1 /*=========================================================================
4 Module: $RCSfile: gdcmOrientation.cxx,v $
6 Date: $Date: 2005/09/19 09:48:27 $
7 Version: $Revision: 1.8 $
9 Copyright (c) CREATIS (Centre de Recherche et d'Applications en Traitement de
10 l'Image). All rights reserved. See Doc/License.txt or
11 http://www.creatis.insa-lyon.fr/Public/Gdcm/License.html for details.
13 This software is distributed WITHOUT ANY WARRANTY; without even
14 the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
15 PURPOSE. See the above copyright notices for more information.
17 =========================================================================*/
19 #include "gdcmOrientation.h"
21 #include "gdcmDebug.h"
22 #include <math.h> // for sqrt
26 //--------------------------------------------------------------------
27 // THERALYS Algorithm to determine the most similar basic orientation
29 // Transliterated from original Python code.
30 // Kept as close as possible to the original code
31 // in order to speed up any further modif of Python code :-(
32 //-----------------------------------------------------------------------
35 * \brief THERALYS' Algorithm to determine the most similar basic orientation
36 * (Axial, Coronal, Sagital) of the image
37 * \note Should be run on the first gdcm::File of a 'coherent' Serie
38 * @return orientation code
39 * # 0 : Not Applicable (neither 0020,0037 Image Orientation Patient
40 * # nor 0020,0032 Image Position found)
44 * # -2 : Coronal invert
46 * # -3 : Sagital invert
48 * # -4 : Heart Axial invert
50 * # -5 : Heart Coronal invert
52 * # -6 : Heart Sagital invert
54 double Orientation::TypeOrientation( File *f )
57 bool succ = f->GetImageOrientationPatient( iop );
60 gdcmErrorMacro( "No Image Orientation (0020,0037) found in the file, cannot proceed." )
64 std::cout << " iop : ";
66 std::cout << iop[i] << " ";
67 std::cout << std::endl;
72 ori1.x = iop[0]; ori1.y = iop[1]; ori1.z = iop[2];
73 ori2.x = iop[3]; ori2.y = iop[4]; ori2.z = iop[5];
75 // two perpendicular vectors describe one plane
76 double dicPlane[6][2][3] =
77 { { {1, 0, 0 },{0, 1, 0 } }, // Axial
78 { {1, 0, 0 },{0, 0, -1 } }, // Coronal
79 { {0, 1, 0 },{0, 0, -1 } }, // Sagittal
80 { { 0.8, 0.5, 0.0 },{-0.1, 0.1 , -0.95 } }, // Axial - HEART
81 { { 0.8, 0.5, 0.0 },{-0.6674, 0.687, 0.1794} }, // Coronal - HEART
82 { {-0.1, 0.1, -0.95},{-0.6674, 0.687, 0.1794} } // Sagittal - HEART
88 Res res; // [ <result> , <memory of the last succes calcule> ]
91 for (int numDicPlane=0; numDicPlane<6; numDicPlane++)
95 refA.x = dicPlane[numDicPlane][0][0];
96 refA.y = dicPlane[numDicPlane][0][1];
97 refA.z = dicPlane[numDicPlane][0][2];
99 refB.x = dicPlane[numDicPlane][1][0];
100 refB.y = dicPlane[numDicPlane][1][1];
101 refB.z = dicPlane[numDicPlane][1][2];
102 res=VerfCriterion( i, CalculLikelyhood2Vec(refA,refB,ori1,ori2), res );
103 res=VerfCriterion( -i, CalculLikelyhood2Vec(refB,refA,ori1,ori2), res );
108 // res=[0,99999] ## [ <result> , <memory of the last succes calculus> ]
109 // for plane in dicPlane:
113 // res=self.VerfCriterion( i , self.CalculLikelyhood2Vec(refA,refB,ori1,ori2) , res )
114 // res=self.VerfCriterion( -i , self.CalculLikelyhood2Vec(refB,refA,ori1,ori2) , res )
120 Orientation::VerfCriterion(int typeCriterion, double criterionNew, Res const &in)
123 double criterion = in.second;
124 if (criterionNew < criterion)
126 res.first = typeCriterion;;
127 res.second = criterionNew;
131 // criterion = res[1]
132 // # if criterionNew<0.1 and criterionNew<criterion:
133 // if criterionNew<criterion:
134 // criterion=criterionNew
135 // type=typeCriterion
136 // return [ type , criterion ]
141 inline double square_dist(vector3D const &v1, vector3D const &v2)
144 res = (v1.x - v2.x)*(v1.x - v2.x) +
145 (v1.y - v2.y)*(v1.y - v2.y) +
146 (v1.z - v2.z)*(v1.z - v2.z);
150 //------------------------- Purpose : -----------------------------------
151 //- This function determines the orientation similarity of two planes.
152 // Each plane is described by two vectors.
