Title of your research undertaking

A Telematics-Enabled System for Medical Image Database, Diagnosis Assistance, and Mobile Access

Executive Summary

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We are doing a research proposal for a modular system for medical image archiving, direction, diagnosing support, and telematics cooperation. It will supply digital imagination and communicating in medical specialty ( DICOM ) -compatible tools for digital image processing and database direction of medical images which besides features algorithm for preprocessing, manual or semi-automatic cleavage, automatic computation of geometrical/size features, and3D visual image for organ or any selected part of involvement. The entree to the database can merely be permitted to authorized user. The package interfaces is user-friendly and it is collaborated with distant experts. The pilot system will be incorporates by a computer-aided diagnosing faculty taking at supplying support in the diagnosing of focal liver lesions from computed imaging images.


I imaging modes bring medical images that can be used to follow-up the patient’s wellness position, for diagnosing intents, and in intervention besides surgical planning processs.

In image acquisition engineering, the progresss have resulted in an increasing sum of quantitative informations derived from medical images. The combination of computer-based medical image archiving and direction allows fast, nonsubjective, and accurate appraisal of findings of findings in medical images and the exchange of cognition between distant health care professionals.

The telematics will enable incorporate system where it supports the aggregation, file awaying, and processing of images. The DIAGNOSIS is characterized by the characteristics to treat and analyse images from much type of medical imagination modes utilizing traditional or advanced image processing tools. The aforesaid capablenesss are supplied both locally and remotely through the integrating of an appropriate telematics faculty. This will allow the tele-consultation and the real-time exchange of consequences affecting the wellness attention professionals

Patients can follow up with their wellness position or the current medical specialty they need to take by utilizing the application for nomadic device where the informations itself are from the consequence of DIAGNOSIS. The informations received from DIAGNOSIS will non be stored in database waiter any longer as many proficient mistakes normally occurred where it is traveling to make problem to the direction system in future. As epoch changed, the informations will be stored in the cloud storage web while the privateness affair will be taken attention of by the service supplier.

Justification of Research

Many medical centres are holding job in hive awaying the information which contain the patient’s DIAGNOSIS consequence and wellness position. With current engineering presents, the informations can be store, manage, and secure by hive awaying them in the cloud web service supplier. It is good in many footings, such as free security in procuring the information of the patients and the addition in no longer keeping the waiter database while the easy entree in file awaying the information.

With the system implemented, patients are no longer needed to line up or wait for their bend to assist them in catching up with their wellness position as all can be accessed from nomadic devices. In other manus, they can besides be updated with their medical check-up bend or an assignment from the presentment sent whether by neither the physician nor the system.

Research Aims

The aims of this research are:

-To cut cost in keeping the waiter database and shop all the informations in cloud waiter web

-To make it efficient when file awaying as the patients informations is classified by patients ID

-To ease the patients in following up with their wellness position

-To make a system where every infirmary or medical Centre can afford

Literature Review

A research conducted by M. Gletsos, S. G. Mougiakakou, G. K. Matsopoulos, K. S. Nikita, A. Nikita, and D. Kelekis ( 2003 ) stated that a computer-aided diagnostic ( CAD ) system for the categorization of hepatic lesions from computed imaging ( CT ) images is presented. Regions of involvement ( ROIs ) taken from non-enhanced CT images of normal liver, hepatic cysts, haemangioma, and hepatocellular carcinomas have been used as input to the system. The proposed system consists of two faculties: the characteristic extraction and the categorization faculties. The characteristic extraction faculty calculates the mean grey degree and 48 texture features, which are derived from the spacial gray-level accompaniment matrices, obtained from the ROIs. The classifier faculty consists of three consecutive placed feed-forward nervous webs ( NNs ) .

The first NN classifies into normal or pathological liver parts. The pathological liver parts are characterized by the 2nd NN as cyst or “ other disease. ” The 3rd NN classifies “ other disease ” into haemangioma or hepatocellular carcinoma. Three characteristic choice techniques have been applied to each single NN: the consecutive forward choice, the consecutive drifting forward choice, and a familial algorithm for characteristic choice. The comparative survey of the above dimensionality decrease methods shows that familial algorithms consequence in lower dimension characteristic vectors and improved categorization public presentation.

