MATLAB File List
| angauss | ANGAUSS Anisotropic gaussian filtering.
INPUT:
Is - Input tomogram
s - Gaussian standard deviation
r - Aspect ration in Z axis, if 1 isotropic
OUTPUT:
S - Filtered output
See also: diff3d
AUTHOR: Antonio Martinez-Sanchez (an.martinez.s.sw@gmail.com)
REFERENCES:
[1] Martinez-Sanchez A., et al. Robust membrane detection based on tensor voting
for electron tomography. J Struct Biol. 186 (2014) 49-61.
9: See also: diff3d |
| cropt | CROPT Crop a tomogram, set to zero voxels in the border of the tomogram
INPUT:
T - input tomogram
i = [li hi] - range for coodinate i
OUTPUT:
C - output cropped tomogram
See also: global_analysis
AUTHOR: Antonio Martinez-Sanchez (an.martinez.s.sw@gmail.com)
REFERENCES:
[1] Martinez-Sanchez A., et al. Robust membrane detection based on tensor voting
for electron tomography. J Struct Biol. 186 (2014) 49-61.
8: See also: global_analysis |
| diff2d | DIFF2D Differentiation in 2D images
INPUT:
Is - Input image
k - Dimension 1: x-dimension, otherwise: y-dimension
h - Step size
OUTPUT:
D - Output tomogram
See also: diff3d, dtvoting, dtvotinge
AUTHOR: Antonio Martinez-Sanchez (an.martinez.s.sw@gmail.com)
REFERENCES:
[1] Martinez-Sanchez A., et al. Robust membrane detection based on tensor voting
for electron tomography. J Struct Biol. 186 (2014) 49-61.
9: See also: diff3d, dtvoting, dtvotinge |
| diff3d | DIFF3D Calculates partial derivative along any dimension in a tomogram.
INPUT:
Is - Input tomogram
k - 1: x-dimension, 2: y-dimension and otherwise: z-dimension
OUTPUT:
D - Output tomgram
See also: angauss, eig3dkmex, diff2d
AUTHOR: Antonio Martinez-Sanchez (an.martinez.s.sw@gmail.com)
REFERENCES:
[1] Martinez-Sanchez A., et al. Robust membrane detection based on tensor voting
for electron tomography. J Struct Biol. 186 (2014) 49-61.
8: See also: angauss, eig3dkmex, diff2d |
| dtvoting | STVOTING Applies dense 2D tensor voting for ridge detection by steerable filters over
every slice of a tomogram at X, Y and Z axes.
INPUT:
E - Input tomogram (foreground dark)
s - Scale factor
sg - variance for gaussian prefiltering
m - Missing wedge semiangle in Z axis, it tries to prevent missing wedge effects
if less than 0 is disabled
d - Input data; 1- foreground black, otherwise- forecast bright
v - If equal to 1 verbose mode activated (disabled by default)
OUTPUT:
S - Output saliency
See also: steer2dtvoting, dtvotinge
AUTHOR: Antonio Martinez-Sanchez (an.martinez.s.sw@gmail.com)
REFERENCES:
[1] Martinez-Sanchez A., et al. Robust membrane detection based on tensor voting
for electron tomography. J Struct Biol. 186 (2014) 49-61.
14: See also: steer2dtvoting, dtvotinge |
| dtvotinge | DTVOTINGE Applies dense 2D tensor voting by for edge detection by steerable filters over
every slice of a tomogram at X, Y and Z axes.
INPUT:
E - Input tomogram
s - Scale factor
sg - variance for gaussian prefiltering
m - Missing wedge semiangle in Z axis, it tries to prevent missing wedge effects
if less than 0 is disabled
v - If equal to 1 verbose mode activated (disabled by default)
OUTPUT:
S - Output saliency
See also: steer2dtvoting, dtvoting
AUTHOR: Antonio Martinez-Sanchez (an.martinez.s.sw@gmail.com)
REFERENCES:
[1] Martinez-Sanchez A., et al. Robust membrane detection based on tensor voting
for electron tomography. J Struct Biol. 186 (2014) 49-61.
