iosr-JCE   Volume-7 ~ Issue-6

Abstract:In the current era of modern health problems should be addressed with utmost importance and data should be secured in the competitive market in order to avoid misuse by third parties which are not responsible in this regard. In view of Health insurance which benefit patients economically should be effective and should also address the issue of providing privacy to their medical record. To address the above issues we are proposing architecture new secure e-health issue system (SEHIS) model which allows to store patients data in Smart e-Health Card (SEHC) which is given to patients by Health Insurance companies in turn used by medical practitioners to access patients data. SEHIS provides expert services to advice patients regarding insurance schemes selection (decision Support system) verify and secure patients data in SEHC. In this model, SEHC contains patients data like finger prints, photo and personnel data is encrypted by using PKI, Digital Image encryption techniques. SEHIS model verifies counterfeit documents, if any of the patient to use insurance facility or an insured person and a physician may deviate from insurance company rules to get benefits illegally etc.Iin our proposed model, SEHIS will benefit insurance companies, patients and works accordance with HIPPA requirements.

[1] "New variant M. J2 – RSA Cryptosystem" : E. Madhusudhan Reddy, B. H. Nagaraiasri, A. B. Rajesh kumar, M. Padmavathamma.
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applications Vol. 6 No. 1 January, 2012.
[7] "Secure E-Health Care Model" IOSR Journal of Computer Engineering (IOSRJCE) ISSN: 2278-0661, ISBN: 2278-8727 Volume X,
Issue X (Sep-Oct. 2012), PP 00-00
[8] Rule-based Expert System for Diagnosis and Symptom of Neurological Disorders "Neurologist Expert System (NES)" ICCIT 2012.

Abstract:The aim of the classification task is to discover some kind of relationship between the input attributes and the output class, so that the discovered knowledge can be used to predict the class of a new unknown tuple. The problem of secure distributed classification is an important one. In many situations, data is split between multiple organizations. These organizations may want to utilize all of the data to create more accurate predictive models or classifier while revealing neither their training data nor the tuples to be classified. The Naïve Bayes classifier is a simple but efficient baseline classifier. In this paper, we present a privacy preserving Naïve Bayes classifier for horizontally partitioned data. Our three layer protocol uses an Un-trusted Third Party (UTP). We study how to calculate model parameters for privacy preserving three-layer Naïve Bayes classifier for horizontally partitioned databases and communicate their intermediate results to the UTP not their private data. In our protocol, an UTP allows to meet privacy constraints and achieve acceptable performance.
Keywords: Naïve Bayes classifier, Privacy preserving, Probability, Un-trusted Third Party (UTP).

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Abstract:Recently, Content Based Image Retrieval (CBIR) plays a significant role in the image processing field. The construction of large datasets has been facilitated by the developments in data storage and image acquisition technologies. In order to manage these datasets in an efficient manner development of suitable information systems are necessary. Based on image content, CBIR extracts images that are relevant to the given query image from large image databases. Images relevant to a given query image are retrieved by the CBIR system utilizing either low level features such as shape, color, texture and homogeneity or high level features such as human perception. Most of the CBIR systems available in the literature extract only concise feature sets that limit the retrieval efficiency. In this paper, additional feature extraction such as homogeneity based feature extraction is used along with color, shape and texture feature extraction to extract the query image from the database images as well as from the RRI library and also to store the retrieved images in the RRI library. The proposed CBIR technique is evaluated by querying different images and the retrieval efficiency is evaluated by determining precision-recall values for the retrieval results.
Keywords: Content Based Image Retrieval, homogeneity feature, High level feature, Image Segmentation, kmean algorithm, Low level feature.

