A Grid based Medical Image Retrieval System using Alchemi

F. Maghraby

, H. M. Faheem

, M.

Roushdy

and M. Amoon

Higher Institute of Computer and Information Technology, ELShorouk Academy, ELShorouk City, Egypt

Faculty of Computer and Information Sciences, Ain Shams University, Abbassia, Cairo, Egypt

Faculty of Electronic Engineering, Menoufia University, Shebeen El-Kom, Menufia, Egypt

Keywords: Alchemi, Database Partitioning, Dicom, Grid Computing, Semantic Features.

Abstract: This paper proposes an approach to perform retrieval process on medical image databases by extracting

semantic information from the dataset values of the DICOM (Digital Imaging and Communications in

Medicine) format which produces a set of images relevant to the given query. Image retrieval in general has

the goal to allow for the retrieval of similar images over very heterogeneous image collections to help the

diagnostic process. With modern radiology, departments produce tens of thousands of images per day. It is

apparent that infrastructures are required to treat this large amount of data. Grid technologies are among

those approaches deployed to make computing power available to large-scale research projects. Often, the

goal is to have a very large number of resources in various locations that can be shared for performing

computationally intensive tasks. Grid computing has the potential to help computer science researchers in

medical institutions to better use an existing infrastructure. It shows that particularly computationally–

intensive tasks such as the extraction of features from large image databases can be performed much faster.

Alchemi framework has been deployed in this paper to provide grid-based environment .Speeding up the

retrieval process was one of the major achievements of this work.

1 INTRODUCTION

Computer grids are promising architectures with a

strong potential for sharing resources. They are

generally valued for the large computing power and

data storage space they provide. Beyond this

interest, grid technologies allow scientists federated

in Virtual Organizations (VOs) to easily share

datasets and algorithms across boundaries of their

organizations. All these grid characteristics make

them particularly interesting for the medical

community who deals with large and fragmented

amounts of medical images. As a consequence,

various medical images simulation, storage, and

processing applications have recently been

developed on grids (

Montagnat et al., 2004b). The

problem of large scale image indexing and retrieval

remains relevant for many of them.

The proposed system uses Alchemi which is an

open source software framework that can be

deployed to aggregate the computing power of

networked machines into a virtual supercomputer

(desktop grid) and to develop applications to run on

the grid. The proposed system uses the DICOM

information for performing the retrieval on medical

images. The retrieval is performed by extracting

semantic features from the dataset values of the

DICOM format. The extracted information can be

used to perform the retrieval which produces a set of

images relevant to the given query.

The rest of this paper is organized as follows:

section 2 provides a brief introducing to general grid

computing principles. Section3 explains database

partitioning on grid. Section 4 discusses content

based image retrieval. Section 5 presents our

proposed system and its modules. Section 6

discusses the experimental results. Section 7

provides some concluding remarks.

2 GRID ENVIRONMENT

Computer grids consist of a network of computers

providing distributed computing and storage

resources to their users through a grid middleware.

The middleware is the software layer implementing

basic services to access a grid infrastructure and

hiding the system complexity to the user (Camarasu

224

Maghraby F., M. Faheem H., Roushdy M. and Amoon M..

A Grid based Medical Image Retrieval System using Alchemi.

DOI: 10.5220/0004448202240230

In Proceedings of the 15th International Conference on Enterprise Information Systems (ICEIS-2013), pages 224-230

ISBN: 978-989-8565-59-4

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

et al., 2008). Alchemi is one of the Software

frameworks to enable grid computing. It has been

primarily written for UNIX operating systems.

However, Microsoft .Net framework provides a

platform to implement windows based grid

computing environment with Alchemi as shown in

Figure1. In particular, it also provides remote

execution, security, multithreading,, asynchronous

programming, disconnected data access, and

managed execution. This makes it an ideal platform

for grid computing middleware (Dhivya and Ruba,

2012).

Figure 1: Layered Architecture of distributed Windows.

2.1 Architecture

Alchemi (Luther et al., 2005) follows the master-

worker parallel programming paradigm in which a

central component dispatches independent units of

parallel execution to workers and manages them.

This smallest unit of parallel execution is a grid

thread. A grid application is defined simply as an

application that is to be executed on a grid and that

consists of a number of grid threads. Grid

applications and grid threads are exposed to the grid

application developer via the object- oriented

Alchemi .NET API. Alchemi tool describes the four

components. They are:

 Manager

 Executor

 Cross-platform manager

 Owner

These components allow Alchemi to be utilized

to create different grid configurations desktop

cluster grid, multi cluster grid, and cross-platform

grid (global grid). (Dhivya and Ruba, 2012)

2.1.1 Cluster Desktop Grid

The cluster desktop grid (shown in Figure 2),

consists of a single Manager and multiple Executors.