A survey conducted by Stavroula G. Mougiakakou, Ioannis K. Valavanis, Nicolaos A. Mouravliansky, Alexandra Nikita, and Konstantina S. Nikita ( 2009 ) stated that DIAGNOSIS, which is a telematics-enabled system back uping direction, processing, and computing machine assisted reading of medical images, has been presented. User-friendly interfaces along with advanced image-processing functionalities permit the optimisation of the diagnosing process through tools for cleavage and quantitative analysis of variety meats or lesions of involvement. Furthermore, the relational database, which contains the patients’ necessary and their tomographic images, along with appropriate tools for information direction, simplifies the image archiving and direction processs, which the system’s telematics constituents allows the coaction between distant wellness attention professionals.

Research Methodology

To prove out the effectivity of the system, it is estimated that at least 50 Region of Interest ( ROI ) s will be used for the design, development, and preliminary rating of the Computer Aided Design ( CAD ) faculty. The ROIs will be distributed into three disjoint informations sets ( preparation, proof, and proving ) . Following, a figure of texture characteristic sets utilizing assorted texture characteristic methods are estimated to set ROI to the characteristic extraction submodule. The full characteristic sets which will be obtained after proper characteristic choice carried out in the characteristic choice submodule will be fed to an EC, dwelling five primary classifiers. For each ROI, a set of texture characteristics is estimated to utilize five texture appraisal methods.

To do the image file awaying system plants, the bootstrap technique will be utilized for resampling the small-sized information set, distribution of informations into preparation, proof and proving sets, and design and rating of the classifiers and the foil of classifiers ( EC ) . The CAD faculty categorization of abdominal non-enhanced CT liver tissue ROIs into healthy tissue ( C1 ) , cyst ( C2 ) , hemangioma ( C3 ) , and hepatocellular carcinoma ( C4 ) . Patients with C2, C3, and C4 will be verified by needle biopsies, denseness measurings, and the typical form of sweetening after the endovenous injection of iodine contrast.

To detect the credibleness of the CAD faculty, the ability of the CAD faculty to know apart the four liver tissue types in unknown informations was measured in footings of its categorization truth in the ROIs of the testing set. The optimum figure of concealed nerve cells and the appropriate values of impulse and initial acquisition rate will be estimated utilizing a trial-and-error procedure. A leaden vote strategy will be used to unite the anticipations of the single classifiers into the concluding determination of the CAD faculty.

Next, the CAD faculty constituents will be undertaken 50 times to obtain dependable consequences on its public presentation utilizing the bootstrap technique. Each preparation set consisting of the estimated ROIs will be sampled with replacing from the available ROIs, the ROIs non looking in the preparation set will be indiscriminately allocated into two every bit sized sets ( proof and proving sets ) . The truth of CAD faculty public presentation will be tested utilizing FOS characteristics and TEM characteristic vector. Sensitivity and specificity will be measured utilizing the one-versus-all comparings. Both prosodies will be measured when know aparting one type of liver tissue from the staying three utilizing each bootstrap proving set.


M. Gletsos, S. G. Mougiakakou, G. K. Matsopoulos, K. S. Nikita, A. Nikita, and D. Kelekis, “A computer-aided diagnostic system to qualify CT focal liver lesions: Design and optimisation of a nervous web classifier, ” IEEE Trans. Inf. Technol. Biomed. , vol. 7, no. 3, pp. 153–162, Sep. 2003.

J. A. Noble and D. Boukerroui, “Ultrasound image cleavage: A study, ” IEEE Trans. Med. Imag. , vol. 25, no. 8, pp. 987–1010, Aug. 2006.

Stavroula G. Mougiakakou, Ioannis K. Valavanis, Nicolaos A. Mouravliansky, Alexandra Nikita, and Konstantina S. Nikita “DIAGNOSIS: A Telematics-Enabled System for Medical Image Archiving, Management, and Diagnosis Assistance.” IEEE Transactions on Instrumentation and Measurement, 58 ( 7 ) , 2113-2120. July 2009


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