Signal to noise ratio in ultrasound medical images captured through the digital camera is poorer, resulting in an inaccurate diagnosis. As a result, it needs an efficient despeckling method for ultrasound images in clinical practice and telemedicine. This article proposes a novel adaptive fuzzy filter based on the directionality and translation invariant property of the Non-Sub sampled Contour-let Transform (NSCT). Since speckle-noise causes fuzziness in ultrasound images, fuzzy logic may be a straightforward technique to derive the output from the noisy images. This filtering method comprises detection and filtering stages. First, image regions classify at the detection stage by applying fuzzy inference to the directional difference obtained from the NSCT noisy image. Then, the system adaptively selects the better-suited filter for the specific image region, resulting in significant speckle noise suppression and retention of detailed features. The suggested approach uses a weighted average filter to distinguish between noise and edges at the filtering stage. In addition, we apply a structural similarity measure as a tuning parameter depending on the kind of noise in the ultrasound pictures. The proposed methodology shows that the proposed fuzzy adaptive filter effectively suppresses speckle noise while preserving edges and image detailed structures compared to existing approaches.

Medical images are increasingly being used for diagnostic purposes, posing preservation, image enhancement, analysis, and transmission problems. Medical acoustic imaging, in particular, is widely used due to its safety, non-invasiveness, utilizes non-ionizing radiation, and inexpensive cost. Even though photo quality in ultrasound B-mode has markedly enhanced in recent times. The main shortcoming in ultrasound imaging is poor image quality due to the minor variations in acoustic impedance and backscattered echo signals of distinct soft tissues called speckles [

Moreover, poor image quality makes it difficult for the physician to diagnose and classify the different regions in the image using computer-based systems [

Krissian et al. [

Fuzzy logic-based many despeckling filters have been proposed [

This article proposes a novel denoising filter based on the Nonsubsampled Contourlet Transform (NSCT) and directional derivative of the transformed noisy ultrasound images. Integrates adaptiveness into two phases; in the first stage, fuzzy is applied on directional the difference features derived from NSCT coefficients to classify the regions in the transform domain. Then, the appropriate filters are applied to the classified areas using a weighted average filter to distinguish the edges and noise in the second stage. The projected algorithm is widely studied and compared with previous denoising filters on different images like standard images, simulated images, and clinical ultrasound images.

We organized this paper as follows: We cover the theoretical basis in Section 2. In Section 3, the noise model, multi-scale transform, fuzzy logic model, direction difference features fuzzy inference, and present the design of the weighted average filter. Then, Section 4 describes quantifying the outcomes of the experiments selected for the study. Finally, we conclude this study in Section 5.

It makes many attempts to reduce speckle noise in ultrasound pictures, which only reduces or eliminates noise and dilutes the essential features. Moreover, the techniques cannot discriminate the edge information and noises, causing sharp and weak edges to be suppressed and assuming them as noise. As a result, a method needs to reduce speckles ultimately and effectively preserve the edges and retain fine superior points in the image. The Viola-Jones technique is a method described in [

In an ultrasound picture, speckle noise displays a granular texture, and it is due to constructive and destructive ultrasound waves with objects from sub-resolution scatters [

I(a, b) is the true image. It acknowledges that additive noise (sensor noise) has less influence than multiplicative noise due to coherent interference.

Arsenault et al. [

NSCT uses despeckling in ultrasound images. When compared with contourlet transform, it observes that the NSCT is entirely shifting invariant, multi-scale, and multi-direction.

We develop NSCT by combining the Non-subsampled Directional Filter Banks (NSDFB) and Non-subsampled Pyramid (NSP) to provide multi-scale properties and variable angles. For example, let _{j} be the input image at _{0} and _{1}, to separate the input image

* is the convolution operator. The highpass subband is _{j,k} is the equivalent filter for the process mentioned above. The output of NSDFB _{jk} is,

The above process works on the low-pass sub-band

Then, the ‘max-flat’ and ‘dmaxflat7’ filters implement NSP and NSDFB [_{0}, G_{1} and _{0},

The input image may then restore using the above process, which is carried out iteratively from the j^{th} to the 1st level. The primary objective of this study is to use adaptive fuzzy logic filtering with a directional difference to denoise each directional subband.

The speckle noise influences the image pixels in ultrasound imaging and classifies regions as homogeneous, detail, or edge. So defined class has its distinct characteristics and varied membership function. As a result, we need to categorize each transformed coefficient in each directional subband as a suitable reasoning approach. As a result, we use fuzzy logic to classify each directional subband’s noisy coefficient into three classes based on the degree of membership function. There are two stages in the method proposed, i.e., detection and filtering. First, the image parameter directional difference was used in the detection stage to classify each level of transformed coefficient in each directional subband for detection [

This stage’s primary goal is to separate the noisy coefficients into homogeneous picture structures or edges. The neighbor reflects the features of every coefficient in the converted noisy picture. Directional Differences (DD) between altered coefficients and it identifies the neighbors to find the attributes of each coefficient in the transformed noisy image. The lesser the differential value, the more regular or homogeneous the noise coefficient is. The bigger the differential value, the more likely the noisy factor is to be considered an edge. Finally, the direction flow depicted in

We represent the coefficient and coordinates in each direction. The absolute deviation with all coefficients in every direction presents in the chosen size frame. Then average values of all differences [

Above

We obtain a, b, and c values using

The coefficients with lower directional difference values belong to the uniform region. We choose the minimum value of the directional difference from

As a result, thresholds a, b, and c values noisy coefficients combine into different classes stated and adaptively varied in each direction based on the quantity of noise present in the ultrasound picture.

