Module org.scijava.ops.image

Package org.scijava.ops.image.threshold.minimum

Class ComputeMinimumThreshold<T extends RealType<T>>

java.lang.Object

org.scijava.ops.image.threshold.AbstractComputeThresholdHistogram<T>

org.scijava.ops.image.threshold.minimum.ComputeMinimumThreshold<T>

All Implemented Interfaces:

BiConsumer<Histogram1d<T>,T>, Computers.Arity1<Histogram1d<T>,T>

public class ComputeMinimumThreshold<T extends RealType<T>>
extends AbstractComputeThresholdHistogram<T>

Implements a minimum threshold method by Prewitt & Mendelsohn.

Author:

Barry DeZonia, Gabriel Landini

Implementation Note:

op names='threshold.minimum', priority='100.'

Constructor Summary

Constructors

Constructor	Description
ComputeMinimumThreshold()

Method Summary

Modifier and Type	Method	Description
static long	computeBin(long[] histogram)	J.
long	computeBin(Histogram1d<T> hist)	TODO

Methods inherited from class org.scijava.ops.image.threshold.AbstractComputeThresholdHistogram

compute

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface java.util.function.BiConsumer

andThen

Methods inherited from interface org.scijava.function.Computers.Arity1

accept

Constructor Detail

ComputeMinimumThreshold

public ComputeMinimumThreshold()

Method Detail

computeBin

public long computeBin(Histogram1d<T> hist)

TODO

Specified by:

computeBin in class AbstractComputeThresholdHistogram<T extends RealType<T>>

Parameters:

hist - the Histogram1d

Returns:

the Minimum threshold value

computeBin

public static long computeBin(long[] histogram)

J. M. S. Prewitt and M. L. Mendelsohn, "The analysis of cell images,"
in
Annals of the New York Academy of Sciences, vol. 128, pp. 1035-1053,
1966.
ported to ImageJ plugin by G.Landini from Antti Niemisto's Matlab
code
(relicensed BSD 2-12-13)
Original Matlab code Copyright (C) 2004 Antti Niemisto
See http://www.cs.tut.fi/~ant/histthresh/ for an excellent slide
presentation and the original Matlab code.
//
Assumes a bimodal histogram. The histogram needs is smoothed (using a
running average of size 3, iteratively) until there are only two
local
maxima.
Threshold t is such that yt−1 > yt ≤ yt+1.
Images with histograms having extremely unequal peaks or a broad and
??at valley are unsuitable for this method.