Speckle de-noising of clinical ultrasound images based on fuzzy spel conformity in its adjacency

Article Type

Research Article

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Applied Soft Computing Journal


This article presents a novel speckle de-noising technique leveraging the properties of fuzzy set theory for preserving context of the echo-texture and dealing with vagueness and uncertainty in ultrasound images. This work is inspired from the theory of Michailovich and Tannenbaum (2006) where it was shown that the log transformation of clinical images converts the speckle noise to white Gaussian noise (WGN) with outliers. In this context, we propose to apply a non-linear transformation (using functions with horizontal asymptotes) to speckle noise which would map the outlier to the asymptote of the function. This would result in suppression of the outliers and thereby reducing the problem to cancellation of WGN. We adhere to fuzzy set theory for defining such transformations as they can be well characterized with vague linguistic terms. In this work, a property scene is generated using a membership function which has the ability to suppress the outliers. Thereafter, the property scene is updated such that the membership of belonging of the spels in a neighborhood is in agreement with each other to the defined property. This, in turn, will restore the homogeneity in the neighborhood of the image when the property scene is de-fuzzified back to image space. To measure the compliance of a neighborhood spel, a concept of degree of conformity is introduced. This degree of conformity determines the contribution of the neighboring spel in the update process of the property scene. To elicit the effectiveness of the proposed technique, comparative analysis has been done with five state-of-the-art techniques on in silico images using five standard measures and in vivo ultrasound images with two performance indices. Moreover, to show the impact of the de-noising algorithm on performance, segmentation based investigation is carried out on 102 ultrasound images of carotid artery. A robustness analysis with increasing level of noise is also investigated. The performance of segmentation is reported in a box plot of precision and recall. Qualitative and quantitative analysis reveal the promising performance of the proposed technique.

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