**2. Grid computing with globus**

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

Properties Valanis [4] CHEN [5] MIR [6] MOUGIAKAKOU [7] Contrast Entropy Correlation Homogeneity

Energy

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

MRI produces images of the insides of the body. Unlike an X-ray, MRI does not use radiation. Instead, a magnetic field is used to make the body's cells vibrate [1]. The vibrations give off electrical signals which are interpreted and turned into very detailed images of "slices" of the body. MRI may be used to make images of every part of the body, including the bones, joints, blood vessels, nerves, muscles and organs. Different types of tissue show up in different grayscale intensities on a computer-generated image. In this study, series of MRI images were acquired from the Diagnostic Imaging Department of Selayang Hospital, Malaysia, using a Siemens Magnetom Avanto, 1.5T MRI Scanner. The sample liver MRI images (256 X 256 pixels, 12 bps) were acquired consisting of sets of cyst,

Inverse Difference Moment

Table 1. SGLCM properties for second-order statistical measurements. The features

Cluster Tendency

fitness function [8].

**1.1 Image acquisition** 

liver tumor and healthy liver, for training and testing.

Angular Second Moment

successfully examined in prior work are summarized in Table 1 below.

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

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 services such as authentication, job starting and resource discovery.

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 local scheduling activities.

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