A Comparative Analysis of Different Despeckling Filters Using Breast Ultrasonographic Images

Tripathi, Priyanshu; Dass, Rajeshwar; Sen, Jyotsna

doi:10.1007/978-981-16-8774-7_34

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…BUS image denoising is one of the essential steps to overcome this problem. Terefore, in this study, the SRAD flter is used to decrease noise and speckle at diferent scales and create a multiscale image series [3,40,41]. Here, the choice of denoising flter is motivated by the fact that SARD stops the difusion process across sharp edges that dynamically preserve the texture information while signifcantly reducing noise [34,42].…”

Section: Bus Image Denoisingmentioning

confidence: 99%

$m B C C_{f}$ : Multilevel Breast Cancer Classification Framework Using Radiomic Features

Panigrahi,

Chandra,

Srivastava

et al. 2024

International Journal of Intelligent Systems

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Breast cancer characterization remains a significant and challenging issue in contemporary medicine. Accurately distinguishing between malignant and benign breast lesions is crucial for effective diagnosis and treatment. The anatomical structure of malignant breast ultrasound images is more chaotic than that of benign images due to disease pathologies. However, texture-based analysis alone often fails to identify the extent of chaoticness in malignant breast ultrasound images due to their vague appearance with normal echo patterns, leading to missed diagnoses and increased mortality rates. To address this issue, we proposed an angular feature-based multilevel breast cancer classification framework mBCCf that aims to improve the accuracy and efficiency of classification. The proposed framework mimics the radiologist interpretation procedure by identifying the chaoticness on the periphery of the breast lesion in a breast ultrasound image (level-1). If the lesion contains an acute angle in any part of the periphery, it can be characterized as malignant or otherwise benign. However, solely relying on level-1 analysis may result in misclassification, especially when benign lesions exhibit echo patterns that resemble malignant ones. To overcome this limitation and to make the proposed system highly sensitive, advanced texture-based analysis (using combined shape, texture, and angular features) is performed (level-2). Finally, the performance of the proposed system is evaluated using a cross-dataset (consisting of 1293 breast ultrasound images) and compared with the different individual feature extraction techniques. Encouragingly, our system demonstrated an accuracy of 96.99% for classifying malignant and benign tumors, which is also validated using statistical analysis. The implications of our research lie in its potential to significantly improve breast cancer diagnosis by providing a reliable, efficient, and sensitive tool for radiologists.

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Section: Bus Image Denoisingmentioning

confidence: 99%

$m B C C_{f}$ : Multilevel Breast Cancer Classification Framework Using Radiomic Features

Panigrahi,

Chandra,

Srivastava

et al. 2024

International Journal of Intelligent Systems

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“…The authors in Ref. 30 have done comparative analysis on various speckle reduction ultrasound filters. The comparative testing is done on breast ultrasound images.…”

Section: Literature Surveymentioning

confidence: 99%

Total variation‐based ultrasound image despeckling using method noise thresholding in non‐subsampled contourlet transform

Singh

Diwakar

2023

Int J Imaging Syst Tech

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Medical diagnostics rely heavily on ultrasound imaging since it is non-invasive, inexpensive, and able to provide images in real time. However, the inherent existence of a kind of signal-dependent noise in these images diminishes the use of ultrasound imaging systems. Speckle noise is inherently present in ultrasound images. Its inherent presence occurs during the image acquisition phase. It decreases the diagnostic usefulness of this imaging modality since it interferes with and reduces the image resolution and contrast. Speckle noise is multiplicative in nature. This multiplicative nature of speckle noise causes adverse effects on ultrasound imaging because it distorts the image quality and causes a loss of the patient's informative content in ultrasound imaging. This causes difficulty in tissue characterization. A lot of research has been done in this field to remove speckle noise while preserving medical information in the image. To handle this issue, research has been grouped into multiple domains. The two main domains include homomorphic and non-homomorphic filtering. The homomorphic filtering uses a logarithmic transform that converts this multiplicative nature into an additive nature and uses any additive image restoration model to do a despeckling task. Non-homomorphic filtering encompasses all methods that do not employ a logarithmic transform. Since the presence of speckle noise in ultrasound imaging is an inherent property. Therefore, the despeckling of medical ultrasound images is a mandatory task that cannot be avoided or ignored. This paper proposes a two-step hybrid and homomorphic despeckling technique, where modified total variation method is applied as step one for speckle reduction purposes. Step two implements method of noise thresholding in the non-subsampled contourlet transform (NSCT) domain using the bivariate shrinkage rule for edge preservation. The effectiveness and reliability of the proposed method are tested by comparing it with some of the latest methods based on qualitative and quantitative analyses. This paper will help the new researchers understand various problems and their solutions related to ultrasound image despeckling.

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Effect of Speckle Filtering in the Performance of Segmentation of Ultrasound Images Using CNNs

Romero-Mercado

Contreras-Ortiz

Marrugo

2022

Communications in Computer and Information Science

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A Comparative Analysis of Different Despeckling Filters Using Breast Ultrasonographic Images

Cited by 3 publications

References 12 publications

$m B C C_{f}$ : Multilevel Breast Cancer Classification Framework Using Radiomic Features

$m B C C_{f}$ : Multilevel Breast Cancer Classification Framework Using Radiomic Features

Total variation‐based ultrasound image despeckling using method noise thresholding in non‐subsampled contourlet transform

Effect of Speckle Filtering in the Performance of Segmentation of Ultrasound Images Using CNNs

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A Comparative Analysis of Different Despeckling Filters Using Breast Ultrasonographic Images

Cited by 3 publications

References 12 publications

m B C C f : Multilevel Breast Cancer Classification Framework Using Radiomic Features

m B C C f : Multilevel Breast Cancer Classification Framework Using Radiomic Features

Total variation‐based ultrasound image despeckling using method noise thresholding in non‐subsampled contourlet transform

Effect of Speckle Filtering in the Performance of Segmentation of Ultrasound Images Using CNNs

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$m B C C_{f}$ : Multilevel Breast Cancer Classification Framework Using Radiomic Features

$m B C C_{f}$ : Multilevel Breast Cancer Classification Framework Using Radiomic Features