153 //------------------------- Parameters : --------------------------------
154 //- <refA> : - type : vector 3D (double)
155 //- <refB> : - type : vector 3D (double)
156 // - Description of the first plane
157 //- <ori1> : - type : vector 3D (double)
158 //- <ori2> : - type : vector 3D (double)
159 // - Description of the second plane
160 //------------------------- Return : ------------------------------------
161 // double : 0 if the planes are perpendicular. While the difference of
162 // the orientation between the planes are big more enlarge is
164 //------------------------- Other : -------------------------------------
165 // The calculus is based with vectors normalice
167 Orientation::CalculLikelyhood2Vec(vector3D const &refA, vector3D const &refB,
168 vector3D const &ori1, vector3D const &ori2 )
171 vector3D ori3 = ProductVectorial(ori1,ori2);
172 vector3D refC = ProductVectorial(refA,refB);
173 double res = square_dist(refC, ori3);
178 //------------------------- Purpose : -----------------------------------
179 //- Calculus of the poduct vectorial between two vectors 3D
180 //------------------------- Parameters : --------------------------------
181 //- <vec1> : - type : vector 3D (double)
182 //- <vec2> : - type : vector 3D (double)
183 //------------------------- Return : ------------------------------------
184 // (vec) : - Vector 3D
185 //------------------------- Other : -------------------------------------
187 Orientation::ProductVectorial(vector3D const & vec1, vector3D const & vec2)
190 vec3.x = vec1.y*vec2.z - vec1.z*vec2.y;
191 vec3.y = -( vec1.x*vec2.z - vec1.z*vec2.x);
192 vec3.z = vec1.x*vec2.y - vec1.y*vec2.x;
197 } // end namespace gdcm
202 // ---------------------------------------------------------------------------
203 // Here is the original Python code, kindly supplied by THERALYS
205 // C++ code doesn't give good results
210 def TypeOrientation(self,file0):
212 # ------------------------- Purpose : -----------------------------------
213 # - This function compare the orientation of the given image and the
214 # basics orientations (Axial, Cornal, Sagital)
215 # ------------------------- Parameters : --------------------------------
216 # - <file0> : - type : string
217 # - The name of the first image file of the serie
218 # ------------------------- Return : ------------------------------------
222 # -2 : Coronal invert
224 # -3 : Sagital invert
226 # -4 : Heart Axial invert
228 # -5 : Heart Coronal invert
230 # -6 : Heart Sagital invert
232 # ------------------------- Other : -------------------------------------
233 # This method finds the most similar basic orientation.
236 toRead = gdcm.File(file0)
237 ValDict = GetValuesDict(toRead)
239 imageOrientation=ValDict["Image Orientation (Patient)"]
241 imageOrientation=ValDict["Image Orientation"]
243 ori1=[float(split(imageOrientation,"\\")[0]),\
244 float(split(imageOrientation,"\\")[1]),\
245 float(split(imageOrientation,"\\")[2])]
246 ori2=[float(split(imageOrientation,"\\")[3]),\
247 float(split(imageOrientation,"\\")[4]),\
248 float(split(imageOrientation,"\\")[5])]
250 ## two vectors perpendicular describe one plane
251 dicPlane=[ [ [1,0,0],[0,1,0] ], ## Axial
252 [ [1,0,0],[0,0,-1] ], ## Coronal
253 [ [0,1,0],[0,0,-1] ], ## Sagittal
254 [ [ 0.8 , 0.5 , 0.0 ],[-0.1 , 0.1 , -0.95] ],## Axial - HEART
255 [ [ 0.8 , 0.5 , 0.0 ],[-0.6674 , 0.687 , 0.1794] ],## Coronal - HEART
256 [ [-0.1 , 0.1 , -0.95],[-0.6674 , 0.687 , 0.1794] ] ] ## Sagittal - HEART
259 res=[0,99999] ## [ <result> , <memory of the last succes calcule> ]
260 for plane in dicPlane:
264 res=self.VerfCriterion( i , self.CalculLikelyhood2Vec(refA,refB,ori1,ori2) , res )
265 res=self.VerfCriterion( -i , self.CalculLikelyhood2Vec(refB,refA,ori1,ori2) , res )
272 def VerfCriterion(self,typeCriterion,criterionNew,res):
275 # if criterionNew<0.1 and criterionNew<criterion:
276 if criterionNew<criterion:
277 criterion=criterionNew
279 return [ type , criterion ]
282 def CalculLikelyhood2Vec(self,refA,refB,ori1,ori2):
284 # ------------------------- Purpose : -----------------------------------
285 # - This function determine the orientation similarity of two planes.
286 # Each plane is described by two vector.
287 # ------------------------- Parameters : --------------------------------
288 # - <refA> : - type : vector 3D (float)
289 # - <refB> : - type : vector 3D (float)
290 # - Description of the first plane
291 # - <ori1> : - type : vector 3D (float)
292 # - <ori2> : - type : vector 3D (float)
293 # - Description of the second plane
294 # ------------------------- Return : ------------------------------------
295 # float : 0 if the planes are perpendicular.
296 # While the difference of the orientation between the planes
297 # are big more enlarge is
299 # ------------------------- Other : -------------------------------------
300 # The calculus is based with vectors normalice
303 ori3=self.ProductVectorial(ori1,ori2)
304 refC=self.ProductVectorial(refA,refB)
305 res=math.pow(refC[0]-ori3[0],2) + math.pow(refC[1]-ori3[1],2) + math.pow(refC[2]-ori3[2],2)
306 return math.sqrt(res)
308 def ProductVectorial(self,vec1,vec2):
310 # ------------------------- Purpose : -----------------------------------
311 # - Calculus of the poduct vectorial between two vectors 3D
312 # ------------------------- Parameters : --------------------------------
313 # - <vec1> : - type : vector 3D (float)
314 # - <vec2> : - type : vector 3D (float)
315 # ------------------------- Return : ------------------------------------
316 # (vec) : - Vector 3D
317 # ------------------------- Other : -------------------------------------
320 vec3[0]=vec1[1]*vec2[2] - vec1[2]*vec2[1]
321 vec3[1]=-( vec1[0]*vec2[2] - vec1[2]*vec2[0])
322 vec3[2]=vec1[0]*vec2[1] - vec1[1]*vec2[0]
325 def GetValuesDict(image):
327 Returns a dictionnary containing values associated with Field Names
328 dict["Dicom Field Name"]="Dicom field value"
330 val=image.GetFirstEntry()
333 if isinstance(val,gdcm.ValEntryPtr):
334 dic[val.GetName()]=val.GetValue()
335 val=image.GetNextEntry()