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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 <algorithm>
#include <iostream>
#include <limits>
#include <QDateTime>
#include "BrainModelSurface.h"
#include "BrainModelSurfaceConnectedSearchMetric.h"
#include "BrainModelSurfaceMetricClustering.h"
#include "DebugControl.h"
#include "MathUtilities.h"
#include "MetricFile.h"
#include "TopologyFile.h"
#include "TopologyHelper.h"
#include "ValueIndexSort.h"
/**
* Constructor.
*/
BrainModelSurfaceMetricClustering::BrainModelSurfaceMetricClustering(BrainSet* bs,
const BrainModelSurface* bmsIn,
MetricFile* metricFileIn,
const CLUSTER_ALGORITHM algorithmIn,
const int inputColumnIn,
const int outputColumnIn,
const QString& outputColumnNameIn,
const int minimumNumberOfNodesIn,
const float minimumSurfaceAreaIn,
const float clusterNegativeMinimumThresholdIn,
const float clusterNegativeMaximumThresholdIn,
const float clusterPositiveMinimumThresholdIn,
const float clusterPositiveMaximumThresholdIn,
const bool outputAllClustersFlagIn)
: BrainModelAlgorithm(bs),
bms(bmsIn),
metricFile(metricFileIn),
algorithm (algorithmIn),
inputColumn(inputColumnIn),
outputColumn(outputColumnIn),
outputColumnName(outputColumnNameIn),
minimumNumberOfNodes(minimumNumberOfNodesIn),
minimumSurfaceArea(minimumSurfaceAreaIn),
clusterNegativeMinimumThreshold(clusterNegativeMinimumThresholdIn),
clusterNegativeMaximumThreshold(clusterNegativeMaximumThresholdIn),
clusterPositiveMinimumThreshold(clusterPositiveMinimumThresholdIn),
clusterPositiveMaximumThreshold(clusterPositiveMaximumThresholdIn),
outputAllClustersFlag(outputAllClustersFlagIn)
{
}
/**
* Destructor.
*/
BrainModelSurfaceMetricClustering::~BrainModelSurfaceMetricClustering()
{
clusters.clear();
}
/**
* execute the algorithm.
*/
void
BrainModelSurfaceMetricClustering::execute() throw (BrainModelAlgorithmException)
{
//
// Check for valid input column
//
if ((metricFile->getNumberOfColumns() <= 0) ||
(metricFile->getNumberOfNodes() <= 0)) {
throw BrainModelAlgorithmException("Metric file has no data");
}
if ((inputColumn < 0) || (inputColumn >= metricFile->getNumberOfColumns())) {
throw BrainModelAlgorithmException("Invalid input column number");
}
//
// Create a new column if needed.
//
if ((outputColumn < 0) || (outputColumn >= metricFile->getNumberOfColumns())){
metricFile->addColumns(1);
outputColumn = metricFile->getNumberOfColumns() - 1;
}
metricFile->setColumnName(outputColumn, outputColumnName);
//
// Copy the input column to the output column
//
if (inputColumn != outputColumn) {
std::vector<float> values;
metricFile->getColumnForAllNodes(inputColumn, values);
metricFile->setColumnForAllNodes(outputColumn, values);
}
//
// Node within threshold flags
//
const int numNodes = bms->getNumberOfNodes();
nodeWithinThresholds.resize(numNodes);
//
// Get a toplogy helper
//
const TopologyFile* tf = bms->getTopologyFile();
const TopologyHelper* th = tf->getTopologyHelper(false, true, false);
//
// Find nodes that are within thresholds
//
for (int i = 0; i < numNodes; i++) {
nodeWithinThresholds[i] = false;
if (th->getNodeHasNeighbors(i)) {
float v = metricFile->getValue(i, outputColumn);
if ((v >= clusterPositiveMinimumThreshold) &&
(v <= clusterPositiveMaximumThreshold)) {
nodeWithinThresholds[i] = true;
}
if ((v <= clusterNegativeMinimumThreshold) &&
(v >= clusterNegativeMaximumThreshold)) {
nodeWithinThresholds[i] = true;
}
}
}
switch (algorithm) {
case CLUSTER_ALGORITHM_NONE:
throw BrainModelAlgorithmException("Invalid clustering algorithm = NONE");
break;
case CLUSTER_ALGORITHM_ANY_SIZE:
break;
case CLUSTER_ALGORITHM_MINIMUM_NUMBER_OF_NODES:
case CLUSTER_ALGORITHM_MINIMUM_SURFACE_AREA:
//
// Find the clusters
// Note find clusters will make sure that a cluster contains just positive nodes
// or just negative nodes.
