image_processing/brain_segmentation.cpp

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image_processing/brain_segmentation.cpp
/*******************************************************
* Copyright (c) 2014, ArrayFire
* All rights reserved.
*
* This file is distributed under 3-clause BSD license.
* The complete license agreement can be obtained at:
* http://arrayfire.com/licenses/BSD-3-Clause
********************************************************/
#include <string.h>
#include <stdio.h>
#include <math.h>
#include <arrayfire.h>
#include "../common/progress.h"
using namespace af;
const float h_sx_kernel[] = { 1, 2, 1,
0, 0, 0,
-1, -2, -1
};
const float h_sy_kernel[] = { -1, 0, 1,
-2, 0, 2,
-1, 0, 1
};
const float h_lp_kernel[] = { -0.5f, -1.0f, -0.5f,
-1.0f, 6.0f, -1.0f,
-0.5f, -1.0f, -0.5f
};
array edges_slice(array x)
{
array ret;
static array kernelx = array(dim4(3, 3), h_sx_kernel);
static array kernely = array(dim4(3, 3), h_sy_kernel);
ret = convolve(x, kernelx) + convolve(x, kernely);
return abs(ret);
}
array gauss(array x, float u, float s)
{
double f = 1 / sqrt(2 * af::Pi * s * s);
array e = exp(-pow((x - u), 2) / (2 * s * s));
return f * e;
}
array segment_volume(array A, int k)
{
array I1 = A(span, span, k);
float mx = max<float>(I1);
float mn = min<float>(I1);
float u0 = 0.9 * mx;
float s0 = (mx - mn) / 2;
float u1 = 1.1 * mn;
float s1 = (mx - mn) / 2;
array L0 = gauss(I1, u0, s0);
array L11 = gauss(I1, u1, s1);
array L10;
array L12;
static array kernel = constant(1, 3, 3) / 9;
static array L11_old;
static array L12_old;
if (k == 0) {
L11 = convolve(L11, kernel);
L10 = L11;
} else {
L10 = L11_old;
L11 = L12_old;
}
if (k < A. dims(2) - 1) {
L12 = gauss(A(span, span, k + 1), u1, s1);
L12 = convolve(L12, kernel);
} else {
L12 = L11;
}
L11_old = L11;
L12_old = L12;
array L1 = (L10 + L11 + L12) / 3;
array S = (L0 > L1);
return S. as(A. type());
}
void brain_seg(bool console)
{
af::Window wnd("Brain Segmentation Demo");
wnd.setColorMap(AF_COLORMAP_HEAT);
double time_total = 30; // run for N seconds
array B = loadImage(ASSETS_DIR "/examples/images/brain.png");
int slices = 256;
B = moddims(B, dim4(B.dims(0), B.dims(1)/slices, slices));
af::sync();
int N = 2 * slices - 1;
timer t = timer::start();
int iter = 0;
/* loop forward and backward for 100 frames
* exit if the user presses escape or the animation
* ends
*/
for (int i = 0; !wnd.close(); i++) {
iter++;
int j = i % N;
int k = std::min(j, N - j);
array Bi = B(span, span, k);
/* process */
array Si = segment_volume(B, k);
array Ei = edges_slice(Si);
array Mi = meanShift(Bi, 10, 10, 5);
/* visualization */
if (!console) {
wnd.grid(2, 2);
wnd(0, 0).image(Bi/255.f, "Input");
wnd(1, 0).image(Ei, "Edges");
wnd(0, 1).image(Mi/255.f, "Meanshift");
wnd(1, 1).image(Si, "Segmented");
wnd.show();
} else {
/* sync the operations so that current
* iteration comptation finishes
* */
af::sync();
}
/* we have had ran throuh simlation results
* exit the rendering loop */
if (!progress(iter, t, time_total))
break;
if (!(i<100*N))
break;
}
}
int main(int argc, char* argv[])
{
int device = argc > 1 ? atoi(argv[1]) : 0;
bool console = argc > 2 ? argv[2][0] == '-' : false;
try {
af::setDevice(device);
af::info();
printf("Brain segmentation example\n");
brain_seg(console);
} catch (af::exception& e) {
fprintf(stderr, "%s\n", e. what());
}
return 0;
}