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Role of Digital Image Processingin Telemedicine

T. Kalaiselvi 1, K. Somasundaram 2 and P. Kumarashankar 3 Department of Computer Science and Applications,

Gandhigram Rural Institute, Deemed University, Gandhigram624302, Tamil Nadu, Indiae-mail: 1kalaivpd@gmail.com, 2 somasundaramk@yahoo.com, 3 pkumarashankar@gmail.com

Abstract In this paper we have discussed about thegrowth of telemedicine and teleradiology in India. Wehave also discussed about the possibility of saving storagespace and bandwidth while sending MRI scan images overthe network for telemedicine purposes. We have come upwith an index to compare the optimized storage spaceusage and bandwidth while transmitting the images. Wehave done this using the datasets used by our previouswork. This article is a continuation of our previous workin further enhancing the role the image processing inteleradiology. We have also discussed about the varioustelemedicine projects in India and the variousorganizations, both government and private entities thathave involved in taking telemedicine to the remote areas ofIndia. We have briefly discussed the usage and benefits ofcloud computing in the field of telemedicine/teleradiology.

Keywords: Digital Image Processing, MRI head scans,Teleradiology, Telemedicine, Medical Imaging,Telemedicine in India, Cloud Computing in Teleradiology.

I. I NTRODUCTION

Telemedicine is the practice of giving clinical careto people at a distance who dont have the privilege ofgetting specialized care in their place. Its a boon to thehumanity by modern science and technology.Telemedicine can be categorized like: Collecting thedata from the patients and sending to medical expertsfor offline assessment; Monitor the health conditions ofthe patients remotely using technological devices;Interactive services providing interaction between themedical officer and the patients through phone or onlinecommunications.

Teleradiology is the process of transmitting theclinical images from one location to another in want ofexpert opinion from other radiologists and physicians.The growth of radiologist population does not meet thegrowth of imaging procedures and hence teleradiology

plays a vital role at this juncture. In situations wheresub specialists are needed, teleradiology ensures thatthere is 24/7 availability. This makes use of specializedsoftware to transmit the medical images throughinternet and other networks. The latest addition to thislist is Cloud computing. Image processing andcompression are often used in teleradiology. These areused to ensure that only slices of interest are sent to thespecialists instead of sending all the information thussaving the cost and bandwidth. The growth oftechnology in the medical imaging andtelecommunications has created a revolution in field ofteleradiology.

II. T ELEMEDICINE IN I NDIA

India is a fore runner in the field of informationtechnology. Now it is emerging as a popular hub formedical tourism given the fact that various treatments inIndia are much cheaper when compared to othercountries in the world. Considering all these facts Indiacan emerge as a cloud computing hub for providingservices in the medical sector particularly in the field of

teleradiology where the requirements for specialists isabundant.Considering the geographical complexity of India

and its population distribution, it is a herculean task to provide health care to most of its people who live inremote villages. Having most of its medical specialistsworking in metros, there are only very little options to

provide health care to remote areas. There are variousgovernment agencies and NGOs which setup occasionalhealth care programmes throughout the country whichdo very little to the Indian community.

At this juncture telemedicine is what seems to be a boon to the Indian health care industry. Given theadvancements of technology in telecommunications in

India, this seems to be a doable job in the next 15 to 20years. As a first step of providing health care to remoteareas, the Union government has introduced ambulanceservices at door step which is well implemented in mostof the states. Some of the states like Gujarat have comeup with plans to setup call centres with paramedics tosuggest primary actions to be taken in case of anemergency. The Apollo Telemedicine NetworkingFoundation has tied up with the Government of Gujaratto create mobile telemedicine facilities keeping in mindthe disaster caused due to the natural calamities [1]. Thetelemedicine units setup as part of this initiative makeuse of the Ku band satellite connectivity provided byISRO.

The government of Tamilnadu has setuptelemedicine at Primary health Centres which are linkedto the nearest medical college hospitals under the RCH

programme. The Meenakshi Mission Hospital andResearch Centre in Madurai has also introducedtelemedicine to cater the needs of the rural mass incollaboration with Mahasemam Trust and supported byDirect Relief International, USA, and Indian SpaceResearch Organization. Sri Ramachandra MedicalCollege & Research Institute, Chennai is one of the

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forerunners in providing telemedicine facilities startingfrom as early as 1997 [2]. It is one of the super specialtyhospitals to provide health care across the country.There are a number of telemedicine centres that have

been setup by various hospitals, research institutes andother organizations across the country.

III. H ANDLING ELECTRONIC PATIENT R EPORTS

All the information of the patients is sent throughinsecure networks and hence there is a high risk ofusing this personal information for inappropriate andillegal purposes. This is a great challenge to thetelemedicine field as the patients medical history is notonly available to the physician, but also to otherindividuals like technical staff etc. Any breach in datasecurity will not only affect the patient but also thetelemedicine practitioners as well. Considering theseveral advantages of telemedicine, such security issuesshould be addressed to ensure that the data is as secure

as paper records or even better than that.Telemedicine becomes successful only if it takesless time to transfer data between both the parties.Especially, in case of Magnetic Resonance Imaging(MRI) scans the data volume is huge and data clarity isvery much on the focus. Bandwidth is considered to bea barrier as bulk amount of images are to be sentthrough the networks which take plenty of time,resources and cost. At the same time we have to ensurethat the Region of Interest should be identifiedautomatically. The data hiding in the MRI volumescould be done taking various factors into considerationlike implementing firewallsat both the ends, allowingaccess to data by allocating user ids and passwords by

rolesSay a technician who is in the position ofsending/receiving the data can have access to send thefiles from appropriate folders from the senderside/download the data to appropriate locations in thereceiving end. But s/he need not view those data byopening them; the consulting physician can have accessto view those data and write reports based on that data;a patient can have access to view his details but not toedit his details.