//
findClusters();
setClustersCenterOfGravityAndArea();
break;
}
//
// Nodes that are to remain after clustering
//
std::vector<bool> validClusterNodes(numNodes, false);
std::vector<Cluster> clustersOut;
const int numClusters = static_cast<int>(clusters.size());
//
// Determine which clusters are good
//
switch (algorithm) {
case CLUSTER_ALGORITHM_NONE:
break;
case CLUSTER_ALGORITHM_ANY_SIZE:
//
// Any nodes within the thresholds are in the cluster
//
for (int i = 0; i < numNodes; i++) {
validClusterNodes[i] = nodeWithinThresholds[i];
}
break;
case CLUSTER_ALGORITHM_MINIMUM_NUMBER_OF_NODES:
//
// Keep nodes that are in clusters with sufficient number of nodes
//
for (int i = 0; i < numClusters; i++) {
Cluster& c = clusters[i];
const int numNodesInCluster = c.getNumberOfNodesInCluster();
if (numNodesInCluster >= minimumNumberOfNodes) {
for (int j = 0; j < numNodesInCluster; j++) {
validClusterNodes[c.nodeIndices[j]] = true;
}
clustersOut.push_back(c);
}
else {
if (outputAllClustersFlag) {
clustersOut.push_back(c);
}
}
}
break;
case CLUSTER_ALGORITHM_MINIMUM_SURFACE_AREA:
//
// Keep nodes that are in clusters with minimum area
//
for (int i = 0; i < numClusters; i++) {
Cluster& c = clusters[i];
if (c.getArea() >= minimumSurfaceArea) {
const int numNodesInCluster = c.getNumberOfNodesInCluster();
for (int j = 0; j < numNodesInCluster; j++) {
validClusterNodes[c.nodeIndices[j]] = true;
}
clustersOut.push_back(c);
}
else {
if (outputAllClustersFlag) {
clustersOut.push_back(c);
}
}
}
break;
}
//
// output the clusters
//
clusters = clustersOut;
//
// Any nodes within the thresholds are in the cluster
//
for (int i = 0; i < numNodes; i++) {
if (validClusterNodes[i] == false) {
metricFile->setValue(i, outputColumn, 0.0);
}
}
}
/**
* Find the clusters
*/
void
BrainModelSurfaceMetricClustering::findClusters() throw (BrainModelAlgorithmException)
{
clusters.clear();
QTime timer;
timer.start();
//
// Search positive values
//
const int numNodes = metricFile->getNumberOfNodes();
for (int i = 0; i < numNodes; i++) {
if (nodeWithinThresholds[i]) {
float minVal = 0.0;
float maxVal = 0.0;
bool doIt = false;
if ((metricFile->getValue(i, outputColumn) >= clusterPositiveMinimumThreshold) &&
(metricFile->getValue(i, outputColumn) <= clusterPositiveMaximumThreshold)) {
minVal = clusterPositiveMinimumThreshold;
maxVal = clusterPositiveMaximumThreshold;
doIt = true;
}
else if ((metricFile->getValue(i, outputColumn) >= clusterNegativeMaximumThreshold) &&
(metricFile->getValue(i, outputColumn) <= clusterNegativeMinimumThreshold)) {
minVal = clusterNegativeMaximumThreshold;
maxVal = clusterNegativeMinimumThreshold;
doIt = true;
}
if (doIt) {
//
// allow other events to process
//
allowEventsToProcess();
BrainModelSurfaceConnectedSearchMetric bmscsm(brainSet,
bms,
i,
metricFile,
outputColumn,
minVal,
maxVal,
&nodeWithinThresholds);
try {
bmscsm.execute();
}
catch (BrainModelAlgorithmException& e) {
throw e;
}
//
// Create a new cluster
//
Cluster c(minVal, maxVal);
//
// See which nodes should be added to the cluster
//
for (int j = i; j < numNodes; j++) {
//
// Is node part of the cluster ?
//
if (bmscsm.getNodeConnected(j)) {
//
// add to the cluster
//
c.addNodeToCluster(j);
//
// do not need to look at this node again
//
nodeWithinThresholds[j] = false;
}
}
//
// If the cluster has nodes, add it to the clusters
//
if (c.getNumberOfNodesInCluster() > 0) {
clusters.push_back(c);
if (DebugControl::getDebugOn()) {
std::cout << "Cluster starting at node " << i
<< " contains " << c.getNumberOfNodesInCluster()
<< " nodes." << std::endl;
}
}
}
//
// This node no longer needs to be examined
//
nodeWithinThresholds[i] = false;
}
}
if (DebugControl::getDebugOn()) {
std::cout << "Time to find clusters: "
<< (static_cast<float>(timer.elapsed()) / 1000.0) << std::endl;
}
}
/**
* set clusters center of gravity and area.
