Neural Network based Texture Analysis of Liver Tumor from Computed Tomography Images
Advances in clinical medical imaging have brought about the routine production of vast numbers of medical images that need to be analyzed. As a result an enormous amount of computer vision research effort has been targeted at achieving automated medical image analysis. Computed Tomography (CT) is highly accurate for diagnosing liver tumors. This study aimed to evaluate the potential role of the wavelet and the neural network in the differential diagnosis of liver tumors in CT images. The tumors considered in this study are hepatocellular carcinoma, cholangio carcinoma, hemangeoma and hepatoadenoma. Each suspicious tumor region was automatically extracted from the CT abdominal images and the textural information obtained was used to train the Probabilistic Neural Network (PNN) to classify the tumors. Results obtained were evaluated with the help of radiologists. The system differentiates the tumor with relatively high accuracy and is therefore clinically useful.
[1] Heiken J.P, Wegman P.J and Lee J.K.T, "Detection of focal hepatic
masses: Prospective evaluation with CT, delayed CT, CT during arterial
portography and MR imaging", Radiology, vol. 171, pp. 47-51, 1989.
[2] E-Liang Chen, Pau-CHoo Chung, Ching-Liang Chen, Hong-Ming Tsai
and Chein I Chang, "An Automatic Diagnostic system for CT Liver
Image Classification", IEEE Transactions Biomedical Engineering, vol
45, no. 6, pp. 783-794, June 1998.
[3] Chung-Ming Wu, Yung-Chang Chen, Kai-Sheng Hsieh, "Texture
features for Classification of Ultrasound Liver Images", IEEE
Transactions on Medical Imaging, vol 11, no 2, pp. 141-152, June
1992.
[4] Yasser M. Kadah, Aly A. Farag, Jacek M. Zaruda, Ahmed M. Badawi,
and Abou-Bakr M. Youssef., "Classification Algorithms for
Quantitative Tissue Characterization of Diffuse Liver Disease from
Ultrasound Images," IEEE transactions on Medical Imaging Vol 15, No
4, pp 466-477, August 1996.
[5] Aleksandra Mojsilovic, Miodrag Popovic, Srdjan Markovic and
Miodrag Krstic, "Characterization of Visually Similar Diffuse Diseases
from B-Scan Liver Images using Non Separable Wavelet Transform",
IEEE Transactions on Medical Imaging, vol. 17, no. 4, pp. 541-549,
Aug. 1998.
[6] E-Liang Chen, Pau-CHoo Chung, Ching-Liang Chen, Hong-Ming Tsai
and Chein I Chang, "An Automatic Diagnostic system for CT Liver
Image Classification", IEEE Transactions Biomedical Engineering, vol
45, no. 6, pp. 783-794, June 1998.
[7] Pavlopoulos.S, Kyriacou.E, Koutsouris.D, Blekas.K, Stafylopatis.A,
Zoumpoulis.P, "Fuzzy Neural Network-Based Texture Analysis of
Ultrasonic Images," IEEE Engineering in Medicine and Biology, pp
39-47, Feb 2000.
[8] Jae-Sung Hong, Toyohisa Kaneko, Ryuzo Sekiguchi and Kil-
Houmpark, "Automatic Liver Tumor Detection from CT", IEICE Trans.
Inf.& Syst.,Vol. E84-D, No. 6, pp 741-748, June 2001.
[9] Gletsos Miltiades, Stavroula G Mougiakakou, George K. Matsopoulos,
Konstantina S Nikita, Alexandra S Nikita, Dimitrios Kelekis, "A
Computer-Aided Diagnostic System to Characterize CT Focal Liver
Lesions: Design and Optimization of a Neural Network Classifier,"
IEEE Transactions on Information Technology in BioMedicine, Vol 7,
Issue 3, pp 153 - 162, Sep. 2003.
