R. Vanithamani

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This paper approaches an intellectual diagnosis system using hybrid approach of Adaptive Neuro-Fuzzy Inference System (ANFIS) model for classification of Electrocardiogram (ECG) signals. This method is based on using Symlet Wavelet Transform for analyzing the ECG signals and extracting the parameters related to dangerous cardiac arrhythmias. In these(More)
Speckle is a random multiplicative noise which obscures the perception and extraction of fine details in ultrasound image and despeckling is necessary to improve the visual quality for better diagnoses. Preliminary treatment of images before segmentation and classification includes despeckling as one of the important steps. This paper aims at introducing(More)
—Speckle is a granular noise that inherently exists in and degrades the quality of ultrasound images. It generally tends to reduce the resolution and contrast, thereby, to degrade the diagnostic accuracy of this modality. Speckle reduction is one of the most important processes to enhance the quality of ultrasound images. This paper proposes a statistical(More)
Wireless Sensor Networks (WSNs) are composed of a large number of battery powered sensor nodes with sensing, computing and communication infrastructure. The sensors are randomly deployed in the sensing field and are organized as clusters. Clustering helps to improve the lifetime of WSNs. Most of the existing sensor networks are homogeneous where the Cluster(More)
Ultrasound imaging has features like non-invasive nature, real time image formation capacity and relatively low cost, which makes this diagnostic tool attractive and hence has become an important imaging modality in medical diagnoses. However the usefulness of this imaging is degraded by the presence of speckle noise. Hence, speckle suppression in(More)
— This paper presents a review of wavelet thresholding techniques for despeckling of medical ultrasound images. An ultrasound image is first transformed into wavelet domain and then the wavelet coefficients are processed by different wavelet thresholding techniques. The denoised image is obtained by taking the inverse wavelet transform of the modified(More)
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