**4.3 Classification for hepatic lesions using neural networks and globus**

The diagnostic value of MRI liver images has become increasingly important in liver disease detection. However, the interpretation effectiveness still relies heavily on experience and skill of the doctors. From the analysis of the SGLCM results obtained, only entropy and correlation are selected for classification for liver tumor and cyst.

276 Grid Computing – Technology and Applications, Widespread Coverage and New Horizons

(a) (b)

(c) (d) Fig. 7. Automated segmentation of image block in a cyst liver boundary using (a) 5 x 5 (b) 8

Table 2 shows the statistical results achieved for entropy calculated based on spatial cooccurrence matrices generated using SGLCM on cysts, tumor and healthy liver of the training set (*T1, T2, …, Tn*). Entropy is consistent within a specific range from 5.174-7.911 for cyst classification and 2.487- 4.291 for tumor classification. In healthy liver, entropy ranges from 0.054-1.954. As the entropy ranges are distinct for each of the 3 categories tested,

Table 3 provides the results for the correlation calculated using SGLCM. As observed, correlation is consistent within a specific range from 5.962-6.997 for cyst and 2.300-4.932 for tumor and 0.071-1.500 for healthy liver. Being different for the 3 categories, correlation may

The statistical results for two more features, homogeneity and contrast, calculated based on SGLCM on healthy liver ROI were inconsistent as shown in Table 4 and Table 5. As all the ranges for the 3 categories overlap, these features cannot be used to classify the liver MRI

The diagnostic value of MRI liver images has become increasingly important in liver disease detection. However, the interpretation effectiveness still relies heavily on experience and skill of the doctors. From the analysis of the SGLCM results obtained, only entropy and

x 8 (c) 10 x 10 image block (d) shows the liver region in MRI abdomen image.

entropy could be a suitable feature for successful liver lesions classification.

**4.3 Classification for hepatic lesions using neural networks and globus** 

correlation are selected for classification for liver tumor and cyst.

also be deemed a suitable classification feature.

images.


Table 2. Entropy results for cyst, tumor and healthy liver.


Table 3. Correlation results for cyst, tumor and healthy liver.

Characterization of Hepatic Lesions

**5. Conclusion** 

**6. References** 

liver tumor in the liver tissue.

1934, 2004.

Using Grid Computing (Globus) and Neural Networks 279

The texture features obtained were then applied to the NN classifier and Globus automated scheduling for the detection. The final decisions of the NN classifier was generated by combining the diagnostic output using the input layer consisting of a number of input neurons equal to the number of features fed into the NN. (i.e. 2, namely entropy and correlation. Training and testing of the NN classification was based on the use of sample

> Entropy Correlation Min Max Min Max Cyst 5.174 7.911 5.962 6.997 Tumor 2.487 4.291 2.300 4.932 Healthy 0.054 1.954 0.071 1.500

In the approach described above, it should be noted that, resolution, ROI image block size and sampling space used for calculation of SGLCM are important considerations in statistical feature extraction. The present study has shown promising results in the use of texture for the extraction of diagnostic information from MR images of the liver. Two features were selected using SGLCM, namely entropy and correlation, whilst it was shown that homogeneity and contrast were unsuitable to differentiate between cyst, tumor and healthy liver. In our experiment, the same features were used as input to the NN with the aid of Globus automated scheduling for hepatic liver tissue characterization of MRI images. In particular, this paper provides results of successful preliminary diagnosis of cyst and

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[3] S. Kitaguchi, S. Westland and M. R. Luo, "Suitability of Texture Analysis Methods for

[4] Valanis, S. G. Mougiakakou, K. S. Nikita and A. Nikita, "Computer-aided Diagnosis of

[5] E. L. Chen, P. C. Chung, C. L. Chen, H. M. Tsai and C. L. Chang, "An automatic

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MRI abdomen images for all 3 categories as observed in Table 6.

Table 6. Classification of hepatic lesions using entropy and correlation.


Table 4. Contrast Results for cyst, tumor and healthy liver.


Table 5. Homogeneity results for cyst, tumor, and healthy liver.

The texture features obtained were then applied to the NN classifier and Globus automated scheduling for the detection. The final decisions of the NN classifier was generated by combining the diagnostic output using the input layer consisting of a number of input neurons equal to the number of features fed into the NN. (i.e. 2, namely entropy and correlation. Training and testing of the NN classification was based on the use of sample MRI abdomen images for all 3 categories as observed in Table 6.


Table 6. Classification of hepatic lesions using entropy and correlation.
