Download Using CT Texture Analysis to Predict Behavior of

Daniel Thomas Ginat, MD, MS
Department of Radiology
eEdE-114
Disclosures
None
Introduction and Purpose
• Texture analysis is a form of quantitative imaging that
provides insights into the nature of tumors that are not
necessarily apparent upon visual inspection, but may
provide added value in terms of better describing the
biological attributes of tumors and impact upon patient
management.
• The goal of this educational exhibit is to provide an
overview of texture analysis potential applications
related to characterization of head and neck tumors.
• Applications in this realm are mostly still investigational
and cutting-edge.
Texture Analysis Definition and Role
in Medical Imaging
• Texture analysis describes a wide range of
techniques that enable quantification of the graylevel patterns, pixel interrelationships, and the
spectral properties of an image.
• By examining the nature of gray-scale patterns in
medical images, a subset of texture features, or
“texture signature,” can be extracted that
characterizes various lesions.
• Textural features may complement the
macrotexture information already apparent to
radiologists.
Pre-Processing
• Segmentation is performed to split the image into
useful components.
• Regions of interest (ROIs) are selected manually
or using software segmentation techniques to
select the lesion or normal tissues for
normalization/harmonization, if needed.
• Next a binary mask is produced to illustrate the
region of the gray level image that will be input
for subsequent texture analysis.
Constructing ROIs
Representative CT shows
a right tonsil tumor
Manually constructed
ROI on CT image
Manually constructed
binary mask
Main Steps in Texture Analysis
Feature
estimation
Syntactic
Statistical
Spectral
Feature
selection
Manual
Co-occurrence matrix
Principal components
analysis
Step-wise discriminant
Classification
Evaluation
Decision trees
k-nearest neighbor
Support vector machines
Neural network techniques
Linear discriminant
analysis
Cross-validation
K-means
Hierarchical clustering
Texture Analysis Techniques
• Syntactic texture analysis: identifies fundamental or
“primitive” elements of the image, which are then
linked through syntax.
• Statistical analysis: first-order features refer to gradient
parameters that characterize local gray-level
differences, while second-order features are extracted
from grey scale coincidence matrices, which are
constructed by systematically considering the
relationship between pixel pairs and tabulating the
frequency of various gray level combinations within an
image or within a region of interest.
• Spectral analysis: exploits wavelet functions to
evaluate spatial frequencies at multiple scales, but is
computationally intensive.
Histogram use in Texture Analysis
• The histogram of intensity levels is a concise and
simple summary of the statistical information
contained in the image.
• Calculation of the histogram involves single
pixels in an image.
• Thus, the histogram contains the first-order
statistical information about the image, but is
not spatially-dependent.
1D and 2D Statistical Texture Analysis
Parameters
(and many more!)
What the Equations Mean
• Mean: average of all numbers in a data set
• Variance: measures how far each number in the set is from the mean
• Skewness: measure of the asymmetry of the probability distribution of a
real-valued random variable about its mean
• Energy: provides the sum of squared elements in the gray-level cooccurrence matrix. Also known as uniformity or the angular second
moment
• Entropy: measures the disorder in a system
• Contrast: measures the local variations in the gray-level co-occurrence
matrix
• Correlation: measure the strength of association between two variables
• Angular second moment: the square of energy
• Homogeneity: measures the closeness of the distribution of elements in
the gray-level co-occurrence matrix to the gray-level co-occurrence matrix
diagonal
• Kurtosis: measure of whether the data are heavy-tailed or light-tailed
relative to a normal distribution
Spatially-Dependent Texture Features
• Matrices used to compute the grey-level run length
features are based on the length and quantity of runs
(contiguous pixels with matching intensity values).
• Short-run emphasis (SRE) feature indicates whether the
image has a majority of short runs, while the long-run
emphasis (LRE) feature indicates a majority of long runs.
1
1
0
1
0
1
1
1
1
Example binary
grey-scale image
and gray-level
run-length matrix
features
Horizontal
Runs
Grey
Level
Run Length
2
3
0
0
0
1
1
1
Feature Selection and Extraction
• It is important to reduce the number of features before
classification so that the statistical model will better reflect
the noise or random error than the underlying data
• Manual selection of a subset of features based on previous
work most relevant to the hypothesis being tested.
• Scatterplots and linear regression can be used to evaluate
correlations between pairs of textural features and identify
potentially redundant features
• To compute a characteristic of a digital image able to
numerically describe its texture properties; First-order
histogram based features, Co-occurrence matrix based
features, Multiscale features.
Feature Selection
• Choosing most effective features for class
distinction.
• Strongly skewed features are rejected, while
strongly correlated features are rejected.
• The most desirable line of approach is to pay a lot
of attention in choosing image features so that
the classes are linearly separable.
