Informatics Publishing Limited

A. Umamageswari ¹, G. R. Suresh ²

Affiliations
1 Department of Computer Science and Engineering, Sathyabama University, IN
2 Department of Electronics and Communication Engineering, Easwari Engineering College, IN

Abstract

Protection of Medical image contents becomes the important issue in computer network security. Digital Watermarking has becomes a promising technique for medical content authentication, it allows to embed relevant information with the image, which provides confidentiality, integrity and authentication by embedding Digital Signature (DS) with the Medical image. In this paper we focus on need for reversible watermarking, Medical Image Compression and security related problems in medical images, it comparing the performances of various lossless watermarking techniques for various medical image modalities like MRI (Magnetic Resonance Imaging), US (Ultrasonic), CT (Computed Tomography), Endoscopic and Angiographic images. Region of Interest (ROI) supporting lossless watermarking systems only considered for discussions. Performance of all lossless watermarking with Digital Signature is analyzed by means of four parameters Capacity Rate, PSNR (Peak Signal to Noise ratio), NPCR (Number of Pixel Change Rate) and Compression Ratio (CR). This Paper also introduces new mechanism for open network security for medical images. This lossless watermarking is responsible for recovering the altered medical image content of the system.

Keywords

Reversible Watermarking, Medical Image Compression, Digital Signature, Medical Image Security, ROI, Kerberos.

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M. Ferni Ukrit ¹, G. R. Suresh ²

Affiliations
1 Department of Electrical and Electronics Engineering, Sathyabama University, IN
2 Department of Electronics and Communication Engineering, Easwari Engineering College, IN

Abstract

With the rapid growth of digital technology the demand to preserve raw image data for further processing is increasing. In medical industry the images are generally in the form of sequences which are much correlated. These images are very important and hence lossless image compression is needed to reproduce the original quality of the image without any loss of information. The correlation among the image sequences is exploited by interframe coding. Interframe coding includes Motion Estimation and Motion Compensation process supported by the Block Matching Algorithm. There are various block matching algorithms. The proposed method has taken Diamond Search and Inverse Diamond Search for comparison. The algorithms are used in Super-Spatial Structure Prediction to achieve high compression ratio. Results are compared in terms of compression ratio and search points to the prior arts.

Keywords

Diamond Search, Inverse Diamond Search, Losssless Compression, MEMC, Super-Spatial Structure Prediction.

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A. Selvarani ¹, G. R. Suresh ²

Affiliations
1 Research Scholar, Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Rajiv Gandhi Road, Chennai, IN
2 Professor, St Peter’s Institute of Higher Education and Research, Tonakela Camp Road, Sankar Nagar, Avadi, Chennai, Tamil Nadu, IN

Abstract

The tongue use as an initial biomarker for disease diagnosis in Greek, western and Chinese culture. The tongue provides a non-invasive method to diagnose diseases in health care. The tongue shows gamut of symptoms due to disease and disorder. The traditional tongue diagnosis requires quantitative experience. In western medicine the tongue symptom use as one of the criteria for disease diagnosis. The tongue reflexology shows functioning of internal organs in body. Recent studies attribute chronic pancreatitis to saliva secretion. In this paper, we propose to diagnose diabetes with tongue thermal image. The Particle swarm optimization (PSO) algorithm to cluster thermally active pixels with similar intensity. The clustered tongue thermal region show drastic change for normal and diabetic person. The study involved 25 normal person and 25 type 1 diabetic person. The proposed approach diagnoses diabetes with 86% accuracy.

Keywords

Tongue, type 1 diabetes, Pancreas, salivary gland, insulin, Thermal image Particle swarm optimization

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