The MRI volumes should contain some patientinformation so as to relate them to the corresponding

patient. The quality of the image should not becompromised to include this information in the images.The patient information could be encrypted and sentalong with the image. The encrypted EPR should notoccupy more space as the space available outside theregion of interest is very less.

The data of the patients are captured electronicallyinstead of traditional paper records. These records aresaved in the computer systems and could be sharedacross geographical areas so as to get opinions fromvarious medical officers. This process has so many

benefits and at the same time it has some practicaldifficulties in some countries like India where multiple

languages are spoken. Multi lingual support for forms isneeded in these scenarios in order to provide service toa larger group.

IV. C LOUD COMPUTING AND CLUSTERING

In teleradiology when information has to be keptconfidential, it is mandatory that there is restrictedaccess and at the same time we have to ensure thatnecessary details are available to the telemedicineapplication users. Seeing it from the perspective ofusing computer clouds, the telemedicine service

providers have access to the details of many patients.This is an advantage for the service providers, as theycan group similar cases together in thus reducing thewait time for expertsA specialist can identify acommon treatment procedure for a group of similarcases, or a specialist can access the treatment procedureof a similar case that has occurred in the past. Thusspecialists do not have to spend more time in designing

a new treatment procedure. This grouping of similarcases is called as Clustering [6] and there are variousalgorithms available to do the clustering. This way wecan build a repository of various patients details undervarious categories, grouping the similar cases undersame categories and indexing them. In this way we canrefer to a similar case in the repository just by passing aquery to the system.

V. S AVING STORAGE SPACE AND BANDWIDTH

While analyzing the brain tumours [3],[4], actual brain portion is identified and the Slices of Interest aregrouped for expert assessments. In cases where remoteanalysis of the medical images is inevitable, we have tosend those images through internet to the expertslocated in remote areas. As discussed above, bandwidth

becomes a major factor in determining what has to besent and what neednt be sent. Referring to the dataset[5], we have done a small assessment to calculate thespace needed to store and transmit the slices of interest.Assuming, in the total set of the images, we will betransmitting the actual slices of interest in addition to10% of the leading and trailing images (a minimum ofone and a maximum three at each end). The imagestaken for comparison are grey images with a resolutionof 256x256 pixels [5].

Using appropriate brain extraction algorithms(BEA) will ensure that exact data is not missed andcorrect slices are chosen for assessments. The existingalgorithm extracts the brain [3], [4] and identifies theslices of interest that contains the abnormal portion [5].Once this is identified, we need to send them forassessment. Table 1 gives the details as to how muchspace is saved which is directly proportional to theeffectiveness of the algorithm that we have chosen toautomatically identify the slices of interest. The abovetable can be used as an index with an acceptable rangefalling within 10%.

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174 National Conference on Signal and Image Processing (NCSIP-2012)

TABLE 1

S. No Volume Total Slices ActualSlices

TransmittedSlices

Total Size(256x256)

Transmitted Size(256x256)

% of SpaceSaved

1 v01 54 - - - - -2 v02 43 - - - - -

3 v03 56 21 25 3670016 1638400 444 v04 29 9 11 1900544 720896 385 v05 24 3 5 1572864 327680 216 v06 24 12 14 1572864 917504 587 v07 27 6 8 1769472 524288 298 v08 24 11 13 1572864 851968 54

VI. C ONCLUSION

Currently we are in the process of developing asimple algorithm that could be used to encrypt the EPRthat is sent along with MRI scan images. Teleradiologyhas a high potential as a business. Various other factorslike, internet connectivity, scan centres and otherlogistics have a say in its success.

R EFERENCES[1] Apollo Telemedicine Networking FoundationKey Projects,

Available: http://www.telemedicineindia.com/KeyProjects.htm[2] Sri Ramachandra Medical CenterTelemedicine, Available:

http://www.sriramachandra.edu.in/telemedicine.htm

[3] K. Somasundaram and T. Kalaiselvi, Automatic BrainExtraction Methods for T1 Magnetic Resonance Images usingRegion Labeling and Morphological Operations, Computers inBiology and Medicine, Vol. 41, pp. 716725, 2011.

[4] K. Somasundaram and T. Kalaiselvi, Fully Automatic BrainExtraction Algorithm for Axial T2-Weighted MagneticResonance Images, Computers in Biology and Medicine, Vol.40, pp. 811822, 2010.

[5] K. Somasundaram and T. Kalaiselvi, Fully Automatic Methodto Identify Abnormal MRI Head Scans using Fuzzy

Classification and Fuzzy Symmetric Measure, InternationalJournal on Graphics, Vision and Image Processing(ICGST-GVIP), Vol. 10, No. 3, pp.19, 2010.

[6] K. Somasundaram and T. Kalaiselvi, Segmentation of BrainPortion from MRI of Head Scans using K-Means Cluster,Accepted in International Journal of Computational Intelligenceand Informatics, 2011.