One or more Owners can execute their applications

on the cluster by connecting to the Manager. Such

environment is appropriate for the deployment on

Local Area Networks as well as the Internet.

(Dhivya and Ruba, 2012).

Figure 2: Cluster (Desktop Grid) Deployment.

2.1.2 Multi Cluster Deployment

A multi-cluster environment (shown in Figure 3) is

created by connecting Managers hierarchically .As

in a single-cluster environment, any number of

Executors and Owners can connect to a Manager at

any level in the hierarchy. The key to accomplishing

multi-clustering in Alchemi's architecture is the fact

that a Manager at a given “intermediate” level is

treated by the higher level-Manager as an Executor.

Such an environment is more appropriate for the

deployment over the Internet.

2.1.3 Cross-Platform Manager

A grid middleware component such as a broker can

use the Cross-Platform Manager (Shown in Figure

4) web service to execute cross-platform

applications (jobs within tasks) on an Alchemi node

(cluster or multi-cluster) as well as resources grid-

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enabled using other technologies such as Globus.

(Dhivya and Ruba, 2012).

Figure 3: Multi Cluster Deployment.

Figure 4: Cross-Platform Manager.

3 DATABASE PARTITIONING

In this paper we study the impact of executing a

medical image database query application on the

grid. For lowering the total computation time, the

image database is partitioned in equal subsets to be

processed on different grid nodes. A theoretical

model of the application computation cost and

estimates of the grid execution overhead are used to

efficiently partition the database. Smart partitioning

of the database can lead to significant improvements

in terms of total computation time (Montagnat et al.,

2004).

If the database is partitioned in bags of images to

be analyzed, each bag can be analyzed by a single

computing job. If one bag is representing one image

so that all images could be processed in parallel,

then Alchemi threads could work in parallel. Hence,

the execution time would be the maximum of the

execution times of each image processing. See

Figure 5.

Figure 5: Database Partitioning Process.

4 CONTENT BASED IMAGE

RETRIEVAL

A large number of medical images in digital format

are generated by hospitals and clinics every day.

Such images constitute an important source of

anatomical and functional information for diagnosis

of diseases, medical research, and education. It is

well known that medical image databases are the

key component in diagnosis and preventive

medicine. This increasing trend towards digitization

of medical images creates a need of technologies for

storage, organization, and retrieval of the medical

images. Content based image retrieval (CBIR) is the

digital image searching problem in large databases

that makes use of the contents of the images

themselves rather than relying on the textual

information. These techniques use the automatically

derived features (low level feature) such as color,

texture and shape as search criteria. Medical images

generated in hospitals contain semantic information

(high level feature). This information can be used to

retrieve the images.

4.1 Semantic Feature Extraction

The DICOM standard was created by the National

Electrical Manufacturers Association (NEMA) to aid

the distribution and viewing of medical images, such

as CT(Computed Tomography) scans, MR(Magnetic

Resonance), and US (Ultrasound). Imaging

equipment used in hospitals generates images which

are in DICOM format. It is a standard format used to

obtain, store and distribute medical images. DICOM

comprise standardized textual descriptions of study,

patient, body region examined and modality. A

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single DICOM file contains both a header (which

stores information about the patient's name, the type

of scan, image dimensions, etc), as well as all of the

image data. This is different from the popular

Analyze format, which stores the image data in one

file (*.img) and the header data in another file

(*.hdr). The DICOM header size varies depending

on how much header information is stored. The

header describes the image dimensions and retains

other text information about the scan. DICOM files

are composed by one image and tags describing the

image. Tags are textual or numerical sequences of

<attribute, value> pairs. The textual information is

considered as the semantic information. For all the

DICOM files the image and the relevant tags are

extracted and are stored in the database. The image

is stored in jpeg file format. The extracted semantic

information is stored in the database which is used

during the retrieval process. (Selvarani and

Annadurai, 2007).

4.2 Content Feature Extraction

Content Based Retrieval system represents each

image as a feature vector and measures the similarity

between images as the distance between their

corresponding feature vectors. For medical images,

shape and texture are the two important low level

features which describe the content of the image.

The shape and texture features are extracted and

stored in the database as feature vectors. (Selvarani

and Annadurai, 2007).