Image type | Threshold | Noise level (_{n}) |
|||
---|---|---|---|---|---|

D1 | D2 | D3 | D4 | ||

Grayscale 8-bit 256 × 256 Lena picture | 0.40 | ||||

a | 0.0549 | 0.0353 | 0.0510 | 0.0314 | |

b | 0.2922 | 0.2824 | 0.2902 | 0.2804 | |

c | 0.5294 | 0.5294 | 0.5294 | 0.5294 | |

0.70 | |||||

a | 0.0902 | 0.0588 | 0.0902 | 0.0510 | |

b | 0.3098 | 0.2902 | 0.3078 | 0.2922 | |

c | 0.5294 | 0.5216 | 0.5255 | 0.5333 | |

1.00 | |||||

a | 0.1373 | 0.0863 | 0.1059 | 0.0784 | |

b | 0.3314 | 0.3059 | 0.3176 | 0.3020 | |

c | 0.5255 | 0.5255 | 0.5294 | 0.5255 |

We frame fuzzy rules to define noisy coefficients as detail, edge, and homogenous. ‘Large’ and ‘Small’ represent the connection level in each class. For the classification of each coefficient in three different classes, six rules are framed based on directional differences, as given below.

These rules may use to easily differentiate all distinct classes of coefficients and perform effective fuzzy inference.

Let ^{′}(^{′}(

Where,

In defining three different classes, each coefficient is X(s, L, θ) is mapped based on directional features. In the reasoning step, each coefficient is examined and classified as homogeneous, detailed, and edge regions. A suitable filter is necessary for denoising, removing the noise without altering the image’s structural information. As a result, appropriate filters apply to the defined classes.

For the homogenous class region, for every scale ‘L’ and every direction ‘θ,’ a simple mean filter of size (2

Fine details carry some helpful information, and if preserved, it cannot apply a mean filter on the coefficients classified as detail region X(s, L, θ). One of the non-linear and order static filters, the median filter, can preserve edges and retains desirable information for analysis given below in

In this class, we define the transformed coefficients with maximum directional difference value as edges, and there is a chance of having noise or sharp edges. Therefore, an adaptive weighted average filter must preserve essential details in a transformed image by suppressing noise, given in

where

Image type | Noise levels (_{n}) |
||
---|---|---|---|

0.40 | 0.70 | 1.00 | |

Lena | 0.18 | 0.3 | 0.44 |

House | 0.12 | 0.16 | 0.24 |

Ultra sound image1 | 0.1 | 0.12 | 0.14 |

Apply inverse NSCT on _{opt} else choose another C value and repeat steps from 6 to 8.

All despeckling filters apply to all types of ultrasound images and analyze with other speckle reduction techniques. In this study, a speckled image obtains as defined in [

The difference between speckled and despeckled images is obtained from the existing methods to validate the proposed method qualitatively to preserve fine details and edges. We depict the results in

The performance of the proposed technique analyzes quantitatively by a standard edge preservation index (β) metric. We use this metric to investigate the ability of the method to preserve sharp and weak edges. We list the values of β for various despeckling methods in

Image | GenLike | SNIG | Adaptive bilateral | ATMAV | Morphological operator on NSCT | Proposed method |
---|---|---|---|---|---|---|

Lena | 0.644 | 0.756 | 0.656 | 0.483 | 0.820 | 0.824 |

House | 0.730 | 0.700 | 0.747 | 0.644 | 0.808 | 0.828 |

Accordingly, using the 3 × 3 typical Laplacian function, ΔZ and

We use Signal to Noise Ratio (SNR) to analyze quantitatively for various despeckling methods, and it is defined by,_{D}, the difference in noise-free and denoised image variance, i.e.,

Picture type | Technique for analysis | Noise standard deviation (_{n}) |
||||
---|---|---|---|---|---|---|

0.60 | 0.70 | 0.80 | 0.90 | 1.00 | ||

Grayscale 8-bit 256 × 256 Lena picture | GenLike | 14.39 | 13.29 | 12.38 | 11.47 | 10.61 |

SNIG | 14.60 | 13.66 | 12.77 | 12.01 | 11.44 | |

ABF | 14.77 | 13.99 | 12.93 | 12.48 | 11.82 | |

ATMAV | 15.36 | 14.93 | 14.39 | 13.83 | 13.34 | |

Morphological operator on NSCT | 18.45 | 17.37 | 16.42 | 15.60 | 15.03 | |

Proposed fuzzy filter on NSCT | 18.35 | 17.62 | 16.60 | 15.84 | 15.20 |

We evaluated the performance of the suggested despeckling scheme with authentic ultrasound images. First, we removed the natural speckle noise from online ultrasound pictures,

The difference between noisy and denoised images obtained to evaluate the various despeckling techniques to preserve fine details is shown in

The case of authentic ultrasound images with the denoised image of different despeckling filtering and Noise level removed ultrasound picture depicted in

We take an adaptive fuzzy logic filter with a directional difference as a feature for defining membership function on NSCT as proposed in this article. In the formulated technique, adaptiveness integrates into two stages. We classify image regions at the first stage by applying the membership function [