Informatics Publishing Limited

N. Kumaravel ¹, M. Ramakrishnan ²

Affiliations
1 Mathematics Department, Selvamm Arts & Science College, Namakkal, IN
2 Mathematics Department, K.S.R College of Arts and Science, Tiruchengode, IN

Abstract

In this paper, regular and totally regular strong fuzzy graphs are introduced. Comparing the above graphs through various examples. A necessary and sufficient condition under which they are equivalent is provided. Some properties of regular strong fuzzy graphs are studied with examples.

 

Keywords

Degree of a Vertex, Regular Fuzzy Graph, Regular Strong Fuzzy Graph, Strong Fuzzy Graph, Total Degree of a Vertex and Totally Regular Strong Fuzzy Graph.

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R. B. Soundharya Devi ¹, N. Kumaravel ²

Affiliations
1 College of Engineering, Anna University, Chennai, IN
2 Centre for Medical Electronics, Anna University, Chennai, IN

Abstract

In this paper we propose an image segmentation algorithm based on Gaussian Mixture model combined with template match. Accurate detection of midline shift of CT brain scans is vital for diagnosis and treatment of Traumatic Brain Injuries (TBI). Automatic processing of these CT brain scans could speed up the decision making process, lower the cost of healthcare, and reduce the chance of human error. In this paper, we focus on automatic processing of CT brain images to segment and identify the ventricular systems. The segmentation of ventricles provides quantitative measures on the changes of ventricles. Initially the ideal midline is detected to calibrate the CT position and also for reference later. Segmentation of the CT images is the important step of this application. The specific aim of this method is to extract ventricles from brain CT images. In the method, different types of brain tissue, of which the ventricles form the region of interest, are segmented using multiple Gaussian mixtures.  Expectation Maximization (EM) method is used to train the GMM. Ventricular tissue has to be then detected in the segmented regions using template matching. The algorithms have then to be evaluated against a dataset of brain CT images captured from both normal and TBI cases. The proposed GMM-based method will allow accurate segmentation of ventricles required for detection of the shift in the midline shift in brain.

Keywords

CT, GMM Segmentation, EM, Template Matching, Traumatic Brain Injury, Brain Midline, Ventricles.

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M. Suchetha ¹, N. Kumaravel ²

Affiliations
1 Department of Electronics and Communication, Velammal Engineering College, Anna University, Chennai-600066, IN
2 Anna University, Chennai, IN

Abstract

The electrocardiogram reflects the electrical activity of the human heart. ECG monitoring gives vital information about the patient health and can support medical decision making in critical situations. But the ECG signal gets degraded due to the addition of noises during acquisition. The predominant noises present in ECG are power line interference and base line wandering. In this paper power line interference in ECG is reduced using wavelet thresholding method and the performance of various wavelet families are discussed. The standard performance metrics like SNR, MSE and SNR improvement are used to compare the performance of noisy and denoised ECG signal. Performance shows that symlets family of wavelet gives better performance compared to other wavelet families.

Keywords

Electrocardiogram, Interference, Wavelet, Power Line.

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