.. _texture_top:
Texture Analysis
================
.. contents::
:depth: 1
:local:
:backlinks: top
Quantitative imaging biomarkers are increasingly used in cancer imaging
to identify tumor stage and aggressiveness. One of the most promising
biomarkers is image texture – a quantitative measure of spatially
organized, quasi-periodic patterns, or granulations, with a specific
brightness and smoothness. The texture feature vector derived from the
signal within the tumor in combination with clinical and genetic
information may be useful in predicting disease aggressiveness and
outcome.
.. _fig_tumor_growth:
.. figure:: ../images/texture_tumor_growth_small.png
:alt: Schematic of tumor growth
:figwidth: 25 %
:align: right
:figclass: align-right
Schematic of Tumor Growth: a) early stage tumor,
b) advanced tumor with a proliferating outer layer,
quiescent inner layer and necrotic core.
This team at NYU Radiology has established a strong association between
texture measured using MRI, PET, and CT images and histological
characteristics of the kidney, prostate, and breast tumors [1-11]. These
and other studies provide increasing evidence that image texture
reflects the underlying properties of cancer that may be used to support
clinical decisions and improve patient care. FireVoxel contains
analytical tools for computing texture feature vector for individual
images and batch processing.
.. _texture_anl_fv:
Texture analysis in FireVoxel
-----------------------------
FireVoxel’s texture analysis algorithm mimics the biological patterns of
cancer growth by separating the tumor and the surrounding region into
concentric 3D shells (:numref:`fig_tumor_growth`). This approach enables
capturing the spatial variation of texture features that cannot be obtained
by conventional methods. The software combines texture from all available
images, such as images acquired by multiple modalities as well as
computed parametric maps.
.. _texture_algo:
Texture algorithm
-----------------
To generate the texture feature vector, FireVoxel’s algorithm executes
nested loops L1-L4:
::
L1: for each imaging modality
L2: for each region of interest (ROI)
L3: for each spatial scale (filtering size)
L4: for each concentric inner & outer ring
compute signal characteristics & append to feature vector
Here promising modalities may include CT and MRI, as well as MRI-derived
maps of the apparent diffusion coefficient (ADC) and volume transfer
constant K\ :sup:`trans`. The L4 loop may include the surrounding rings,
the inner ring, and the remaining core (:numref:`fig_tumor_rings`). The L3 loop
includes the original image and various filtered versions at a given feature
size (:numref:`fig_feature_filter`). The computed signal characteristics include
the mean, standard deviation, skewness, kurtosis, and entropy.
.. _fig_tumor_rings:
.. figure:: ../images/texture_tumor_rings.png
:alt: Tumor segmented into concentric rings
:align: center
:scale: 50 %
:figclass: align-center
Tumor segmented into concentric rings.
FireVoxel’s texture analysis tool introduces optional 3D image
filters that modify the original image. The dual goal of filtering is to
(a) extract features of given granularity size (:numref:`fig_feature_filter`);
and (b) suppress image artifacts. The current texture-sensitive filtering
is based on the analysis of a spherical vicinity S of each voxel.
FireVoxel’s algorithm considers all possible splits of S into two
hemispheres {S+, S-} and identifies one in which these hemispheres have
the most distinct histograms. Histograms are compared using either a
simple bin-by-bin difference or a sophisticated Earth Mover Distance
method [12]. This yields at each voxel the texture gradient with the
magnitude equal to the largest difference between histograms.
.. _fig_feature_filter:
.. figure:: ../images/texture_features_small.png
:alt: Feature at different 3D filtering scales
:align: center
:figwidth: 50%
:figclass: align-center
Feature identification on MRI image by 3D filtering.
A: original image; B: the same image filtered by texture
gradient sensitized to 1-mm features; C: image filtered
and sensitized to 3-mm features..
.. _texture_batch_tool:
Texture batch tool
------------------
.. _fig_texture_batch:
.. figure:: ../images/texture_batch_cmd.png
:alt: Texture analysis batch tool
:align: right
:scale: 70 %
:figclass: align-right
Texture analysis batch tool.