[10] John.E.Koss, F.D.Newman FD, T.K.Johnson, and D.L.Kirch,
"Abdominal Organ Segmentation Using Texture Transforms and a
Hopfield Neural Network", IEEE Transactions on Medical Imaging,
vol 18, no. 7, pp 640-648, July 1999.
[11] Chien Cheng Lee, Pau-Choo Chung, Hong-Ming Tsai, "Identifying
Abdominal organs from CT image series using a Multimodule
Contextual Neural network and Spatial Fuzzy rules", IEEE
Transactions on Information Technology in Biomedicine, vol 7, no. 3,
pp. 208-217, Sep. 2003.
[12] Lee.W.L, Chen.Y.C, and Hsei.K.S., "Ultrasonic liver tissues
classification by fractal feature vector based on M-band wavelet
transform," IEEE Trans. Med. Imaging, vol. 22, pp. 382-392, Mar.
2003.
[13] Yu-Len Huang, Jeon-Hor Chen1, Wu-Chung Shen1 "Computer-Aided
Diagnosis of Liver Tumors in Non-enhanced CT Images" Department
of Computer Science and Information Engineering, Tunghai University,
Mid Taiwan, Journal of Medical Physics, Vol. 9, pp. 141-150, 2004.
[14] Robert M Haralick, Stanely R Sternberg and Xinhua Zhuang, "Image
analysis using Mathematical Morphology", IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol. PAMI-9, no. 4, pp.
532-549, July, 1987.
[15] Robert.M, Haralick, K. Shanmugam, Dinstein, "Texture Features for
Image Classification", IEEE Transactions on Systems, Man, and
Cybernetics, Vol.SMC-3, No. 6, pp 610-621, Nov. 1973.
[16] A.Gagalowicz, "A new method for texture field synthesis: Some
applications to the study of human vision", IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol. PAMI-3, pp. 520-533,
1982.
[17] G.O. Lendaris and G. L. Stanley, "Diffraction pattern sampling for
automatic pattern recognition," Proceedings. IEEE, vol. 58, pp. 198-
216, 1970.
[18] J.S. Weszka, C.R. Dryer, and A. Rosenfeld, "A comparative study of
texture measures for terrain classification," IEEE Trans. Syst.,
Man,Cybern., vol. SMC-6, pp. 269-285, 1976
[19] K. I. Laws, "Texture energy measures," Proc. Image Understanding
Workshop, pp. 47-51, 1979.
[20] Van.G. Wouwer, P. Scheunders, and D. Van Dyck, "Statistical texture
characterization from discrete wavelet representations," IEEE
Trans.Image Processing, vol. 8, pp. 592-598, Apr. 1999.
[21] Mallat.S.G.,"A theory for multiresolution signal decomposition: A
wavelet representation", IEEE Transactions on Pattern Analysis and
Machine Intelligence. Vol. 11, pp 674-693, July 1989.
[22] P. P. Raghu and B. Yegnanarayana, "Supervised Texture Classification
Using a Probabilistic Neural Network and Constraint Satisfaction
Model, IEEE Transactions on Neural Networks, vol. 9, no. 3,pp 516-
522, May 1998
[23] Sujana.H, S. Swarnamani, and S. Suresh, "Artificial neural networks
for the classification of liver lesions by image texture parameters,"
Ultrasound Med. Biol., vol. 22, pp. 1177-1181, Sept. 1996.
[24] Donald.F.Specht, "Probabilistic neural networks", Neural Networks,
vol. 3, pp. 109-118, 1990.
[25] Donald.F.Specht, "Probabilistic neural networks and the Polynomial
Adaline as complementary techniques for pattern classification", IEEE
Transactions on Neural Networks, vol 1, no 1, pp 111 - 121, March
1990.
[26] www.dacs.dtic.mil/techs/neural/neural7.html
[1] Heiken J.P, Wegman P.J and Lee J.K.T, "Detection of focal hepatic
masses: Prospective evaluation with CT, delayed CT, CT during arterial
portography and MR imaging", Radiology, vol. 171, pp. 47-51, 1989.
