# Otsu Segmentation

Segment an image using Otsu’s method for automated threshold detection. The threshold is selected to maximize the inter-region variance and minimize the intra-region variance. Returns optimal threshold values and binary image maps.

## Input Parameters

#### Images

Arbitrary bit-depth images with any number of channels.

Type: Image File (`net.imagej.Dataset`)

## Results

#### Channel

The channel index. Each channel of each source image is processed separately.

Type: Integer (`int`)

#### Threshold

The optimal threshold value which maximizes inter-class variance:

ω_{a}(t) ω_{b}(t) [μ_{a}(t) - μ_{b}(t)]^{2}

Here, `t` is the threshold value, `ω`_{a}(t) and `ω`_{b}(t) are the number of pixels in each class, and `μ`_{a}(t) and `μ`_{b}(t) are the mean pixel values of each class.

Type: Numeric (`double`)

#### Threshold Map

Binary images of each image channel segmented according to the computed optimal threshold value.

Type: Image File (`net.imagej.Dataset`)

## Reference

Nobuyuki Otsu (1979). “A threshold selection method from gray-level histograms”. IEEE Trans. Sys., Man., Cyber. 9 (1): 62–66. doi:10.1109/TSMC.1979.4310076