5 PROPOSED SYSTEM

5.1 Semantic Feature Extraction

Module

Semantic features module extracts DICOM tags

information from .dcm files format. Some extracted

DICOM information can be observed in Figure 6.

After extracting all needed features from images,

it will be stored in SQL database server to be used in

searching process, and then the database is

transported to computation nodes and partitioned to

equally sized subsets.

5.2 Database Modules

The database consists of 2 parts: DICOM images

and DICOM tags (Semantic Feature).

Figure 6: DICOM Tags.

5.2.1 DICOM Images

We used a database of 100,000 images from 6000

patients. The selected images were composed by CT

and MR images representing different anatomical

structure: Head, heart, Shoulders ..., etc. DICOM

images are stored as image data types, which are

data types that hold any type of binary data. We read

BLOBs (Binary Large Objects) as streams and

manipulate/display images according to the

information (e.g., Bit Depth) extracted from the

DICOM Info file.

5.2.2 DICOM Tags

For each DICOM image we extract all DICOM

semantic features and store it in SQL database and

make relation between image and its semantic

information for the ease of retrieval process.

5.3 DICOM Selection Module

In this module, the user interface guides user to

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construct the query performing the selection by

querying DICOM tags. The system then selects the

most appropriate images related to these tags. The

selection is based on a simple SQL database query.

5.4 Output Module

In this module, the proposed system collects the

resulted images from all threads and then displays it

on the screen .Also all information related to the

resulted images are available to users who can save

it in a text or xml file format.

6 EXPERIMENTAL RESULTS

AND DISCUSSION

We construct Dictionary containing all DICOM tags

to allow users to choose tags related to their query

attributes. Assume that the user query is to retrieve

all the images of US modality. The user must

specify the query attributes (e.g., Acquisition Date,

Modality, Patient Name, Admitting Diagnosis

Description) using interface module. This is shown

in Figure 7.

When the user selects the search operation, the

system connects to grid middleware, and then

Alchemi imitates traditional multi threaded

programming. GThread is a grid thread and

GApplication is application thread. “Just in time

scheduler algorithm” is used for splitting the

comparison job into different threads. Assume that

T1, T2, T3….Tn are the threads generated by

Alchemi. Alchemi owner provides an interface with

the grid application between the application

developer and the grid. The owner submits the

completed threads to the Alchemi Manager. The

Alchemi Manager manages the execution of threads

responsible for the searching process in its own

dataset.

The executors register themselves with the

Manager which in turn keeps track of their

availability. Threads received from the Owner are

placed in a pool and scheduled to be executed on the

various available Executors. The Executor accepts

threads from the Manager and executes them.

Executor API is used to make an interface with the

Alchemi Manager.

Figure 8 shows the configuration of Alchemi

Manager. In the figure, setup connection enables or

starts the Alchemi manager. Figure 9 shows the

configuration of Alchemi Executor that contains the

host and the port number of connectivity, and

Figure 7: User Interface.

Figure 8: Manager Window.

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Figure 9: Executor Window.

credentials required to login on to the system. Figure

10 shows Console Form .This form provides the

system statistics and real time graph of power

availability and usage.

We can see that partitioning the dataset on

different executor nodes while there is no task

dependency can lead to a significant improvement in

the retrieval time. Figure 11 provides a comparison

between sequential and parallel execution time. The

system was tested on set of 100,000 images .We had

provisioned 10 executor nodes.

Processing sequentially can be an inefficient way

to handle large datasets. The gap in runtime greatly

increases as the dataset grows larger in size .These

results demonstrate that the grid computing can

dramatically reduce the time required to retrieve

medical images.

7 CONCLUSIONS

In this paper we propose a grid based system for

retrieving medical images. Grids are promising

architectures that can bring different solutions to

medical image storage and retrieval problems. The

proposed system performs content based medical

Figure 10: Console Form.

Figure 11: Comparison between Sequential and Parallel

Execution Time.

image retrieval by extracting semantic information

from the dataset values of the DICOM format which

produces a set of images relevant to the given query.

To speedup retrieval process, we partitioned the

database into equally sized subsets. So, queries can

benefit from the grid computing parallelism and

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execute different queries, each on a different subset

of images. We can see that Alchemi framework is

ideally qualified to be deployed in medical image

retrieval system .Of course, as the number of

executors increases, the overall system performance

increases. The system is considered a step towards a

complete grid-based implementation for a complete

medical retrieval system.

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