Source Code¶

On my GitHub This code is wrapped in a class which makes it harder to post to a Notebook (a few too many lines for a post).

Segmenting Fundus Images with kNN¶

After preprocessing the images as described in a previous post, I tried to extract learnable features by applying the k-nearest neighbors algorithm. In order to seed the cluster I have applied my (immense) domain knowledge in the area of retinology. I used the annotations tool that comes with OpenCV. It resides in the applications folder of the OpenCV project created with CMake and built with Visual Studio 2013.

I ran the tool on a small set of images and selected the following regions (to the best of my knowledge acquired after reading articles on retinopathy and watching slides). This produces a text file where each line has the following format:

annotations\1196_right.jpeg $\color{green}{10\ 1318\ 182\ 22}\color{red}{\ 12\ 1356\ 195\ 8}$…

Here after the name of the file are the coordinates of annotated rectangles, where each rectangle is a 4-tuple: $(x, y, \Delta x, \Delta y)$, where $(x, y)$ are the coordinates of the left top corner and the rest are lenghts of the sides of the rectangle.

I have annotated 10 rectangles in the following order:

0 – 1: drusen/exudates
2 – 3: texture
4 – 5: Camera effects
6 – 7: Haemorages/blood vessels
8 – 9: Optic Disc

and did these annotations for 7 images selected at random

I used k-nearest neighbors algorithm to segment each image:

In [17]:

%matplotlib inline
import matplotlib.pylab as plt
%run "../regions_detect_knn.py"

In [18]:

# path to the images preprocessed by histogram specification from 16_left.jpeg
root = '/kaggle/retina/train/labelled' # root of a tree, where each subdirectory is a class label of the image (0 - 4)
annotations = 'annotations.txt' # annotations file
masks_dir = '/kaggle/retina/train/masks' # masks produced during preprocessing, identifying the eye
im_file = '4/16_left.jpeg' # preprocessed image

In [19]:

knn = KNeighborsRegions(root, im_file, annotations, masks_dir, n_neighbors = 3)
show_images([knn._image])

In order to seed the clusters, I read the annotatations from all ten regions of the seven files, and for each region stacked them together, averaged the images and then averaged the resulting image. I thus obtained a 1×1 seed for each of the 5 classes I wanted to learn, 2 seeds for each class (i.e., in the current scheme of things, two neighbors vote for the same class) :

In [20]:

# these functions are not called directly by users of the class
knn._rects = np.array([])
knn._avg_pixels = np.array([])
knn._process_annotations()
knn._get_initial_classes()
# this is what our seeds look like
knn.display_average_pixels()

(These do look like swatches of fabric or paint samples…)

Now we simply apply the KNeighborsClassifier() from scikit-learn to learn our classes:

In [21]:

# first need to set the class labels
knn.labels = [Labels.Drusen, Labels.Drusen, Labels.Background, Labels.Background, 
                    Labels.CameraHue, Labels.CameraHue, 
                    Labels.Haemorage, Labels.Haemorage, Labels.OD, Labels.OD]
segments = knn.analyze_image()

In [24]:

# segmented image
plt.imshow(segments)
plt.show()

Let’s see how we did on regions of interest (drusen, haemorages):

In [26]:

knn.display_artifact(segments, Labels.Drusen, (0, 255, 0), "Drusen")
knn.display_artifact(segments, Labels.Haemorage, (0, 0, 255), "Haemorages")