Study of Classification of Breast Lesions Using Texture GLCM Features Obtained From the Raw Ultrasound Signal
DOI:
https://doi.org/10.5566/ias.2113Keywords:
breast lesion classification, quantitative ultrasound, feature selection, texture analysis, stepwise logistic regressionAbstract
Most of the methods of classification of breast lesions in ultrasound (US) images have been tested on B-mode images from the commercial equipment. The new possibility of further analysis of this problem showed up with the availability of a public database containing original raw radio frequency (RF) signals. In particular, it appeared that the original texture might contain diagnostic information which could be modified in the typical image processing and which is more difficult to perceive than the details of lesion shape/contour. In this paper a detailed analysis of the lesion texture is conducted by means of the decision trees and logistic regression. The decision trees turned out useful mainly for selecting texture features to be employed in the stepwise logistic regression. The RF signals database of 200 breast lesions was used for testing the performance of the benign vs malignant lesion classifier. The Gray Level Cooccurrence Matrix (GLCM) was calculated with the vertical/horizontal offset of up to five pixels. For each of these matrices six features were calculated resulting in a total of 210 features. Using these features a sufficient number of decision trees were generated to calculate pseudo-Receiver Operating Characteristics (ROCs). The outcome of this process is a collection of generated trees for which the employed features are known. These features were then used for generating generalized linear model by means of stepwise logistic regression. The analyzed regression models included the coefficients of up-to-the second degree terms. The texture features were further completed by a single shape feature, that is tumor circularity. The automatic procedure for finding the exact mask of a lesion is also provided for the conditions when the acoustic shadowing makes it impossible to obtain the entire contour reliably and a half-contour has to be used. The selected logistic regression models gave ROCs with the Area Under Curve (AUC) of up to 0.83 and the 95 % confidence region (0.63 0.96). Analyzing classification results one comes to the conclusion that the tumor circularity, which is the most informative among shape/contour features, is not essential against the background of textural features. The reported study shows that a relatively straightforward procedure can be employed to obtain benign vs malignant classifier comparable with that originally used for the database of the raw RF signals and based on the more complicated segmentation of the parameter maps of homodyned K distribution.
References
Adler, Lausen B (2009). Bootstrap estimated true and
false positive rates and ROC curve. Computat
Statistics Data Anal 53:718-29.
Alacam B, Yazici B, Bilgutay N (2003). Breast tissue
characterization based on ultrasound RF echo
modeling and tumor morphology. In: Proc 25th
Annu Int Conf IEEE EMBS. pp. 1180-83.
Alacam B, Yazici B, Bilgutay N, Forsberg F,
Piccoli C (2004). Breast tissue characterization
using FARMA modeling of ultrasonic RF echo.
Ultrasound Med Biol 10:1397-1407.
Alvarenga AV, Infantosi AFC, Pereira WCA, Azevedo
CM (2012). Assessing the combined performance
of texture and morphological parameters in
distinguishing breast tumors in ultrasound images.
Med Phys 39:7350-58.
Bingham NH, Fry JM (2010). Regression. Linear
Models in Statistics. London: Springer.
Byra M, Nowicki A, Piotrzkowska-Wróblewska
H, Dobruch-Sobczak K (2016). Classification
of breast lesions using segmented quantitative
ultrasound maps of homodyned K distribution
parameters. Med Phys 43:5561-69.
Byra M, Dobruch-Sobczak K, Piotrzkowska-
Wróblewska H, Nowicki A (2017). Added value of
morphological features to breast lesion diagnosis
in ultrasound, http://arxiv.org/abs/1706.01855
Byra M (2018). Discriminant analysis of neural style
representations for breast lesion classification in
ultrasound. Biocybernetics Biomed Eng 38:684–90.
Byra M, Sznajder T, Koržinek D, Piotrzkowska-
Wróblewska H, Dobruch-Sobczak K, Nowicki
A, Marasek K (2018a). Impact of ultrasound
image reconstruction method on breast lesion
classification with neural transfer learning,
http://arxiv.org/abs/1804.02119v1
Chen D-R, Kuo W-J, Chang R-F, Moon WK, Lee CC
(2002). Use of the bootstrap technique with small
training sets for computer-aided diagnosis in breast
ultrasound. Ultrasound Med Biol 28:897-902.
Cheng HD, Shan J, Ju W, Guo Y, Zhang L
(2010). Automated breast cancer detection and
classification using ultrasound images: a survey.
Pattern Recogn 43:299-317.
Drukker K, Giger ML, Mendelson EB (2003).
Computerized analysis of shadowing on breast
ultrasound for improved lesion detection. Med
Phys 30:1833-42.
Efron B, Tibshirani R (1998). An Introduction to the
Bootstrap. Boca Raton: Chapman and Hall/CRC.
Gokhale S (2009). Ultrasound characterization of
breast masses. Indian J Radiol Imaging. 19:242–47.
Gonzalez RC, Woods RE, Eddins SL (2009). Digital
Image Processing Using MATLAB. Gatesmark
Publishing.