FireVoxel’s batch tool enables performing texture analysis on
a collection of cases in one source folder. All study cases must be
saved as FireVoxel documents (\*.fvx), which allows saving multiple
coregistered images and ROI masks in a single file. It is important to
name imaging modalities and ROIs consistently across the study for
easier identification of the results.
With no images loaded, select **Applications** > **Region Heterogeneity
Analysis Batch process** (:numref:`fig_texture_batch`). This opens the dialog
to configure the analysis (:numref:`fig_texture_dialog`). The settings in this
dialog are described below.
**Source Folder** – To specify the source directory, click
Browse to open browse-for-folder dialog and select the directory of the
study cases. The source directory must contain all study cases saved as
FireVoxel documents (.fvx).
**Output log** – To specify the output file, click Browse to open
save-as dialog, navigate to the target location and enter a file name
(\*.txt). The output file will show the path to the source directory and
the processing settings from this dialog. The results are presented as a
tab-delimited table, with each layer from each case in a separate row
and the following columns: case ID, modality, ROI (shell), texture
radius, ROI volume, and ROI parameters (mean, standard deviation,
skewness, kurtosis, and entropy).
.. _fig_texture_dialog:
.. figure:: ../images/texture_dialog.png
:alt: Texture analysis dialog
:scale: 70 %
:align: center
:figclass: align-center
Texture analysis dialog.
**Texture levels (mm)** – Parameters describing automatically created
texture filters that will be applied to all image layers. For example,
if interested in analyzing 1) the original image and 2) a 3-mm filtered
image that enhances 3-mm-size features, enter 0,3 in this box. Note that
filtering can be time-consuming, so for the initial testing, skip
filtering and enter 0.
**Voxel Weights** (drop-down menu), **Texture Sensitive Gradient (EMD)**
(checkbox), **2D** (checkbox) – Filter parameters. The options for
**Voxel Weights** include Constant (default), Gaussian, Radial, and Rayleigh.
**Texture Sensitive Gradient (EMD)**, if checked, selects the Earth Mover Distance
method. Users may wish to start with the default settings (Constant, No, No).
**Outer layer width (mm)** – If not zero, the program will automatically
create a 3D region of the specified thickness. This ROI is located just
outside the lesion. The histogram features for this region will be
labeled: lesion_or, where lesion is the name of the main (whole-lesion)
ROI.
**Number of inner layers** – If not zero, the program will automatically
create the specified number of inner layers, or rings. These concentric
rings are all located inside the lesion. The histogram features for the
first ring will be labeled lesion_ir1; for the second ring, lesion_ir2,
etc. The algorithm will also segment the inner core – the area remaining
after all of rings are subtracted from the whole-lesion ROI. The
histogram features for the core are labeled lesion_co.
**Inner layer width (mm)** – The thickness (in mm) of each ring. When
selecting the ring thickness and the number of rings, consider the
smallest lesion size in the study. Some rings may end up being empty
if the layer thickness is too large.
.. _fig_texture_roistats3d:
.. figure:: ../images/texture_roistats3d.png
:alt: ROI Stats 3D dialog
:align: center
:scale: 60 %
:figclass: align-center
ROI Stats 3D dialog.
**Histogram** – Histogram parameters for the analysis. Note that these
settings will impact the computed results. Prior to the analysis, these
parameters may need to be tested and selected interactively in **Layer
Control** > **ROI Stats 3D** (:numref:`fig_texture_roistats3d`).
- **Number of Bins** – The number of bins in histogram (default, 100,
as in ROI Stats 3D).
- **Value range** (min and max values) – The minimum and maximum values
of the range of the histogram. The default is [0, 100000]. In ROI
Stats 3D dialog, the default range is set to the full range of data
within the ROI, between the lowest and the highest values.
