Detecting HCC Tumor in Three Phasic CT Liver Images with Optimization of Neural Network

The aim of this work is to build a model based on
tissue characterization that is able to discriminate pathological and
non-pathological regions from three-phasic CT images. With our
research and based on a feature selection in different phases, we are
trying to design a neural network system with an optimal neuron
number in a hidden layer. Our approach consists of three steps:
feature selection, feature reduction, and classification. For each
region of interest (ROI), 6 distinct sets of texture features are
extracted such as: first order histogram parameters, absolute gradient,
run-length matrix, co-occurrence matrix, autoregressive model, and
wavelet, for a total of 270 texture features. When analyzing more
phases, we show that the injection of liquid cause changes to the high
relevant features in each region. Our results demonstrate that for
detecting HCC tumor phase 3 is the best one in most of the features
that we apply to the classification algorithm. The percentage of
detection between pathology and healthy classes, according to our
method, relates to first order histogram parameters with accuracy of
85% in phase 1, 95% in phase 2, and 95% in phase 3.

• Mahdieh Khalilinezhad
• Silvana Dellepiane
• Gianni Vernazza

• Feature selection
• Multi-phasic liver images
• Neural network
• Texture analysis.

[1] M. Gletsos, S. G. Mougiakakou, G. K. Matsopoulos, K. S. Nikita. “A
computer Aided Diagnostic system to characterize CT focal Liver
Lesions: Design and optimization of a neural network classifier,” in
Plastics, 2nd ed. vol. 3, J. Peters, Ed. New York: McGraw-Hill, 1964,
pp. 15–64.
[2] I. Valavanis, S. G. Mougiakakou, K. S. Nikita, and A. Nikita. "Computer
aided diagnosis of CT focal liver lesions by an ensemble of neural
network and statistical classifiers." In Neural Networks, 2004.
Proceedings. 2004 IEEE International Joint Conference on, vol. 3, pp.
1929-1934. IEEE, 2004.
[3] S. Gr Mougiakakou, I. Valavanis, K. S. Nikita, A. Nikita, D. Kelekis,
“Characterization of CT liver Lesions based on Texture Features and a
multiple Neural Network Classification Scheme.” 25th Annual
International Conferences of the IEEE EMNS, Cuncun, Mexico,
September, 2003. [4] D. Smutek, A. Shimizu, H. Kotake, S. Nawano, L.and L. Tesar, “Texture
Analysis of Hepatocellular Carcinoma and Liver Cysts in CT images,”
in SPPRA, 2006, pp.56-59.
[5] S. Su and Y.Sun. "Key techniques research in computer-aided hepatic
lesion diagnosis system based on multi-phase CT images." Image and
Signal Processing (CISP), 2011 4th International Congress on. Vol. 4.
IEEE, 2011
[6] A.Ciurte, S. Nedevschi, “Texture Analysis within Contrast Enhanced
Abdominal CT Images,” Intelligent Computer Communication and
Processing, 2009. ICCP 2009. IEEE 5th International Conference on.
IEEE, 2009.
[7] Y. Jun, Y. Sun, S. Wang, L. Gu, L. Qian, and J. Xu. "Multi-phase CT
image based hepatic lesion diagnosis by SVM." In Biomedical
Engineering and Informatics, 2009. BMEI'09. 2nd International
Conference on, pp. 1-5. IEEE, 2009.
[8] http://www.eletel.p.lodz.pl/programy/mazda/
[9] D. Cavouras, G. Karangellis, M. Raissaki, L. Kostaridou and G.
Panayiotakis. “Application of Neural Network and four statistical
Classifiersin Characterizing Small Focal Liver Lesions on CT.” 18th
Annual International Conference of the IEEE engineering in medicine
and Biology Society, Amesterdam. 1996.
[10] J.Jiang, P. Trundle, J.Ren, “Medical Image Analysis with Artificial
Neural Networks.” Computerized Medical Imaging and Graphics, 2010,
vol. 34, pp. 617-631.
[11] C. C. Lee, C. Y. Shih, (2009, March). “Classification of Liver Disease
from CT Images Using Sigmoid Radial Basis Function Neural Network”.
In Computer Science and Information Engineering, 2009 WRI World
Congress on Vol. 5, pp. 656-660. IEEE.
[12] J. Heaton, “Introduction to Neural Networks for Java”, 2nd Edition.
ISIN: 1604390085.
[13] S. Senturk, B. Cetin, M. Cengiz, A. Bilici and S. Ozekinci, “ Dynamic
Multidetector Computed Tomography Findings of Hepatocellular
Carcinoma of Hepatitis B Virus-positive and – Negative patients,”
Cancer Imaging Journal, vol. 19, no. 9, 2014.