*/
void
BrainModelSurfaceMetricClustering::setClustersCenterOfGravityAndArea() throw (BrainModelAlgorithmException)
{
const int numClusters = static_cast<int>(clusters.size());
if (numClusters > 0) {
//
// Get the area of all nodes
//
std::vector<float> nodeAreas;
bms->getAreaOfAllNodes(nodeAreas);
const CoordinateFile* cf = bms->getCoordinateFile();
//
// process each cluster
//
for (int i = 0; i < numClusters; i++) {
Cluster& c = clusters[i];
const int numNodesInCluster = c.getNumberOfNodesInCluster();
if (numNodesInCluster > 0) {
double area = 0.0;
double cogSum[3] = { 0.0, 0.0, 0.0 };
for (int j = 0; j < numNodesInCluster; j++) {
const int nodeNumber = c.nodeIndices[j];
area += nodeAreas[nodeNumber];
const float* xyz = cf->getCoordinate(nodeNumber);
cogSum[0] += xyz[0];
cogSum[1] += xyz[1];
cogSum[2] += xyz[2];
}
//
// Note: Area of nodes is just summed, no need to divide by zero
//
c.setArea(area);
const float cog[3] = {
cogSum[0] / static_cast<double>(numNodesInCluster),
cogSum[1] / static_cast<double>(numNodesInCluster),
cogSum[2] / static_cast<double>(numNodesInCluster)
};
c.setCenterOfGravity(cog);
}
}
}
}
/**
* get clusters indices sorted by number of nodes in cluster.
*/
void
BrainModelSurfaceMetricClustering::getClusterIndicesSortedByNumberOfNodesInCluster(std::vector<int>& indices) const
{
indices.clear();
//
// Sort the indices by number of nodes in clusters
//
ValueIndexSort vis;
const int num = getNumberOfClusters();
for (int i = 0; i < num; i++) {
const Cluster* c = getCluster(i);
vis.addValueIndexPair(i, c->getNumberOfNodesInCluster());
}
vis.sort();
//
// Set output indices
//
const int numItems = vis.getNumberOfItems();
for (int i = 0; i < numItems; i++) {
int indx;
float value;
vis.getValueAndIndex(i, indx, value);
indices.push_back(indx);
}
}
//*****************************************************************************************
//
// Cluster methods
//
/**
* Constructor
*/
BrainModelSurfaceMetricClustering::Cluster::Cluster(const float threshMinIn, const float threshMaxIn)
{
clusterArea = 0.0;
centerOfGravity[0] = 0.0;
centerOfGravity[1] = 0.0;
centerOfGravity[2] = 0.0;
threshMin = threshMinIn;
threshMax = threshMaxIn;
}
/**
* get thresholds.
*/
void
BrainModelSurfaceMetricClustering::Cluster::getThresholds(float& threshMinOut,
float& threshMaxOut) const
{
threshMinOut = threshMin;
threshMaxOut = threshMax;
}
/**
* get the center of gravity.
*/
void
BrainModelSurfaceMetricClustering::Cluster::getCenterOfGravity(float cog[3]) const
{
cog[0] = centerOfGravity[0];
cog[1] = centerOfGravity[1];
cog[2] = centerOfGravity[2];
}
/**
* set the center of gravity.
*/
void
BrainModelSurfaceMetricClustering::Cluster::setCenterOfGravity(const float cog[3])
{
centerOfGravity[0] = cog[0];
centerOfGravity[1] = cog[1];
centerOfGravity[2] = cog[2];
}
/**
* get the maximum Y-Value.
*/
float
BrainModelSurfaceMetricClustering::Cluster::getMaximumY(const BrainModelSurface* bms) const
{
float maxY = 0.0;
const int numClusterNodes = getNumberOfNodesInCluster();
if (numClusterNodes > 0) {
const CoordinateFile* cf = bms->getCoordinateFile();
maxY = -std::numeric_limits<float>::max();
for (int i = 0; i < numClusterNodes; i++) {
const float* xyz = cf->getCoordinate(nodeIndices[i]);
maxY = std::max(maxY, xyz[1]);
}
}
return maxY;
}
/**
* get the center of gravity using the surface (does not overwrite cluster's cog).
*/
void
BrainModelSurfaceMetricClustering::Cluster::getCenterOfGravityForSurface(const BrainModelSurface* bms,
float cog[3]) const
{
double cogSum[3] = { 0.0, 0.0, 0.0 };
const int numClusterNodes = getNumberOfNodesInCluster();
if (numClusterNodes > 0) {
const CoordinateFile* cf = bms->getCoordinateFile();
for (int i = 0; i < numClusterNodes; i++) {
const float* xyz = cf->getCoordinate(nodeIndices[i]);
cogSum[0] += xyz[0];
cogSum[1] += xyz[1];
cogSum[2] += xyz[2];
}
cogSum[0] /= static_cast<double>(numClusterNodes);
cogSum[1] /= static_cast<double>(numClusterNodes);
cogSum[2] /= static_cast<double>(numClusterNodes);
}
cog[0] = cogSum[0];
cog[1] = cogSum[1];
cog[2] = cogSum[2];
}
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