The effects of the median filter with different window sizes for ultrasound image

Güngör, Murat Alparslan; Karagöz, İrfan

doi:10.1109/compcomm.2016.7924761

Cited by 12 publications

(6 citation statements)

References 25 publications

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“…(O) represents the elements (Cluster1) and the (x) symbol represents the elements (Cluster2) where cluster1 represents the images of the female embryos and cluster2 represents the images of the male embryos. As shown by the results of the diagram: The cluster1 consists of (39) images which are classified to (24) in correct form and it is indicated by the green color and (15) of them classified in wrong way that why it is indicated by the sign ( ) With a health rating of The results showed that the best method of classification was obtained from the application of the hybrid filter resulting from the application of the median filter with the wiener filter. The total error in both Claster1 and Claster2 was lower than the results of the other tables.…”

Section: Resultsmentioning

confidence: 99%

Determination of Fetal sex by Fetal anatomy parameters using a Fuzzy C-Mean Cluster

Kissara

Hassan

2023

Al-Kitab J. Pure Sci.

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This paper proposes a new approach to determining the sex of the fetus using the measurement of dimensions of the head. The research attempted to use one of the techniques of fuzzy logic in the field of medicine, and here it was dealt with the visual properties designed to mix the properties of fuzzy logic (FL) and feature images. The results that some traits cannot give good results such as the results obtained from the local binary pattern (LBP) algorithm and the power and superiority of the results of hybrid filters because the ultrasound images have a special color spectrum. The results also showed the ability of the fuzzy logic proposed by using the characteristics derived from the hybrid filter to deal with the study of images and to achieve a better diagnosis of the gender of the fetus through measuring the dimensions of the head.

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Section: Resultsmentioning

confidence: 99%

Determination of Fetal sex by Fetal anatomy parameters using a Fuzzy C-Mean Cluster

Kissara

Hassan

2023

Al-Kitab J. Pure Sci.

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“…Speckle noise, the most critical type of noise for degrading ultrasound image quality, is a product type in which the mean and standard deviation are mutually proportional and generally follow a Rayleigh distribution [ 18 ]. Improved median and Wiener filters have been widely used to reduce speckle noise generated in ultrasound images [ 19 , 20 ]. However, due to difficulties in processing the generated noise assuming a Gaussian distribution or when the mean value and variance of the image are proportional, many parts undesirable for the ultrasound image appear.…”

Section: Discussionmentioning

confidence: 99%

Improvement of Ultrasound Image Quality Using Non-Local Means Noise-Reduction Approach for Precise Quality Control and Accurate Diagnosis of Thyroid Nodules

Kim

Chon²,

Jeong

et al. 2022

IJERPH

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This study aimed to improve the quality of ultrasound images by modeling an algorithm using a non-local means (NLM) noise-reduction approach to achieve precise quality control and accurate diagnosis of thyroid nodules. An ATS-539 multipurpose phantom was used to scan the dynamic range and gray-scale measurement regions, which are most closely related to the noise level. A convex-type 3.5-MHz frequency probe is used for scanning according to ATS regulations. In addition, ultrasound images of human thyroid nodules were obtained using a linear probe. An algorithm based on the NLM noise-reduction approach was modeled based on the intensity and relative distance of adjacent pixels in the image, and conventional filtering methods for image quality improvement were designed as a comparison group. When the NLM algorithm was applied to the image, the contrast-to-noise ratio and coefficient of variation values improved by 28.62% and 19.54 times, respectively, compared with those of the noisy images. In addition, the image improvement efficiency of the NLM algorithm was superior to that of conventional filtering methods. Finally, the applicability of the NLM algorithm to human thyroid images using a high-frequency linear probe was validated. We demonstrated the efficiency of the proposed algorithm in ultrasound images and the possibility of capturing improved images in the dynamic range and gray-scale region for quality control parameters.

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“…The median filter can able to remove speckle noise from ultrasound images [39] . So, it can extract efficient features by using QFEM.…”

Section: Materials and Methodologymentioning

confidence: 99%

“…4 shows that the features of QFEM provide a clear separability among classes. The median filter can able to remove speckle noise from ultrasound images [39]. So, it can extract efficient features by using QFEM.…”

Section: ) Quantitative Feature Extraction Machinementioning

confidence: 99%

Particle Swarm Optimized Fuzzy CNN With Quantitative Feature Fusion for Ultrasound Image Quality Identification

Hossain

Hasan

Rahim

et al. 2022

IEEE J. Transl. Eng. Health Med.

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Background: Inherently ultrasound images are susceptible to noise which leads to several image quality issues. Hence, rating of an image's quality is crucial since diagnosing diseases requires accurate and high-quality ultrasound images. Methods: This research presents an intelligent architecture to rate the quality of ultrasound images. The formulated image quality recognition approach fuses feature from a Fuzzy convolutional neural network (fuzzy CNN) and a handcrafted feature extraction method. We implement the fuzzy layer in between the last max pooling and the fully connected layer of the multiple state-of-the-art CNN models to handle the uncertainty of information. Moreover, the fuzzy CNN uses Particle swarm optimization (PSO) as an optimizer. In addition, a novel Quantitative feature extraction machine (QFEM) extracts hand-crafted features from ultrasound images. Next, the proposed method uses different classifiers to predict the image quality. The classifiers categories ultrasound images into four types (normal, noisy, blurry, and distorted) instead of binary classification into good or poor-quality images. Results:The results of the proposed method exhibit a significant performance in accuracy (99.62%), precision (99.62%), recall (99.61%), and f1-score (99.61%). Conclusion: This method will assist a physician in automatically rating informative ultrasound images with steadfast operation in real-time medical diagnosis. INDEX TERMSUltrasound image, Quantitative feature extraction machine (QFEM), Particle swarm optimization (PSO), Feature fusion, Fuzzy convolutional neural network, Feature extraction. Clinical translation statement: This research has not been trialed in any real-life diagnosis. The whole research has been experimented with in our Lab setup. However, the research is designed to assist a physician.

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The effects of the median filter with different window sizes for ultrasound image

Cited by 12 publications

References 25 publications

Determination of Fetal sex by Fetal anatomy parameters using a Fuzzy C-Mean Cluster

Determination of Fetal sex by Fetal anatomy parameters using a Fuzzy C-Mean Cluster

Improvement of Ultrasound Image Quality Using Non-Local Means Noise-Reduction Approach for Precise Quality Control and Accurate Diagnosis of Thyroid Nodules

Particle Swarm Optimized Fuzzy CNN With Quantitative Feature Fusion for Ultrasound Image Quality Identification

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