File: 2dseriessmoothgradMAD.cc

package info (click to toggle)
mia 2.2.2-1
  • links: PTS, VCS
  • area: main
  • in suites: jessie, jessie-kfreebsd
  • size: 13,532 kB
  • ctags: 16,800
  • sloc: cpp: 137,909; python: 1,057; ansic: 998; sh: 146; xml: 127; csh: 24; makefile: 13
file content (245 lines) | stat: -rw-r--r-- 6,859 bytes parent folder | download
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
/* -*- mia-c++  -*-
 *
 * This file is part of MIA - a toolbox for medical image analysis 
 * Copyright (c) Leipzig, Madrid 1999-2014 Gert Wollny
 *
 * MIA 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 3 of the License, or
 * (at your option) any later version.
 *
 * This program 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 MIA; if not, see <http://www.gnu.org/licenses/>.
 *
 */

#define VSTREAM_DOMAIN "SERGRADVAR"

#include <iostream>
#include <iomanip>
#include <string>
#include <limits>
#include <sstream>
#include <stdexcept>

#include <mia/internal/main.hh>
#include <mia/2d/filterchain.hh>
#include <mia/2d/imageio.hh>
#include <mia/2d/segsetwithimages.hh>
#include <mia/core.hh>

using namespace std;
using namespace mia;

const SProgramDescription g_description = {
	{pdi_group, "Tools for Myocardial Perfusion Analysis"}, 

	{pdi_short, "Evaluate pixel-wise time-intensity gradient of a 2D image series."}, 
	
	{pdi_description, "Given a set of images of temporal sucession, evaluate the temporal "
	 "pixel-wise gaussian and evaluate pixel-wise its MAD." 
	 "A spacial pre-filtering may be applied by specifying additional plugins "
	 "(filter/2dimage)"}, 
	
	{pdi_example_descr, "Evaluate the MAD-image of the bounding box surrounding the segmentation "
	 "from a series segment.set after applying a temporal Gaussian "
	 "filter of width 5. No spacial filtering will be applied. "
	 "The bounding box will be enlarged by 3 pixels in all directions. "
	 "Store the image in OpenEXR format."}, 
	 
	{pdi_example_code, "-i segment.set -o mad.exr -g 2 -c -e 3"}
};

template <typename T>
struct fabsdelta {
	T operator () (T x, T y) const {
		return fabs(x - y);
	}
};
struct C2DVarAccumulator : public TFilter<bool> {
	typedef vector<double> CBuffer;
	
	C2DVarAccumulator(size_t n, size_t gauss_width):
		m_min(numeric_limits<float>::max()),
		m_max(-numeric_limits<float>::max()),
		m_n(n - 1),
		m_initialized(false)	{
		stringstream gauss_descr;
		gauss_descr << "gauss:w=" << gauss_width;
		gauss = C1DSpacialKernelPluginHandler::instance().produce(gauss_descr.str().c_str());
		
	}
	
	template <typename T>
	bool operator ()(const T2DImage<T>& image){
		if (!m_initialized) {
			m_field.resize(image.size());
			m_size = image.get_size();
			m_initialized = true;
		}
		
		if (image.get_size() != m_size)
			throw invalid_argument("Input images are not all of same size");
		
		auto i = image.begin();
		auto e = image.end();
		auto v = m_field.begin();
		
		for(; i != e; ++i, ++v)
			v->push_back(*i);
		
		auto src_minmax = minmax_element(image.begin(), image.end());
		if (m_min > *src_minmax.first)
			m_min = *src_minmax.first;
		if (m_max < *src_minmax.second)
			m_max = *src_minmax.second;
		
		return true;
	}
	
	void evaluate_gradients(){
		auto v = m_field.begin();
		auto e = m_field.end();
		
		while ( v != e ) {
			gauss->apply_inplace(*v);
			CBuffer gradient(v->size() - 1);
			transform(v->begin() + 1, v->end(), v->begin(), gradient.begin(),
				  [](double x, double y) {return fabs(x - y);});
			*v = gradient;
			++v;
		}
	}
	
	static float median( CBuffer::iterator begin, CBuffer::iterator end, size_t len){
		if (len & 1) {
			auto i = begin + (len - 1) / 2;
			nth_element(begin, i, end);
			return *i;
		}else {
			auto i1 = begin + len / 2 - 1;
			auto i2 = begin + len / 2;
			nth_element(begin, i1, end);
			nth_element(begin, i2, end);
			return (*i1 + *i2) / 2.0;
		}
	}
		