13: See also: steer2dtvoting, dtvoting |
| eig3dkmex | EIG3DKMEX Multicore eigenvalues and eigenvectors computation in 3x3 symmetric real tensors
of tomograms
INPUT:
Iij - Tomograms with the i, j discrete diferentials, input data must be single and double
mode: 1-> analytical (fastest), 2-> hybird (intermediate) ohterwise-> Jacobi
(the most precise)
OUTPUT:
Lk: the k eigen value ordered by its absolute value (descendant)
Vk: the corresponding eigenvector
See also: diff3d, surfaceness
AUTHOR: Antonio Martinez-Sanchez (an.martinez.s.sw@gmail.com)
REFERENCES:
[1] Martinez-Sanchez A., et al. Robust membrane detection based on tensor voting
for electron tomography. J Struct Biol. 186 (2014) 49-61.
11: See also: diff3d, surfaceness |
| getsaliency | GETSALIENCY Get ridge saliency and its normal
INPUT:
E - Input tomogram (foreground bright), input data must be single and double
s - smoothing scale
mode: 1-> analytical (fastest), 2-> hybird (intermediate) ohterwise-> Jacobi
(the most precise)
OUTPUT:
S - normal vector field
Vi - the i coordinate of the normal vector field
See also: eig3dkmex, nonmaxsup, diff3d, angauss
AUTHOR: Antonio Martinez-Sanchez (an.martinez.s.sw@gmail.com)
REFERENCES:
[1] Martinez-Sanchez A., et al. Robust membrane detection based on tensor voting
for electron tomography. J Struct Biol. 186 (2014) 49-61.
11: See also: eig3dkmex, nonmaxsup, diff3d, angauss |
| getsysinfo_linux | GETSYSINFO_LINUX Return system information on Linux Computers
OUTPUT: If error in some output it will be set ot -1
c - number of available cores
m - memory size
l - Cache size
See also:
AUTHOR: Antonio Martinez-Sanchez (an.martinez.s.sw@gmail.com)
REFERENCES:
[1] Martinez-Sanchez A., et al. Robust membrane detection based on tensor voting
for electron tomography. J Struct Biol. 186 (2014) 49-61.
No See Also line |
| global_analysis | GLOBAL_ANALYSIS Perform a simple global analysis for tomogram segmentation, firstly binarizes
and secondly labels tomogram structures according to their size
INPUT:
M - Input tomogram
tv - Binarization threshold
c - Voxel connectivity: 6, 8 (default) or 26
v - if 1 verbose mode activated
OUTPUT:
C - Output tomogram with the structures labeled according their size
See also: tomosegmemtv, cropt
AUTHOR: Antonio Martinez-Sanchez (an.martinez.s.sw@gmail.com)
REFERENCES:
[1] Martinez-Sanchez A., et al. Robust membrane detection based on tensor voting
for electron tomography. J Struct Biol. 186 (2014) 49-61.
12: See also: tomosegmemtv, cropt |
| linmap | LINMAP Calcultes the parameters for remaping data linearly
INPUT:
li - Lower bound input range for independent variable
ui - Upper bound input range for independent variable
lo - Lower bound input range for dependent variable
uo - Upper bound input range for dependent variable
OUTPUT:
m - Linear slope
c - Linear offset
See also:
AUTHOR: Antonio Martinez-Sanchez (an.martinez.s.sw@gmail.com)
REFERENCES:
[1] Martinez-Sanchez A., et al. Robust membrane detection based on tensor voting
for electron tomography. J Struct Biol. 186 (2014) 49-61.
11: See also: |
| membflt_kernel | MEMBFLT_KERNEL Kernel code for TOMOSEGMEMTV. Do not call this function directly, call tomosegmemtv instead.
INPUT:
I - Input tomogram (foreground dark), input data must be single and double
s - Scale factor
t - Membrane thickness factor
v - If equal to 1 verbose mode activated (disabled by default)
m - If equal to 1 ridge detection (default), otherwise edge detection
w - Missing wedge semiangle in Z axis, it tries to prevent missing wedge effects
if less than 0 is disabled
e - Mode for resolving eigenproblem; 1- Fast (default), 2- Intermediate,
otherwise- Accurate
d - Densification scale factor
OUTPUT:
F - filtered tomogram with the membranes enhanced, -1 if error
Vi - Coordinate i of the normal to membrane
See also: tomosegmemtv
AUTHOR: Antonio Martinez-Sanchez (an.martinez.s.sw@gmail.com)
REFERENCES:
[1] Martinez-Sanchez A., et al. Robust membrane detection based on tensor voting
for electron tomography. J Struct Biol. 186 (2014) 49-61.