[2] E-Liang Chen, Pau-CHoo Chung, Ching-Liang Chen, Hong-Ming Tsai
and Chein I Chang, "An Automatic Diagnostic system for CT Liver
Image Classification", IEEE Transactions Biomedical Engineering, vol
45, no. 6, pp. 783-794, June 1998.
[3] Chung-Ming Wu, Yung-Chang Chen, Kai-Sheng Hsieh, "Texture
features for Classification of Ultrasound Liver Images", IEEE
Transactions on Medical Imaging, vol 11, no 2, pp. 141-152, June
1992.
[4] Yasser M. Kadah, Aly A. Farag, Jacek M. Zaruda, Ahmed M. Badawi,
and Abou-Bakr M. Youssef., "Classification Algorithms for
Quantitative Tissue Characterization of Diffuse Liver Disease from
Ultrasound Images," IEEE transactions on Medical Imaging Vol 15, No
4, pp 466-477, August 1996.
[5] Aleksandra Mojsilovic, Miodrag Popovic, Srdjan Markovic and
Miodrag Krstic, "Characterization of Visually Similar Diffuse Diseases
from B-Scan Liver Images using Non Separable Wavelet Transform",
IEEE Transactions on Medical Imaging, vol. 17, no. 4, pp. 541-549,
Aug. 1998.
[6] E-Liang Chen, Pau-CHoo Chung, Ching-Liang Chen, Hong-Ming Tsai
and Chein I Chang, "An Automatic Diagnostic system for CT Liver
Image Classification", IEEE Transactions Biomedical Engineering, vol
45, no. 6, pp. 783-794, June 1998.
[7] Pavlopoulos.S, Kyriacou.E, Koutsouris.D, Blekas.K, Stafylopatis.A,
Zoumpoulis.P, "Fuzzy Neural Network-Based Texture Analysis of
Ultrasonic Images," IEEE Engineering in Medicine and Biology, pp
39-47, Feb 2000.
[8] Jae-Sung Hong, Toyohisa Kaneko, Ryuzo Sekiguchi and Kil-
Houmpark, "Automatic Liver Tumor Detection from CT", IEICE Trans.
Inf.& Syst.,Vol. E84-D, No. 6, pp 741-748, June 2001.
[9] Gletsos Miltiades, Stavroula G Mougiakakou, George K. Matsopoulos,
Konstantina S Nikita, Alexandra S Nikita, Dimitrios Kelekis, "A
Computer-Aided Diagnostic System to Characterize CT Focal Liver
Lesions: Design and Optimization of a Neural Network Classifier,"
IEEE Transactions on Information Technology in BioMedicine, Vol 7,
Issue 3, pp 153 - 162, Sep. 2003.
[10] John.E.Koss, F.D.Newman FD, T.K.Johnson, and D.L.Kirch,
"Abdominal Organ Segmentation Using Texture Transforms and a
Hopfield Neural Network", IEEE Transactions on Medical Imaging,
vol 18, no. 7, pp 640-648, July 1999.
[11] Chien Cheng Lee, Pau-Choo Chung, Hong-Ming Tsai, "Identifying
Abdominal organs from CT image series using a Multimodule
Contextual Neural network and Spatial Fuzzy rules", IEEE
Transactions on Information Technology in Biomedicine, vol 7, no. 3,
pp. 208-217, Sep. 2003.
[12] Lee.W.L, Chen.Y.C, and Hsei.K.S., "Ultrasonic liver tissues
classification by fractal feature vector based on M-band wavelet
transform," IEEE Trans. Med. Imaging, vol. 22, pp. 382-392, Mar.
2003.
[13] Yu-Len Huang, Jeon-Hor Chen1, Wu-Chung Shen1 "Computer-Aided
Diagnosis of Liver Tumors in Non-enhanced CT Images" Department
of Computer Science and Information Engineering, Tunghai University,
Mid Taiwan, Journal of Medical Physics, Vol. 9, pp. 141-150, 2004.
