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Breast cancer, commonly found in women is a serious life threatening disease due to its invasive nature. Ultrasound (US) imaging method plays an effective role in screening early detection and diagnosis of Breast cancer. Speckle noise generally affects medical ultrasound images and also causes a number of difficulties in identifying the Region of Interest. Suppressing speckle noise is a challenging task as it destroys fine edge details. No specific filter is designed yet to get a noise free BUS image that is contaminated by speckle noise. In this paper M2 filter, a novel hybrid of linear and nonlinear filter is proposed and compared to other spatial filters with 3×3 kernel size. The performance of the proposed M2 filter is measured by statistical quantity parameters like MSE, PSNR and SSI. The experimental analysis clearly shows that the proposed M2 filter outperforms better than other spatial filters by 2% high PSNR values with regards to speckle suppression.

Keywords

Ultrasound Imaging, Speckle Noise, Spatial Filters, M3 Filter, M2 Filter.

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Modified NLM Model for Despeckling Ultrasound Images Using FCM Clustering Based Pre Classification and RIBM

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Authors

K. M. Prabusankarlal ¹, P. Thirumoorthy ², R. Manavalan ³, R. Sivaranjani ⁴

Affiliations
1 Department of Electronics and Communication, K.S. Rangasamy College of Arts and Science, IN
2 Department of Electronics and Communication, Government Arts College, Dharmapuri, IN
3 Department of Information Technology, Arignar Anna Government Arts College, IN
4 Department of Mathematics, K.S.R. College of Arts and Science for Women, IN

Source

ICTACT Journal on Image and Video Processing, Vol 8, No 3 (2018), Pagination: 1708-1715

Abstract

Speckle noise is an inherent characteristic of ultrasound which reduces the classification accuracy of computer aided diagnosis (CAD) systems. A modified non local means (NLM) filter for despeckling ultrasound images is proposed in this article. The proposed NLM model utilizes a preclassification method in which the feature vectors of the input image are constructed using moment invariants and then they are clustered using fuzzy c means (FCM) algorithm. The rotationally invariant block matching (RIBM) algorithm is applied among the blocks within each cluster instead of the entire image. This intra cluster block matching reduces computational complexity of NLM process without the elimination of any pixel candidate. Further, the rotationally invariant moment distance measure improves the noise reduction performance of the algorithm by increasing the chance of getting more similar candidates for NLM process. Extensive experiments are conducted using synthetic images, phantom images and ultrasound images. The method is comparatively evaluated with other denoising methods using statistical parameters such as MSE, PSNR, SSIM, EPI and ENL. The quantitative results suggested that the proposed method outperforms other four state of the art methods in despeckling and preservation of image details.

Keywords

Speckle Noise, Non Local Means, Fuzzy C Means, Ultrasound, CAD Systems.

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Despeckleing Prostate Ultrasonograms Using PDE with Wavelet

Abstract Views :191 | PDF Views:3

Authors

J. Ramesh ¹, R. Manavalan ²

Affiliations
1 Department of Computer Applications, K.S. Rangasamy College of Arts and Science, IN
2 Department of Computer Science, Arignar Anna Government Arts College, IN

Source

ICTACT Journal on Image and Video Processing, Vol 8, No 4 (2018), Pagination: 1776-1780

Abstract

Prostate cancer is the leading cause of death for men, since the cause of the disease is mysterious and its early detection is also monotonous. Ultrasound (US) is the most popular tool to detect the human organ glands and also used to diagnose the prostate cancer. Speckle noise is an inherent nature of ultrasound images, which degrades the image quality. So far, No specific filter is available to suppress the speckle noise in prostate image. In this paper, a novel despeckling method PDE with Wavelet is presented for prostate US images. The enhancement method is evaluated by using standard measures like Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR) and Edge Preservation Index (EPI). Further, the despeckling approaches' is also evaluated time and space complexity. From the results, it is observed that the filtering method PDE with Wavelet is superior to PDE in terms of denoising and also preserving the information content.

Keywords

Ultrasound Prostate Image, Partial Differential Equation, Wavelet.

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