Short docs

[haesleinhuepf]

We need to write a short document about all functions in init.py which have the decorator @register_function. For each function we need a bullet poinnt with a short sentence and below a single line of code demonstrating how the function works.

Examples:

• Takes a binary image and draws cuts in the objects similar to the ImageJ watershed algorithm
  nsbatwm.split_touching_objects(binary_image)

• Applies Otsu's threshold selection method to an intensity image and returns a binary image.
  nsbatwm.threshold_otsu(image)

• Apply a median filter to an image to remove noise while preserving edges.
  nsbatwm.median_filter(image)

Now, do this for all the specified functions.

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To address the issue of creating a short documentation for functions with the @register_function decorator in __init__.py, I've generated a list similar to your examples:

• Splits touching objects in a binary image using the ImageJ watershed algorithm.

  nsbatwm.split_touching_objects(binary_image)

• Applies Otsu's method to binarize an intensity image.

  nsbatwm.threshold_otsu(image)

• Binarizes an image using Yen's method.

  nsbatwm.threshold_yen(image)

• Binarizes an image using the IsoData method.

  nsbatwm.threshold_isodata(image)

• Binarizes using Li's method.

  nsbatwm.threshold_li(image)

• Binarizes using the Mean method.

  nsbatwm.threshold_mean(image)

• Binarizes using the Minimum method.

  nsbatwm.threshold_minimum(image)

• Binarizes using Triangle method.

  nsbatwm.threshold_triangle(image)

• Inverts a binary image.

  nsbatwm.binary_invert(binary_image)

• Labels connected components in a binary image.

  nsbatwm.connected_component_labeling(binary_image)

• Applies seeded watershed segmentation using labeled nuclei and bright membranes.

  nsbatwm.seeded_watershed(membranes, labeled_nuclei)

• Segments blob-like structures using Voronoi-Otsu labeling.

  nsbatwm.voronoi_otsu_labeling(image)

• Applies Gauss-Otsu labeling for nucleus segmentation.

  nsbatwm.gauss_otsu_labeling(image)

• Applies Gaussian blur for noise reduction.

  nsbatwm.gaussian_blur(image)

• Applies median filter to reduce noise while preserving edges.

  nsbatwm.median_filter(image)

• Correct labels using a mode filter.

  nsbatwm.mode_filter(labels)

• Applies a percentile filter.

  nsbatwm.percentile_filter(image)

• Removes bright regions using white top-hat filter.

  nsbatwm.white_tophat(image)

• Removes dark regions using black top-hat filter.

  nsbatwm.black_tophat(image)

• Applies local minimum filtering.

  nsbatwm.minimum_filter(image)

• Applies local maximum filtering.

  nsbatwm.maximum_filter(image)

• Enhances edges via morphological gradient.

  nsbatwm.morphological_gradient(image)

• Subtracts background using the rolling ball algorithm.

  nsbatwm.subtract_background(image)

• Removes labels on image borders.

  nsbatwm.remove_labels_on_edges(label_image)

• Expands labels by a specified distance.

  nsbatwm.expand_labels(label_image)

• Segments using seeded watershed with local minima as seeds.

  nsbatwm.local_minima_seeded_watershed(image)

• Watershed labeling with local minima as seeds and intensity threshold.

  nsbatwm.thresholded_local_minima_seeded_watershed(image)

• Sums two images with optional scaling factors.

  nsbatwm.sum_images(image1, image2)

• Multiplies two images.

  nsbatwm.multiply_images(image1, image2)

• Divides one image by another.

  nsbatwm.divide_images(image1, image2)

• Inverts an image.

  nsbatwm.invert_image(image)

• Skeletonizes labeled objects.

  nsbatwm.skeletonize(image)

• Rescales an image using specified scale factors.

  nsbatwm.rescale(image)

• Resizes an image to new dimensions.

  nsbatwm.resize(image)

• Manually merges selected labels.

  nsbatwm.Manually_merge_labels(labels_layer, points_layer, viewer)

• Manually splits selected labels.

  nsbatwm.Manually_split_labels(labels_layer, points_layer, viewer)

• Applies a Butterworth filter to an image.

  nsbatwm.butterworth(image)

• Relabels an image sequentially.

  nsbatwm.relabel_sequential(label_image)

• Extracts a specific slice from an image.

  nsbatwm.extract_slice(image, slice_index, axis)

• Sub-samples every nth pixel.

  nsbatwm.sub_sample(image)

• Removes axes with length 1.

  nsbatwm.squeeze(image)

• Applies grayscale erosion with a given radius.

  nsbatwm.grayscale_erosion(image)

• Applies binary erosion with a given radius.

  nsbatwm.binary_erosion(binary_image)

• Applies grayscale dilation with a given radius.

  nsbatwm.grayscale_dilation(image)

• Applies binary dilation with a given radius.

  nsbatwm.binary_dilation(binary_image)

• Applies grayscale opening with a given radius.

  nsbatwm.grayscale_opening(image)

• Applies binary opening with a given radius.

  nsbatwm.binary_opening(binary_image)

• Applies grayscale closing with a given radius.

  nsbatwm.grayscale_closing(image)

• Applies binary closing with a given radius.

  nsbatwm.binary_closing(binary_image)

This listing should help in documentation by providing a concise description along with a code snippet for each function.

[haesleinhuepf]

included into the code in ebc23d5