[14] Robert M Haralick, Stanely R Sternberg and Xinhua Zhuang, "Image
analysis using Mathematical Morphology", IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol. PAMI-9, no. 4, pp.
532-549, July, 1987.
[15] Robert.M, Haralick, K. Shanmugam, Dinstein, "Texture Features for
Image Classification", IEEE Transactions on Systems, Man, and
Cybernetics, Vol.SMC-3, No. 6, pp 610-621, Nov. 1973.
[16] A.Gagalowicz, "A new method for texture field synthesis: Some
applications to the study of human vision", IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol. PAMI-3, pp. 520-533,
1982.
[17] G.O. Lendaris and G. L. Stanley, "Diffraction pattern sampling for
automatic pattern recognition," Proceedings. IEEE, vol. 58, pp. 198-
216, 1970.
[18] J.S. Weszka, C.R. Dryer, and A. Rosenfeld, "A comparative study of
texture measures for terrain classification," IEEE Trans. Syst.,
Man,Cybern., vol. SMC-6, pp. 269-285, 1976
[19] K. I. Laws, "Texture energy measures," Proc. Image Understanding
Workshop, pp. 47-51, 1979.
[20] Van.G. Wouwer, P. Scheunders, and D. Van Dyck, "Statistical texture
characterization from discrete wavelet representations," IEEE
Trans.Image Processing, vol. 8, pp. 592-598, Apr. 1999.
[21] Mallat.S.G.,"A theory for multiresolution signal decomposition: A
wavelet representation", IEEE Transactions on Pattern Analysis and
Machine Intelligence. Vol. 11, pp 674-693, July 1989.
[22] P. P. Raghu and B. Yegnanarayana, "Supervised Texture Classification
Using a Probabilistic Neural Network and Constraint Satisfaction
Model, IEEE Transactions on Neural Networks, vol. 9, no. 3,pp 516-
522, May 1998
[23] Sujana.H, S. Swarnamani, and S. Suresh, "Artificial neural networks
for the classification of liver lesions by image texture parameters,"
Ultrasound Med. Biol., vol. 22, pp. 1177-1181, Sept. 1996.
[24] Donald.F.Specht, "Probabilistic neural networks", Neural Networks,
vol. 3, pp. 109-118, 1990.
[25] Donald.F.Specht, "Probabilistic neural networks and the Polynomial
Adaline as complementary techniques for pattern classification", IEEE
Transactions on Neural Networks, vol 1, no 1, pp 111 - 121, March
1990.
[26] www.dacs.dtic.mil/techs/neural/neural7.html
@article{"International Journal of Medical, Medicine and Health Sciences:54720", author = "K.Mala and V.Sadasivam and S.Alagappan", title = "Neural Network based Texture Analysis of Liver Tumor from Computed Tomography Images", abstract = "Advances in clinical medical imaging have brought about the routine production of vast numbers of medical images that need to be analyzed. As a result an enormous amount of computer vision research effort has been targeted at achieving automated medical image analysis. Computed Tomography (CT) is highly accurate for diagnosing liver tumors. This study aimed to evaluate the potential role of the wavelet and the neural network in the differential diagnosis of liver tumors in CT images. The tumors considered in this study are hepatocellular carcinoma, cholangio carcinoma, hemangeoma and hepatoadenoma. Each suspicious tumor region was automatically extracted from the CT abdominal images and the textural information obtained was used to train the Probabilistic Neural Network (PNN) to classify the tumors. Results obtained were evaluated with the help of radiologists. The system differentiates the tumor with relatively high accuracy and is therefore clinically useful.
", keywords = "Fuzzy c means clustering, texture analysis, probabilistic neural network, LVQ neural network.", volume = "2", number = "1", pages = "20-8", }
