Jisha J U

Bio: Jisha J U is an academic researcher. The author has contributed to research in topics: Peak signal-to-noise ratio & Thresholding. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Poisson Noise Removal in Biomedical Imagesusing Non-Linear Techniques

Abstract: Medical images have always been an important factor in diagnosis of disease. Poisson Noise in those images has always been a problem with the image clarity. We propose two technique which combines Multi-Scale Variance Stabilizing Transform (MS-VST), Fast Discrete Curvelet Transform (FDCT) with Thresholding and MS-VST, FDCT with Null Hypothesis testing for effectively removing the Poisson Noise from the medical images. The effectiveness of using these techniques has been analyzed using Peak Signal to Noise Ratio and Universal Image Quality Index.

Poisson noise reduction from X-ray images by region classification and response median filtering

Abstract: Medical imaging is perturbed with inherent noise such as speckle noise in ultrasound, Poisson noise in X-ray and Rician noise in MRI imaging. This paper focuses on X-ray image denoising problem. X-ray image quality could be improved by increasing dose value; however, this may result in cell death or similar kinds of issues. Therefore, image processing techniques are developed to minimise noise instead of increasing dose value for patient safety. In this paper, usage of modified Harris corner point detector to predict noisy pixels and responsive median filtering in spatial domain is proposed. Experimentation proved that the proposed work performs better than simple median filter and moving average (MA) filter. The results are very close to non-local means Poisson noise filter which is one of the current state-of-the-art methods. Benefits of the proposed work are simple noise prediction mechanism, good visual quality and less execution time.

Reduction of Noise in Medical Imaging Quality

Abstract: When it comes to diagnosing patients’ illnesses, digital image modalities like X-ray, Ultrasound (US), Computer Tomography (CT), Magnetic resonance imaging (MRI), etc. play an essential part. Noise is a common problem in the pictures produced by these modalities, reducing image quality. An important factor in making correct diagnosis of illness is the quality of the medical pictures used. Poisson noise is a prevalent problem in X-ray pictures. Hairline fractures inside bones, chest coughs, and other similar conditions become more difficult to diagnose when this noise is present. These sounds need to be eliminated from the X-ray picture before it may be improved. In this study, we aimed to establish a method for effectively denoising X-ray pictures, hence reducing the amount of Poisson noise present in them. The suggested filter makes use of the Absolute Difference and Mean Filter (ADMF) to replace the processed pixel with the mean of its nearest neighbors within a 5x5 frame when the absolute difference between them is minimal. Using 75 X-rays of teeth from the Digital Dental X-ray Database, the proposed technique is compared to the state-of-the-art Region Classification and Response Median Filtering (RCRMF) method. Filter performance is measured by Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) scores; the suggested approach improves PSNR by 5.41 percentage points and reduces MSE by 33.44 percentage points.