Tools for the Analysis of 3D image series


Get derivative a transformation obtained by by using image registration for any given positions in 3D. The position data is given in CSV format: id;time;x;y;z;reserved The output data will be stored in a binary file with an ascii header describing the data. An example header is given below: MIA tensorfield { dim=3 #number of dimensions of the data components=13 #number of components per element component_description=vector3,scalar,matrix3x3 #interpretation of components elements=20 #number of elements in file interpretation=strain #interpretation of elements (strain|derivative) style=sparse #storage style (sparse|grid) repn=float32 #representation of values size=1000 1000 200 #grid size of the transformation endian=low #endianess of binary data (low|big) } This example header has to be interpreted like follows: three-dimensional data, each entry consists of 13 values the values etry consists of a 3D vector, a scalar, and a 3x3 matrix (saved in row-major format).The data records represent strain tensors, and only a sparse set of points is given. The values are given as single floating point (32 bit). The original transformation field corresponds to images of 1000x1000x200 voxels and the binary data is stored in low endian format.


This program tracks the intensity of a pixel at the given coordinates.


Track the position of a pixel by means of a transformation obtained by using image registration. The pixel data is given in CSV format id;time;x;y;z;reserved The fields 'time', 'x', 'y', and 'z' are updated, and the fields 'id' and 'reserved' are preserved, empty lines are ignored.