Granchi S, Vannacci E, Biagi E, Masotti L
(2015). Differentiation of breast lesions by
use of hyperspace: hyper-spectral analysis for
characterization in echography. Ultrasound Med
Biol 41:1967-80.
Harvey P, Arger PH, Conant EF, Sehgal CM
(2009). Differentiation of solid benign and
malignant breast masses by quantitative analysis
of ultrasound images. In: Proc IEEE Ultrasonics
Symposium. Department of Computer Science,
University of Copenhagen. pp. 530-33.
Hastie T, Tibshirani R, Friedman J (2009). The
Elements of Statistical Learning. New York:
Springer.
Kleinbaum DG, Klein M (2010). Logistic Regression.
New York: Springer.
Kotsiantis SB (2013). Decision trees: a recent
overview. Artif Intell Rev 39:261-83.
Lee H-W, Liu B-D, Hung KI-C, Lei S-F, Wang P-C,
Yang T-L (2009). Breast tumor classification
of ultrasound images using wavelet-based channel
energy and imageJ. IEEE J Select Topics Image
Proc 3:81-93.
Lizzi F, Astor M, Feleppa EJ, Shao M, Kalisz A
(1997). Statistical framework for ultrasonic
spectral parameter imaging. Ultrasound Med Biol
:1371-82.
McCullagh P, Nelder JA (1989). Generalized Linear
Models. London: Chapman & Hall/CRC.
Menon RV, Raha P, Chakrabarti I (2016).
Classification of breast mass in ultrasound
images using CAD: a survey. In: Proc Int Conf
Systems in Medicine and Biology. IIT Kharagpur.
EMBS, IEEE. pp. 31-35.
Minavathi M, Murali S, Dinesh MS (2012).
Classification of mass in breast ultrasound
images using image processing techniques. Int J
Comput Applic 42:29-36.
Moon WK, Lo C-M, Cho N, Chang JM, Huang
C-S, Chen J-H, Chang R-F (2013). Computer-
aided diagnosis of breast masses using quantified
BI-RADS findings. Computer Methods Programs
Biomed 111:84-92.
Nemat H, Fehri H, Ahmadinejad N, Frangi AF,
Gooya A (2018). Classification of breast lesions
in ultrasonography using sparse logistic regression
and morphology-based texture features. Med Phys
:4113-24.
Nieniewski M, Zajączkowski P (2014). Real-time
speckle reduction in ultrasound images by means
of nonlinear coherent diffusion using GPU. In:
Proc Int Conf Comput Vis Graphics. LNCS No.
, Springer. pp. 462-469.
Padilla PP, Bernardo DC, Encinas MAO, Marcos R,
Castro AH, Palacios AL (2013). Ultrasound non-
neoplastic breast lesions with posterior acoustic
shadowing, http://dx.doi.org/10.1594/ecr2013/C-
Piotrzkowska-Wróblewska H, Nowicki A, Litniewski
J, Gambin B, Dobruch-Sobczak K (2014).
Breast carcinoma tissues characterization
using statistics of ultrasonic backscatter.
In: 7th Forum Acusticum 2014, 9 pages,
http://www.ippt.pan.pl/en/staff/hpiotrzk
Piotrzkowska-Wróblewska H, Dobruch-Sobczak K,
Byra M, Nowicki A (2017). Open access database
of raw ultrasonic signals acquired from malignant
and benign breast lesions. Med Phys 44:6105-09.
Pratiwi M, Alexander, Harefa J, Nanda S (2015).
Mammograms classification using gray-level co-
occurrence matrix and radial basis function neural
network. Procedia Comput Science 59:83-91.
Sadeghi-Naini A, Suraweera H, Tran WT, Hadizad
F, Bruni G, Rastegar RF, Curpen WT, Czarnota
GJ (2017). Breast-lesion characterization using
textural features of quantitative ultrasound
parametric maps. Scientific Reports 7:1-10,
http://www.nature.com/scientificreports
Uniyal N, Eskandri H, Abolmaesumi P, Sojoudi S,
Gordon P, Warren L, Rohling RN, Salcudean SE,
Moradi M (2015). Ultrasound RF time series for
classification of breast lesions. IEEE Trans Med
Imag 34:652-61.
Walach E, Kisilev P, Chevion D, Barkan E, Harary
S, Hashaul S, Ben-Horesh A, Tzadok A (2013).
A fully automatic lesion classification in breast
ultrasound. In: Workshop Breast Image Analysis
in conjunction with MICCAI 2013. Technical Report
No 01/2013. Department of Computer Science,
University of Copenhagen. pp. 98-105.
Zakeri FS, Behnam H, Ahmadinejad N (2012).
Classification of benign and malignant breast
masses based on shape and texture features in
sonography images. J Med Syst 36:1621-27.
Zhou Z, Wu S, Chang K-J, Chen W-R, Chen Y-S, Kuo
W-H, Lin C-C, Tsui P-H (2015). Classification of
benign and malignant breast tumors in ultrasound
images with posterior acoustic shadowing using
half-contour features. J Med Biol Eng 35:178-87.
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