OtsuThresholdSegment

OtsuThresholdSegment computes a global threshold with skimage.filters.threshold_otsu, creates a foreground mask, and labels connected components.

Inputs are input_image and above. Outputs are labels, object_count, and the computed threshold.

Minimal Example

from bioimageflow_core import Arguments
from bioimageflow_segmentation_tools import OtsuThresholdSegment

result = OtsuThresholdSegment().process_row(
    Arguments(input_image="image.tif", labels="labels.tif", above=True)
)

Inputs

• input_image: 2D intensity image.

• above: when true, pixels above the Otsu threshold are foreground.

Outputs

• labels: connected-component label image.

• object_count: number of foreground components.

• threshold: computed Otsu threshold.

Dependencies and Core Libraries

imageio, NumPy, and scikit-image thresholding/labeling functions.

Assumptions

The image has a bimodal enough intensity distribution for global Otsu threshold to be meaningful. Background is label 0.

Expected Results

Synthetic bright objects on a darker background produce the expected number of connected labels and an Otsu threshold matching skimage.

Failure Modes

Unreadable images, unsupported dimensions, and write failures stop execution.