- **Use full range** (checkbox) – Has the same effect as the Full
button in ROI Stats 3D. Expands the range to include the full range
of data between the lowest and highest data values.
- **Exclude outside** (checkbox) – Has the same effect as Clip checkbox
in ROI Stats 3D. If checked, the data outside the value range are
excluded.
**References**
1. Dittmer-Roche B, Rusinek H, Ko JP, McGuinness G, Naidich D. Automated
assessment of small airway disease on lung CT. SPIE Medical Imaging:
Image Processing, Vol. 5030, 403-13, 2003.
2. Doshi AM, Ream JM, Kierans AS, Bilbily M, Rusinek H, Huang W,
Chandarana H. Use of MRI in differentiation of papillary renal cell
carcinoma subtypes: Qualitative and quantitative analysis. AJR Am J
Roentgenol 206(3):566-72, 2016.
`PMID: 26901013 `__
3. Kierans AS, Bennett GL, Mussi TC, Babb JS, Rusinek H, Melamed J,
Rosenkrantz AB. Characterization of malignancy of adnexal lesions using
ADC entropy: comparison with mean ADC and qualitative DWI assessment. J
Magn Reson Imaging 37(1):164-71, 2013. PMID: 23188749.
`PMID: 23188749 `__
4. Kierans AS, Rusinek H, Lee A, Shaikh MB, Triolo M, Huang WC,
Chandarana H. Textural differences in apparent diffusion coefficient
between low- and high-stage clear cell renal cell carcinoma. AJR Am J
Roentgenol 203(6):W637-44, 2014.
`PMID: 25415729 `__
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Crawford B, Tsai EB, Koo CW, Mikheev A, Rusinek H. Lung adenocarcinoma:
Correlation of quantitative CT findings with pathologic findings.
Radiology 280(3):931-9, 2016.
`PMID: 27097236 `__
6. Pires A, Rusinek H, Suh J, Naidich DP, Pass H, Ko JP. Clustering of
lung adenocarcinomas classes using automated texture analysis on CT
images. Proc SPIE Vol. 8669, 25-32, Medical Imaging 2013: Computer-Aided
Diagnosis. Nico Karssemeijer, Ronald M. Summers, Eds.
7. Rosenkrantz AB, Obele C, Rusinek H, Balar AV, Huang WC, Deng FM, Ream
JM. Whole-lesion diffusion metrics for assessment of bladder cancer
aggressiveness. Abdom Imaging 40(2):327-32, 2015.
`PMID: 25106502 `__
8. Rosenkrantz AB, Triolo MJ, Melamed J, Rusinek H, Taneja SS, Deng FM.
Whole-lesion apparent diffusion coefficient metrics as a marker of
percentage Gleason 4 component within Gleason 7 prostate cancer at
radical prostatectomy. J Magn Reson Imaging 41(3):708-14, 2015.
9. Rosenkrantz AB, Ream JM, Nolan P, Rusinek H, Deng FM, Taneja SS.
Prostate cancer: Utility of whole-lesion apparent diffusion coefficient
metrics for prediction of biochemical recurrence following radical
prostatectomy. AJR Am J Roentgenol 205(6):1208-14, 2015.
`PMID: 24616064 `__
10. Rosenkrantz AB, Meng X, Ream JM, Babb JS, Deng FM, Rusinek H, Huang
WC, Lepor H, Taneja SS. Likert score 3 prostate lesions: Association
between whole-lesion ADC metrics and pathologic findings at
MRI/ultrasound fusion targeted biopsy. J Magn Reson Imaging
43(2):325-32, 2016.
`PMID: 26131965 `__
11. Stember JN, Ko JP, Naidich DP, Kaur M, Rusinek H. The self-overlap
method for assessment of lung nodule morphology in chest CT. J Digit
Imaging 26(2):239-47, 2013.
`PMID: 23065123 `__
12. Rubner Y, Tomasi C, Guibas LJ. The earth mover's distance as a
metric for image retrieval. Int J Comput Vis 40(2): 99-121, 2000.
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