Indian Journal of Public Health Research & Development

V. Thilakrathi ¹, K. Vidhya ²

Affiliations
1 Research Scholar, Department of Electronics and Communication Engineering, Saveetha School of Engineering, SIMATS, Chennai, IN
2 Professor, Department of Electronics and Communication Engineering, Saveetha School of Engineering, SIMATS, Chennai, IN

Abstract

In this paper an exhaustive study of despeckling techniques used for ultrasound images that are available in the literature is carried out. Noise removal from Ultrasound images still remains a challenging task for the image processing experts. The huge amount of research papers published in this area stands as a testimony to it. Generally there’s no unified approach for the removal of noises in US images. In this paper different types of most prominent existing speckle noise reduction techniques both in Spatial or Space domain and Frequency or Transform domain are discussed and compared.

Keywords

Ultrasound Images, Speckle Noise.

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P. Lavanya ¹, K. Vidhya ²

Affiliations
1 Research Scholar, Department of Electronics and Communication Engineering, Saveetha School of Engineering, SIMATS, Chennai, IN
2 Professor, Department of Electronics and Communication Engineering, Saveetha School of Engineering, SIMATS, Chennai, IN

Abstract

Lung cancer is the primary reason for mortalities among cancer-affected persons worldwide. We are living in a decade of emerging personalized and targeted medicine. With the help of the current advancements in the imaging hardware’s, in terms of quality, sensitivity and resolution, and the computing capabilities of the vast amount of data made personalized medicine possible. Non-invasive diagnosis is often required to detect lung cancer before actual clinical treatment started. Here come the latest radiomics proceedings using the advanced imaging technologies and high throughput computing capabilities aid the radiologists to diagnosis the lung cancer at a primitive stage. This paper, we survey the latest published scientific literature on Computer-Aided Decision Support Systems (CAD), which uses the radiomics proceedings to prognosis lung cancer.

Keywords

Radiomics, Biomarkers, Computed Tomography, Lung Cancer, Image Features, Image Processing.

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