**1. Introduction**

20 Will-be-set-by-IN-TECH

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

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Magnetic Resonance Imaging (MRI) images have been widely used for liver disease diagnosis. Designing and developing computer-assisted image processing techniques to help doctors improve their diagnosis has received considerable interest over the past years. In this paper, a computer-aided diagnostic (CAD) system for the characterization of hepatic lesions, specifically cyst and tumor as well as healthy liver, from MRI images using texture features and implementation of grid computing (Globus approach) and neural networks (NN) is presented. Texture analysis is used to determine the changes in functional characteristics of organs at the onset of a liver disease, Region of interest (ROI) extracted from MRI images are used as the input to characterize different tissue, namely liver cyst and healthy liver using first-order statistics. The results for first-order statistics are given and their potential applicability in grid computing is discussed. The measurements extracted from First-order statistic include entropy and correlation achieved obvious classification range in detecting different tissues in this work.

In this chapter, texture analysis of liver MRI images based on the Spatial Grey Level Cooccurrence Matrix (SGLCM) [3] is proposed to discriminate normal, malignant hepatic tissue (i.e. liver tumor) and cysts in MRI images of the abdomen. SGLCM, also known as Grey Tone Spatial Dependency Matrix [3], is a tabulation of how often different combinations of pixel brightness values (i.e. grey-level) occur in an image. Regions of interest (ROI) from cysts, tumor and healthy liver were used as input for the SGLCM calculation. Second order statistical texture features estimated from the SGLCM are then applied to a Feed-forward Neural Network (FNN) and Globus toolkit for the characterization of suspected liver tissue from MRI images for hepatic lesions classification. This project proposed an automated distributed processing framework for high-throughput, large-scale applications targeted for characterization of liver texture statistical measurements mainly healthy liver, fatty liver, liver cyst for MRI (Magnetic Resonance Imaging) images.

Table 1 lists eight second-order statistical calculations based on SGLCM, namely, contrast, entropy, correlation, homogeneity, cluster tendency, inverse difference moment, energy, and angular second moment, which have shown useful results in hepatic lesions classification for liver tumor using Computed Tomography (CT), Ultrasonography (US) and

Characterization of Hepatic Lesions

local scheduling activities.

**2. Grid computing with globus** 

Using Grid Computing (Globus) and Neural Networks 269

Grid Computing describes computation in which jobs are run on a distributed computational unit spanning two or more administrative domains. It has sparked tremendous excitement among scientists worldwide and has renewed the interest of the scientific community toward

The Globus toolkit [4] was created in the late 1990s as part of a joint research project between Argonne National Laboratory and the Information Sciences Institute at the University of Southern California. Its aim was to provide a solution to the computational needs of large virtual organizations [4] that span multiple institutional and administrative domains. Globus is a middleware toolkit that provides fundamental distributed computing

Globus provides a collection of services [5] including: GSI, Grid Security Infrastructure which provides authentication based on a Certificate Authority trust model; GRAM, Grid Resource Allocation Manager which handles job starting or submission; GridFTP, providing extensions to the FTP standard to provide GSI authentication and high performance transfer; MDS, Monitoring and Discovery Service enabling remote resource discovery.

By itself Globus does not provide all of the tools and services required to implement a full featured distributed computing environment. Additional tools are available to fill some of the gaps. The National Center for Supercomputing Applications (NCSA) provides a patch to add GSI authentication to OpenSSH. This allows Globus environments to have terminal based single-signon. Globus does not provide any scheduling functionality, but rather relies on the client operating system scheduler or batch schedulers such as OpenPBS [6] to handle

Global scheduling between Globus processes can be provided by meta-schedulers, such as Condor-G [6]. Condor-G submits jobs to the GRAM service running on Globus nodes and

The SGLCM aspect of texture is concerned with the spatial distribution and spatial dependence among the grey levels in a local area. This concept was first used by Julesz [9] in texture discrimination experiments. Being one of the most successful methods for texture discrimination at present, we have investigated its effectiveness for use with MRI images in the present work. This method is based on the estimation of the second order joint

where *θ* = 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°.Each *f(i,j|d,θ) i*s the probability of going from grey level *i* to grey level *j*, given that the inter-sample spacing is *d* and the direction is given by the angle *θ*. The estimated value for these probability density functions

*f (i, j|d, )* (1)

*(d, ) [ f (i, j,|d, )]* . (2)

GRAM handles the task of submitting the job to the local scheduling system.

**3. Spatial grey level co-occurrence matrices** 

conditional probability density function [10]

can thus be written in matrix form [11]

distributed computing, an area which was almost forgotten during the 90's.

services such as authentication, job starting and resource discovery.


Table 1. SGLCM properties for second-order statistical measurements. The features successfully examined in prior work are summarized in Table 1 below.

MRI. The measurements identified in various approaches are indicated by a tick. The SGLCM approach undertaken by Valanis et al. [4] was to classify three hepatic tissues: normal, hemangeoma and hepatocellular carcinoma on CT images with a resolution of 512 X 512 pixels and 8 bits per pixel (bpp) (256 grey levels). Correlation, inverse difference moment and cluster tendency were shown in the paper to achieve classification rates of up to 90.63% after being applied with feature selection based on a Genetic Algorithm (GA) approach. Of particular interest is an approach by Chen [5], using a modified probabilistic neural network (MPNN) to classify liver tumor, hepatoma and hemangeoma on CT images with 12 bpp representing 4096 grey levels and resolution of 320 X 320 pixels. The entropy and correlation showed better performance than other features extracted from co-occurrence matrices at directions θ = 0°, 45°, 90° and 135°, resulting in a classification rate of 83% where the misclassification resulted from the tumor matrices block size. The classification rate could be increased by reducing the block size. Another approach was by Mir [6] to classify normal and malignant liver on 256 X 256 pixels CT images. Entropy and local homogeneity were found to be consistent within a class and most appropriate for discrimination of the malignant and normal liver. Mougiakakou [7] implemented an automated CAD system for characterization of liver CT images into cysts, hepatoma and hemangeoma using a multiple NN classification scheme. Contrast, entropy, correlation and homogeneity were the identified features based on feature selection using the Squared Mahalanobis Distance as the fitness function [8].
