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/*LICENSE_START*/
/*
* Copyright 1995-2002 Washington University School of Medicine
*
* http://brainmap.wustl.edu
*
* This file is part of CARET.
*
* CARET is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* CARET is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with CARET; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*
*/
/*LICENSE_END*/
#include <cmath>
#include <iostream>
#include "BrainModelSurface.h"
#include "BrainModelSurfaceMetricFullWidthHalfMaximum.h"
#include "CoordinateFile.h"
#include "MathUtilities.h"
#include "MetricFile.h"
#include "StatisticMeanAndDeviation.h"
#include "TopologyFile.h"
#include "TopologyHelper.h"
/**
* constructor.
*/
BrainModelSurfaceMetricFullWidthHalfMaximum::BrainModelSurfaceMetricFullWidthHalfMaximum(
BrainSet* bs,
BrainModelSurface* brainModelSurfaceIn,
MetricFile* metricFileIn,
const int metricColumnIn)
: BrainModelAlgorithm(bs)
{
fullWidthHalfMaximum = 0.0;
brainModelSurface = brainModelSurfaceIn;
metricFile = metricFileIn;
metricColumn = metricColumnIn;
}
/**
* destructor.
*/
BrainModelSurfaceMetricFullWidthHalfMaximum::~BrainModelSurfaceMetricFullWidthHalfMaximum()
{
}
/**
* Execute the algorithm.
*
* This algorithm is from:
* Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data
* Donald J. Hagler Jr., Ayse Pinar Saygin, and Martin I. Sereno
* NeuroImage 33 (2006) 1093-1103
*
* Full Width Half Maximum (FWHM) is computed using formula 2
* on page 1094 of the above paper.
*/
void
BrainModelSurfaceMetricFullWidthHalfMaximum::execute() throw (BrainModelAlgorithmException)
{
fullWidthHalfMaximum = 0.0;
//
// Check inputs
//
if (brainModelSurface == NULL) {
throw BrainModelAlgorithmException("Surface is NULL.");
}
if (metricFile == NULL) {
throw BrainModelAlgorithmException("Surface is NULL.");
}
const int numNodes = brainModelSurface->getNumberOfNodes();
if (numNodes <= 0) {
throw BrainModelAlgorithmException("Surface contains no nodes.");
}
if (metricFile->getNumberOfNodes() != numNodes) {
throw BrainModelAlgorithmException("Surface and metric file contain a different number of nodes.");
}
if ((metricColumn < 0) ||
(metricColumn >= metricFile->getNumberOfColumns())) {
throw BrainModelAlgorithmException("Metric column is invalid.");
}
//
// Get topology file and topology helper
//
const TopologyFile* tf = brainModelSurface->getTopologyFile();
if (tf == NULL) {
throw BrainModelAlgorithmException("Surface has no topology.");
}
const TopologyHelper* th = tf->getTopologyHelper(false, true, false);
//
// Get distance between each node and its neighbors
// and the metric differences
// USE ABSOLUTE VALUE FOR METRIC DIFFERENCES???
//
const CoordinateFile*cf = brainModelSurface->getCoordinateFile();
std::vector<float> nodeToNeighborEuclideanDistances;
std::vector<float> nodeToNeighborMetricDifferences;
std::vector<float> nodeMetricValues;
for (int myNodeNumber = 0; myNodeNumber < numNodes; myNodeNumber++) {
int numNeighbors = 0;
const int* neighbors = th->getNodeNeighbors(myNodeNumber, numNeighbors);
//
// Node is connected??
//
if (numNeighbors > 0) {
const float* myXYZ = cf->getCoordinate(myNodeNumber);
const float myMetric = metricFile->getValue(myNodeNumber, metricColumn);
nodeMetricValues.push_back(myMetric);
for (int neighborIndex = 0; neighborIndex < numNeighbors; neighborIndex++) {
const int neighborNodeNumber = neighbors[neighborIndex];
if (myNodeNumber < neighborNodeNumber) { // avoid counting distance twice
//
// Inter neighbor distances
//
nodeToNeighborEuclideanDistances.push_back(
MathUtilities::distance3D(myXYZ,
cf->getCoordinate(neighborNodeNumber)));
//
// Inter neighbor metric differences
//
float metricDiff = myMetric - metricFile->getValue(neighborNodeNumber,
metricColumn);
//metricDiff = std::fabs(metricDiff);
nodeToNeighborMetricDifferences.push_back(metricDiff);
}
}
}
}
//
// Mean and Sample Variance for node to neighbor euclidean distances
//
StatisticMeanAndDeviation interNeighborMeanAndDeviation;
interNeighborMeanAndDeviation.addDataArray(&nodeToNeighborEuclideanDistances[0],
static_cast<int>(nodeToNeighborEuclideanDistances.size()));
try {
interNeighborMeanAndDeviation.execute();
}
catch (StatisticException& e) {
throw BrainModelAlgorithmException(e);
}
//
// "dv" is average neighbor inter-distance
//
const double dv = interNeighborMeanAndDeviation.getMean();
//
// Mean and Sample Variance for node to neighbor metric differences
//
StatisticMeanAndDeviation metricDifferenceMeanAndDeviation;
metricDifferenceMeanAndDeviation.addDataArray(&nodeToNeighborMetricDifferences[0],
static_cast<int>(nodeToNeighborMetricDifferences.size()));
try {
metricDifferenceMeanAndDeviation.execute();
}
catch (StatisticException& e) {
throw BrainModelAlgorithmException(e);
}
//
// "varDS" is the variance of the metric inter-neighbor differences
//
const double varDS = metricDifferenceMeanAndDeviation.getPopulationSampleVariance();
//
// Sample Variance for node metric values
//
StatisticMeanAndDeviation metricMeanAndDeviation;
metricMeanAndDeviation.addDataArray(&nodeMetricValues[0],
static_cast<int>(nodeMetricValues.size()));
try {
metricMeanAndDeviation.execute();
}
catch (StatisticException& e) {
throw BrainModelAlgorithmException(e);
}
//
// "varS" is the variance of the all nodes' metric values
//
const double varS = metricMeanAndDeviation.getPopulationSampleVariance();
//
// Equation from code MRISgaussFWHM
// Same results as code that duplicates equation in paper
//
/*
if (varS != 0) {
double varratio = -std::log(1.0 - 0.5 * (varDS / varS));
if (varratio <= 0.0) {
varratio = 0.5 * (varDS / varS);
}
if (varratio <= 0.0) {
fullWidthHalfMaximum = 0.0;
}
else {
fullWidthHalfMaximum = std::sqrt(2.0 * std::log(2) / varratio) * dv;
}
}
*/
//
// Calculate FWHM
// Exactly as in equation in paper
//
if (varS != 0.0) {
const double denom = std::log((double)(1.0 - (varDS / (2.0 * varS))));
if (denom != 0.0) {
const double val = (-2.0 * std::log(2.0)) / denom;
if (val >= 0.0) {
fullWidthHalfMaximum = dv * std::sqrt(val);
//std::cout << "Paper Equation: " << XXXfullWidthHalfMaximum
// << " Hagler Code: " << fullWidthHalfMaximum << std::endl;
}
}
}
}
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