Speckle reduction of ultrasound medical images using Bhattacharyya distance in modified non-local mean filter

Spatial correlation information between pixels is considered to be very important in thresholding methods. However, it is often ignored and thus unsatisfied segmentation results maybe obtained. To overcome this shortcoming, we propose a new image segmentation approach by taking not only pixels’ spatial information but also pixels’s gray level into account. First, a non-local mean filter is imposed on the image. Then the filtered image and the original image together are adopted to build a two dimensional histogram, it is called non-local mean two dimensional histogram. Finally, a minimum relative entropy criteria is used to select the ideal thresholding vector. Since the non-local mean filter process is performed in a neighborhood of current pixel, it carries out the spatial information of current pixel. Segmentation results on several images illustrate the effectiveness of the proposed thresholding method, whose segmentation accuracy are greatly improved compared to most existing thresholding methods.

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Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image

Neurocomputing ◽

10.1016/j.neucom.2015.05.140 ◽

2016 ◽

Vol 195 ◽

pp. 88-95 ◽

Cited By ~ 48

Author(s):

Jian Yang ◽

Jingfan Fan ◽

Danni Ai ◽

Xuehu Wang ◽

Yongchang Zheng ◽

...

Keyword(s):

Noise Reduction ◽

Ultrasound Image ◽

Speckle Noise ◽

Medical Ultrasound ◽

Local Statistics ◽

Mean Filter ◽

Local Mean ◽

Non Local ◽

Speckle Noise Reduction

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Spatially-guided non-local mean filter for denoising of clinical whole-body PET images

2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC) ◽

10.1109/nssmic.2018.8824613 ◽

2018 ◽

Cited By ~ 1

Author(s):

Hossein Arabi ◽

Habib Zaidi

Keyword(s):

Whole Body ◽

Mean Filter ◽

Local Mean ◽

Non Local

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An Efficient Random Valued Impulse Noise Suppression Technique Using Artificial Neural Network and Non-Local Mean Filter

10.4018/978-1-6684-2408-7.ch055 ◽

2022 ◽

pp. 1157-1173

Author(s):

Bibekananda Jena ◽

Punyaban Patel ◽

G.R. Sinha

Keyword(s):

Neural Network ◽

Impulse Noise ◽

Noise Suppression ◽

Back Propagation ◽

Feed Forward Neural Network ◽

Suppression Technique ◽

Mean Filter ◽

Local Mean ◽

Non Local ◽

A New Technique

A new technique for suppression of Random valued impulse noise from the contaminated digital image using Back Propagation Neural Network is proposed in this paper. The algorithms consist of two stages i.e. Detection of Impulse noise and Filtering of identified noisy pixels. To classify between noisy and non-noisy element present in the image a feed-forward neural network has been trained with well-known back propagation algorithm in the first stage. To make the detection method more accurate, Emphasis has been given on selection of proper input and generation of training patterns. The corrupted pixels are undergoing non-local mean filtering employed in the second stage. The effectiveness of the proposed technique is evaluated using well known standard digital images at different level of impulse noise. Experiments show that the method proposed here has excellent impulse noise suppression capability.

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