17: See also: tomosegmemtv |
| nonmaxsup | NONMAXSUP Ridge centreline detection by non-maximum suppresion criteria
INPUT:
I: Input tomogram, input data must be single or double
M: Binary (logical format) mask for improve the speed
Vi: The i coordinate of the major eigenvector
OUTPUT:
B: binary output
See also: eig3dkmex, surfaceness, getsaliency
AUTHOR: Antonio Martinez-Sanchez (an.martinez.s.sw@gmail.com)
REFERENCES:
[1] Martinez-Sanchez A., et al. Robust membrane detection based on tensor voting
for electron tomography. J Struct Biol. 186 (2014) 49-61.
9: See also: eig3dkmex, surfaceness, getsaliency |
| steer2dtvoting | STEER2DTVOTING Tensor voting for 2D image by using steerable filters.
INPUT:
Is - Input image stickness
Io - Input orientation
Wi - Basis filter i in Fourier space
OUTPUT:
S - Output saliency
See also: dtvoting, dtvotinge
AUTHOR: Antonio Martinez-Sanchez
REFERENCES:
[1] Martinez-Sanchez A., et al. Robust membrane detection based on tensor voting
for electron tomography. sent to PLoS ONE. (2013)
9: See also: dtvoting, dtvotinge |
| surfaceness | SURFACENESS Enhances local ridges (or edges) with surface shape
INPUT:
I - Input tomogram (foreground bright), input data must be single and double
s - Scale factor
m - If equal to 1 ridge detection (default), otherwise edge detection
e - Mode for resolving eigenproblem; 1- Fast (default), 2- Intermediate,
otherwise- Accurate
v - If equal to 1 verbose mode activated (disabled by default)
OUTPUT:
F - filtered tomogram with the membranes enhanced, -1 if error
Vi - Coordinate i of the normal to membrane
See also: tomosegmemtv
AUTHOR: Antonio Martinez-Sanchez (an.martinez.s.sw@gmail.com)
REFERENCES:
[1] Martinez-Sanchez A., et al. Robust membrane detection based on tensor voting
for electron tomography. J Struct Biol. 186 (2014) 49-61.
13: See also: tomosegmemtv |
| tomosegmemtv | TOMOSEGMEMTV Centreline detection of plane ridges, membranes (Mac OS version 0.2)
INPUT:
I - Input tomogram (foreground dark), all of its dimension must be greater than 3
s - Scale factor
t - Membrane thickness factor
v - (optional) If equal to 1 verbose mode activated (disabled by default)
m - (optional) If equal to 1 ridge detection (default), otherwise edge detection
w - (optional) Missing wedge semiangle in Z axis, it tries to prevent missing wedge effects,
if less than 0 is disabled (default)
e - (optional) Mode for resolving eigenproblem; 1- Fast (default), 2- Intermediate,
otherwise- Accurate
d - (optional) Densification scale factor, by default is 10
u - (optional) Memory reduction coeficient if it is less than 1 the input data may be
divided in blocks.
OUTPUT:
If the input format for data are "double" the output will have this format, otherwise
will have "single" format
F - filtered tomogram with the membranes enhanced
Vi - Coordinate i of the normal to membrane
See also: global_analysis, cropt
AUTHOR: Antonio Martinez-Sanchez (an.martinez.s.sw@gmail.com)
REFERENCES:
[1] Martinez-Sanchez A., et al. Robust membrane detection based on tensor voting
for electron tomography. J Struct Biol. 186 (2014) 49-61.
21: See also: global_analysis, cropt |
| tomosegmemtv_batch | Script for processing all tomograms in a directory with tomosegmemtv Author: Antonio Martinez-SanchezNo See Also line |