Image Denoising via Combination Anisotropic Diffusion and Bilateral Filtering

Zeinali, Mohammad Hossein; Saryazdi, Saeı̈d; Rafsanjani, Hossein Khodabakhshi

doi:10.1109/cicn.2011.88

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Various imaging modalities are used for diagnosis of internal organs of the human body. A real time, noninvasive and nonionizing technique is Ultrasound (US) imaging technique [1,12]. The US images of soft tissue in human body are corrupted due to several noises such as additive noise, US device noise, multiplicative noise (speckle) etc.…”

Section: Introductionmentioning

confidence: 99%

Improved Despeckle Filtering Technique for Liver Cirrhosis US Images

Rajesh¹,

Priyadharson²

2018

IJET

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The liver ultrasonic (US) images suffer from inherent speckle noise, degrading the image quality; thereby, affecting human interpretation and also reducing the accuracy in computer-assisted diagnostic techniques. In this paper, we propose an improved despeckle filtering technique by combining, maximum homogeneity over a pixel neighborhood filtering (MHON) and speckle-reducing anisotropic diffusion filtering (SRAD) using a binary classifier map (BCM). The textural and fine details of residual image are denoised by SRAD filtering. The SRAD filter denoised image and MHON filter denoised image combines to attain BCM. A BCM is generated by examining the local coefficient of dispersion (CoD) assessed using a kernel with the one obtained from a specific region. The proposed method is evaluated on clinical US image set of liver, by assessing the quality factors PSNR, SNR, ISC, LMSE and EPI. Results are compared with specified algorithms separately while accomplishing for proposed method.

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Section: Introductionmentioning

confidence: 99%

Improved Despeckle Filtering Technique for Liver Cirrhosis US Images

Rajesh¹,

Priyadharson²

2018

IJET

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Laryngeal Tumor Detection and Classification in Endoscopic Video

Barbalata

Mattos

2016

IEEE J. Biomed. Health Inform.

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The development of the narrow-band imaging (NBI) has been increasing the interest of medical specialists in the study of laryngeal microvascular network to establish diagnosis without biopsy and pathological examination. A possible solution to this challenging problem is presented in this paper, which proposes an automatic method based on anisotropic filtering and matched filter to extract the lesion area and segment blood vessels. Lesion classification is then performed based on a statistical analysis of the blood vessels' characteristics, such as thickness, tortuosity, and density. Here, the presented algorithm is applied to 50 NBI endoscopic images of laryngeal diseases and the segmentation and classification accuracies are investigated. The experimental results show the proposed algorithm provides reliable results, reaching an overall classification accuracy rating of 84.3%. This is a highly motivating preliminary result that proves the feasibility of the new method and supports the investment in further research and development to translate this study into clinical practice. Furthermore, to our best knowledge, this is the first time image processing is used to automatically classify laryngeal tumors in endoscopic videos based on tumor vascularization characteristics. Therefore, the introduced system represents an innovation in biomedical and health informatics.

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Image Denoising via Combination Anisotropic Diffusion and Bilateral Filtering

Cited by 2 publications

References 13 publications

Improved Despeckle Filtering Technique for Liver Cirrhosis US Images

Improved Despeckle Filtering Technique for Liver Cirrhosis US Images

Laryngeal Tumor Detection and Classification in Endoscopic Video

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