• Scatterplots and linear regression can be used to
evaluate correlations between pairs of textural
features and, therefore, identify potentially
redundant features.
Feature Classification
• Involves partitioning the feature space according
to tissue class or diagnostic category.
• Classification is typically accomplished using a
decision or discriminant function, which is used
to determine what variables distinguish between
two or more groups.
• For example, linear discriminant analysis
expresses one dependent variable as a linear
combination of other features or measurements
and is a component of pattern recognition and
machine learning.
Feature Evaluation
• The accuracy or success of our feature classification strategy is
generally evaluated by cross-validation:
– dividing the data into training and testing subsets
– performing the classification on the training set
– validating the results of the classification on the testing set
– re-partition the original cases and repeat the procedure
• Logistic regression can also be used by assigning the most
discriminating features as predictors and either tissue class or
diagnosis as the outcome measure. The classification accuracy is
then calculated by measuring the area under the ROC curve.
• K-means or hierarchical clustering are suitable when there is no
prior knowledge of how the feature space is organized.
Potential Clinical Applications
• Objective strategy for lesion segmentation and
characterization, particularly in cases in which the
lesions are inseparable on the basis of
conventional CT and MRI.
• Differentiating benign versus malignant tumors,
types of malignant tumors, and prognosis.
• Texture may assist in monitoring disease
progression or evaluation of emerging therapies.
Benign versus Malignant Tumors
Pleomorphic adenoma ADC map
• 2D and 3D texture-based analysis
have shown promise in differentiating
between benign and malignant
tumors, such as cysts, glomus tumors,
and schwannomas, versus lymphoma,
and adenocarcinoma, but the number
of correctly classified data vectors
available so far is insufficient to
implement this technique in the
routine evaluation of MRI.
• Images should originate from one
scanner with an identical protocol.
Lymphoma ADC map
Determining Human Papillomavirus (HPV) Status in
Oropharyngeal Squamous Cell Carcinoma
• HPV-positive oropharyngeal squamous
cell carcinomas have a different
prognosis and behavior pattern,
including fewer secondary malignancies
compared with HPV-negative
oropharyngeal squamous cell
carcinomas and an overall more
favorable prognosis.
• The histogram feature median and
entropy are different among HPVpositive and HPV-negative
oropharyngeal squamous cell
carcinomas, reflecting differences in the
internal consistency of the tumor types.
HPV-
HPV+
Predicting Treatment Response
Initial analysis performed on oropharyngeal squamous cell carcinomas revealed that
certain computer-extracted tumor texture features show trends that correlate with
response to induction chemotherapy in oropharyngeal squamous cell carcinomas.
No response
Response
Selected texture
features for response
to induction
chemotherapy versus
no response.
Selected References
•
•
•
•
•
•
•
•
•
Tourassi GD. Journey toward computer-aided diagnosis: role of image texture analysis.
Radiology 213:317-320, 1999.
Dang M, Lysack JT, Wu T, Matthews TW, Chandarana SP, Brockton NT, et al. MRI Texture
Analysis Predicts p53 Status in Head and Neck Squamous Cell Carcinoma. Am J
Neuroradiol. 36(1):166–70, 2015.
Fujita A, Buch K, Li B, Kawashima Y, Qureshi MM, Sakai O. Difference between HPVpositive and HPV-negative non-oropharyngeal head and neck cancer: texture analysis
features on CT. J Comput Assist Tomogr. 2015 Oct 13.
Mattfeldt T, Fleischer F. Characterization of squamous cell carcinomas of the head and
neck using methods of spatial statistics. J Microsc. 256(1):46-60, 2014.
Fruehwald-Pallamar J, et al. Texture-based analysis of 100 MR examinations of head
and neck tumors - Is it possible to discriminate between benign and malignant masses
in a multicenter trial? Rofo. 2015 Sep 30.
Hoeben BA, et al. Systematic analysis of 18F-FDG PET and metabolism, proliferation
and hypoxia markers for classification of head and neck tumors. BMC Cancer. 14:130,
2014.
Buch K, et al. Using Texture Analysis to Determine Human Papillomavirus Status of
Oropharyngeal Squamous Cell Carcinomas on CT. Am J Neuroradiol. 36(7):1343-1348,
2015.
Lee J. Texture Feature Ratios from Relative CBV Maps of Perfusion MRI Are Associated
with Patient Survival in Glioblastoma. AJNR Am J Neuroradiol. 2016 Jan;37(1):37-43.
Kassner A, Thornhill RE. Texture analysis: a review of neurologic MR imaging
applications. AJNR Am J Neuroradiol. 2010 May;31(5):809-16.
Acknowledgements
•
•
•
•
•
•
Maryellen Giger, PhD
James Melotek, MD
Hui Li, PhD
Kayla Mendel
Tamari Miller
Anup Alexander, MD