[1] Wang Xiaoling and Xie Kanglin, "Content based Image retrieval incorporating the AHP method", International Journal of
Information Technology, Vol.11, No.1, pp.25-37,
[2] Irfan and Sumari, "Automated content based image retrieval using wavelets", International Journal of Computational Intelligence,
Vol.1, No.1, pp.1-12, 2004
[3] Chi-Man Pun and Chan-Fong Wong, "Content-Based Image Retrieval Using Rectangular Segmentation", International Journal of
Computers, Vol.2, No.1, pp.72-79, 2008
[4] Hiremath and Jagadeesh Pujari, "Content based image Retrieval based on color, texture and shape features using image and its
complement", International Journal of Computer science and security, Vol.1, No.4, pp.25-35
[5] Arun and Hema P Menon, "Content Based Medical Image Retrieval by Combining Rotation Invariant Contourlet Features and
Fourier Descriptors", International Journal of Recent Trends in Engineering, Vol.2, No.2, pp., November 2009
[6] Srinivasa Rao, Srinivas Kumar and Chandra Mohan, "Content Based Image Retrieval using Exact Legendre moments and support
vector machine", The International Journal of Multimedia & its Applications (IJMA), Vol.2, No.2, pp. 69-79,May 2010
[7] Jalil Abbas, Salman Qadri, Muhammad Idrees, Sarfraz Awan and Naeem Akhtar Khan, "Frame Work for Content Based Image
Retrieval(Textual Based) System", Journal of American Science, Vol.6, No.9,pp.704-707, 2010
[8] Christopher C.Yang, "Content based image retrieval: a comparison between query by example and image browsing map
approaches", Journal of Information Sciences, Vol.30, No.3, pp.254-267, 2004
[9] Rajshree S.Dubey, Rajnish Choubey and Joy Bhattacharjee, "Multi Feature Content Based Image Retrieval", International Journal
on Computer Science and Engineering, Vol.2, No.6, pp.2145-2149, 2010
[10] Nandagopalan, Adiga and Deepak, "A Universal Model for Content-Based Image Retrieval", WASET, Vol.46, No.112, pp.644-647, 2008

Abstract:We describe the method for segmentation of Left Ventricle (LV) in short axis cardiac MR Images in order to visibly identify the LV, the endocardium and the epicardium. The interpretation and evaluation of images obtained from Magnetic Resonance Imaging (MRI) is not a trivial task and also it is very timeconsuming if done completely manually. This is especially the case for higher dimensional image data such as volumetric and/or time-dependent data such as the Cardiac Images. The objective of this study was show clearly the LV in particular so that any deviation from the standard dimensions in terms of shape, size or texture, can be unmistakably identified. Early diagnosis of heart abnormalities can potentially decrease the risk of heart attacks as well as benefit people who have just undergone Heart Transplant Surgery. For segmentation of the Cardiac MRI, Principal Component Analysis (PCA) is used in the Active Appearance Model (AAM) building process. The extraction of statistical features with PCA can be regarded as a projection of high-dimensional data vectors into a space of much lower dimensionality. The process of building a computerized model is equated with making the computer learn possible appearances of an object. The basis for such a learning process is the training set which is prepared from the data obtained from a reputed hospital in Pune.
Keywords: Magnetic Resonance Imaging(MRI), Left Ventricle(LV), Principal Component Analysis(PCA), Active Appearance Model(AAM).

[1] Neeti Gore. "Image Segmentation Combining Level Sets and Principal Component Analysis" A Thesis. Department of Electrical
and Computer Engineering, Carnegie Mellon University.
[2] Caroline Petitjean, Jean-Nicolas Dacher. "A review of segmentation methods in short axis cardiac MR images" Science Direct
Medical Image Analysis.
[3] Andy Tsai*, Anthony Yezzi, Jr., William Wells, Clare Tempany, Dewey Tucker, Ayres Fan, W. Eric Grimson, and Alan Willsky.
"A Shape-Based Approach to the Segmentation of Medical Imagery Using Level Sets" in IEEE Transactions on Medical Imaging,
Vol. 22, No. 2, pp. 137-154 February 2003
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Lelieveldt. "Automated Detection of Regional Wall Motion Abnormalities Based on a Statistical Model Applied to Multislice
Short-Axis Cardiac MR Images" in IEEE Transactions on Medical Imaging,, Vol. 28, No. 4, pp. 595-607, April 2009
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