	P2DImage  result() {
		evaluate_gradients();
			
		float range = 255.0 / (m_max - m_min);
			
		C2DFImage *variation = new C2DFImage(m_size);
			
		auto ii = variation->begin();
		auto iv = m_field.begin();
		auto ev = m_field.end();
		while (iv != ev) {
			*ii++ = median(iv->begin(), iv->end(), m_n);
			++iv;
		}
			
		ii = variation->begin();
		iv = m_field.begin();
			
		while (iv != ev) {
			auto ip = iv->begin();
			auto ep = iv->end();
				
			while (ip != ep) {
				*ip = fabs(*ip - *ii);
				++ip;
			}
			++iv;
			++ii;
		}
		ii = variation->begin();
		iv = m_field.begin();
		while (iv != ev) {
			*ii++ = median(iv->begin(), iv->end(), m_n) * range;
			++iv;
		}
		return P2DImage(variation);
	}
		
private:
	C2DBounds m_size;
	float m_min;
	float m_max;
	vector<CBuffer> m_field;
	size_t m_n;
	bool m_initialized;
	std::shared_ptr<C1DFilterKernel > gauss;
};
	
int do_main( int argc, char *argv[] )
{

	string in_filename;
	string out_filename;
	string out_type;
	bool crop;
	size_t skip = 0;
	size_t enlarge_boundary = 5;
	size_t gauss_width = 1;

	const auto& imageio = C2DImageIOPluginHandler::instance();

	CCmdOptionList options(g_description);
	options.add(make_opt( in_filename, "in-file", 'i', "input segmentation set", CCmdOptionFlags::required_input));
	options.add(make_opt( out_filename, "out-file", 'o', "output file name", CCmdOptionFlags::required_output, &imageio));
	options.add(make_opt( skip, "skip", 'k', "Skip files at the beginning"));
	options.add(make_opt( enlarge_boundary,  "enlarge-boundary", 'e', "Enlarge cropbox by number of pixels"));
	options.add(make_opt( crop, "crop", 'c', "crop image before running statistics"));
	options.add(make_opt( gauss_width, "gauss", 'g', "gauss filter width for moothing the gradient"));

	if (options.parse(argc, argv, "filter", &C2DFilterPluginHandler::instance()) != CCmdOptionList::hr_no)
		return EXIT_SUCCESS; 
	
	C2DImageFilterChain filter_chain(options.get_remaining());

	cvdebug() << "IO supported types: " << imageio.get_plugin_names() << "\n";
	CSegSetWithImages  segset(in_filename, true);

	if (crop) {
		C2DBoundingBox box = segset.get_boundingbox();
		box.enlarge(enlarge_boundary);
		stringstream crop_descr;
		crop_descr << "crop:"
			   << "start=[" << box.get_grid_begin()
			   << "],end=[" << box.get_grid_end() << "]";
		cvdebug() << "Crop with " << crop_descr.str() << "\r";


		filter_chain.push_front(crop_descr.str().c_str());
	}


	if (skip >= segset.get_images().size())
		throw invalid_argument("Skip is equal or larger then image series");

	auto iimages = segset.get_images().begin();
	auto  eimages = segset.get_images().end();
	advance(iimages, skip);

	C2DVarAccumulator acc(distance(iimages, eimages), gauss_width);
	for (; iimages != eimages; ++iimages) {
		
		P2DImage in_image = *iimages;
		if (!filter_chain.empty() )
			in_image = filter_chain.run(in_image);
		mia::accumulate(acc, *in_image);
	}
	
	if (save_image(out_filename, acc.result()))
		return  EXIT_SUCCESS;
	return EXIT_FAILURE; 
}

MIA_MAIN(do_main)