Toplot = True )
craek, craeh = out["craek"], out["craeh"]
####Veins
blood_vessel_segmentation_path = '../PVBM_datasets/INSPIRE/veins/image13.png'
segmentation = np.array(Image.open(blood_vessel_segmentation_path))/255 #Open the segmentation
skeleton = skeletonize(segmentation)*1
segmentation_roi = (segmentation * roi)
skeleton_roi = (skeleton * roi)
out = creVBMs.compute_central_retinal_equivalents(segmentation_roi.copy(), skeleton_roi.copy(),center[0],center[1], radius, artery = False, Toplot = True )
crvek, crveh = out["crvek"], out["crveh"]
AVR_h = craeh/crveh
AVR_k = craek/crvek
print(f"CRAE_H: {craeh}, CRAE_K: {craek},CRVE_H: {crveh}, CRVE_K: {crvek}, AVR_H: {AVR_h}, AVR_K: {AVR_k} ")
Artery/Veins Blood Vessel Segmentation Datasets
===============================================

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var DOCUMENTATION_OPTIONS = {
URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
VERSION: '2.9.5',
VERSION: '2.9.7',
LANGUAGE: 'en',
COLLAPSE_INDEX: false,
BUILDER: 'html',
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<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Index &mdash; PVBM 2.9.5 documentation</title>
<title>Index &mdash; PVBM 2.9.7 documentation</title>
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<meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />

<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>PVBM: A Python Vasculature Biomarker Toolbox Based on Retinal Blood Vessel Segmentation &mdash; PVBM 2.9.5 documentation</title>
<title>PVBM: A Python Vasculature Biomarker Toolbox Based on Retinal Blood Vessel Segmentation &mdash; PVBM 2.9.7 documentation</title>
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Expand Down Expand Up @@ -130,11 +130,20 @@ <h2>Optic Disc Segmentation<a class="headerlink" href="#optic-disc-segmentation"
<span class="n">segmenter</span> <span class="o">=</span> <span class="n">DiscSegmenter</span><span class="p">()</span>

<span class="c1"># Define the segmentation path and replace specific parts of the path</span>
<span class="n">segmentation_path</span> <span class="o">=</span> <span class="s1">&#39;../PVBM_datasets/INSPIRE/artery/image13.png&#39;</span>
<span class="n">segmentation_path</span> <span class="o">=</span> <span class="s1">&#39;../PVBM_datasets/INSPIRE/images/image13.png&#39;</span>
<span class="n">optic_disc</span> <span class="o">=</span> <span class="n">segmenter</span><span class="o">.</span><span class="n">segment</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">segmentation_path</span><span class="p">))</span>
</pre></div>
</div>
</section>
<section id="geometrical-vbms">
<h2>Geometrical VBMs<a class="headerlink" href="#geometrical-vbms" title="Permalink to this heading"></a></h2>
</section>
<section id="fractal-analysis">
<h2>Fractal Analysis<a class="headerlink" href="#fractal-analysis" title="Permalink to this heading"></a></h2>
</section>
<section id="central-retinal-equivalent-analysis">
<h2>Central Retinal Equivalent Analysis<a class="headerlink" href="#central-retinal-equivalent-analysis" title="Permalink to this heading"></a></h2>
</section>
<section id="artery-veins-blood-vessel-segmentation-datasets">
<h2>Artery/Veins Blood Vessel Segmentation Datasets<a class="headerlink" href="#artery-veins-blood-vessel-segmentation-datasets" title="Permalink to this heading"></a></h2>
<p>You can access the external test set used in the LUNet paper directly from PVBM:
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<title>Python Module Index &mdash; PVBM 2.9.5 documentation</title>
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<link rel="stylesheet" href="_static/custom.css" type="text/css" />
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2 changes: 1 addition & 1 deletion docs/_build/html/searchindex.js

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2 changes: 1 addition & 1 deletion docs/conf.py
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project = 'PVBM'
copyright = '2023, JF'
author = 'JF'
release = '2.9.5'
release = '2.9.7'

# -- General configuration ---------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
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