Ultrasound Image Enhancement using Dynamic Filtering

Ju, Shenggen; Zhou, Jiliu; He, Kun

doi:10.1109/icig.2007.43

Cited by 9 publications

(10 citation statements)

References 6 publications

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“…2c shows how standard FCM can be used for finding the Region Of Interest (ROI). Figure 3 shows the image clustering using Repulsive Fuzzy C-Means Clustering (Li and Liu, 2007;Cannon et al, 1986) applied on image corrupted with homogenous noise at σ = 0.015. Results show that the presence of noise affects the clustering process in terms of image quality and the efficiency.…”

Section: Resultsmentioning

confidence: 99%

“…In this study Repulsive FCM ( Li and Liu, 2007;Cannon et al, 1986) Algorithm has been tested on noisy breast US image. Results showed that our technique effectively clusters the breast malignant areas, which could take more computations by conventional clustering methods.…”

Section: Resultsmentioning

confidence: 99%

“…Fuzzy Filters was proposed (Li and Liu, 2007) and refined (Rashid et al, 2011;Cannon et al, 1986) and others. They use fuzzy logic model, which characterize the image as a fuzzy set of the fuzzy theory which is related to the rules of fuzzy inference model.…”

Section: Fuzzy C-means (Fcm) Based Clustering Techniquementioning

confidence: 99%

“…In this study we have performed image clustering using Repulsive Fuzzy C-means (FCM) algorithm (Li and Liu, 2007) in Ultrasound images which have the property of speckle noise. The motivation of this study is to devise a better diagnostic imaging for breast cancer detection.…”

Section: Introductionmentioning

confidence: 99%

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Novel Fuzzy Technique for Cancer Detection in Noisy Breast Ultrasound Images

Naomie¹

2012

American Journal of Applied Sciences

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Problem statement: Detecting tumor areas in breast Ultrasound (US) images is a challenging task. The occurrence of benign areas in breast may result in false identification of malignant areas which may have serious outcome. Approach: The CAD system could act as a major function in the early detection of breast cancer and could decrease the death rate among women with breast cancer. This challenge was especially daunting in non homogenous noisy US Images where benign and malignant images were difficult to identify. The US images possess speckle noise which was its inherent property. This study was an attempt to reduce false alarm in Breast cancer detection using computationally efficient fuzzy based image clustering. Results: The proposed system was tested using images which was obtained from the famous American Cancer database for conducting experiments. We had compared the Noise Induced images with that of the De-speckled images and found that the de-speckled images yeild a better image for diagnosis based. Later the image was clustered based on Fuzzy C-Means based clustering technique to identify the cancerous cells. Conclusion: An efficient method is suggested in this study which assist in diagnosing the cancer cells. The Fuzzy C-Means clustering system identifies various important artifacts, such as cyst, tumor and micro calcifications. The challenge in this system is the speckle noise. It can be extended to FCM class 2 non-homogeneous images.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Fuzzy C-means (Fcm) Based Clustering Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Novel Fuzzy Technique for Cancer Detection in Noisy Breast Ultrasound Images

Naomie¹

2012

American Journal of Applied Sciences

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“…Yang et al used histogram matching for enhancing the ultrasound images and their experiment results show their method can leave speckle unchanged and enhance tissue boundaries [8]. Li et al proposed an adaptive image enhancement method using a dynamic filtering for speckle detection [9].…”

Section: Introductionmentioning

confidence: 99%

Comparative Evaluation of Ultrasound Kidney Image Enhancement Techniques

Hafizah¹,

Supriyanto²

2011

IJCA

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Evaluation have been done to different enhancement techniques applied to ultrasound kidney images to see which enhancement techniques is the most suitable techniques that can be applied to the kidney images before segmenting the edge of the kidney. Five common enhancement techniques have been used including the spatial domain filtering, frequency domain filtering, histogram processing, morphological filtering and wavelet filtering. The techniques applied were assessed by few methods which are the observer sensitivity, measuring the image quality by calculating the MSE and PSNR of the image and applying one of the segmentation techniques to the output images. In conclusion, for ultrasound kidney image, if the whole image were taken into consideration (by measuring MSE and PSNR), morphological filtering seems to be the best option in enhancing the image. If the evaluator is concerning more on the kidney edges, enhancement techniques that should be taken into consideration are median filtering and histogram equalization. General Terms Image Processing KeywordsKeywords are your own designated keywords which can be used for easy location of the manuscript using any search engines.

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A review of computational methods applied for identification and quantification of atherosclerotic plaques in images

Jodas

Pereira

Tavares

2016

Expert Systems with Applications

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Evaluation of the composition of atherosclerotic plaques in images is an important task to determine their pathophysiology. Visual analysis is still as the most basic and often approach to determine the morphology of the atherosclerotic plaques. In addition, computer-aided methods have also been developed for identification of features such as echogenicity, texture and surface in such plaques. In this article, a review of the most important methodologies that have been developed to identify the main components of atherosclerotic plaques in images is presented. Hence, computational algorithms that take into consideration the analysis of the plaques echogenicity, image processing techniques, clustering algorithms and supervised classification used for segmentation, i.e. identification, of the atherosclerotic plaque components in ultrasound, computerized tomography and magnetic resonance images are introduced. The main contribution of this paper is to provide a categorization of the most important studies related to the segmentation of atherosclerotic plaques and its compo- * Corresponding author. Tel.: +351 22 508 1487; fax: +351 22 508 1445Email addresses: danilojodas@gmail.com (Danilo Samuel Jodas), aledir@sjrp.unesp.br (Aledir Silveira Pereira), tavares@fe.up.pt (João Manuel R. S. Tavares ) URL: http://www.fe.up.pt/~tavares (João Manuel R. S. Tavares ) October 12, 2015 nents in images acquired by the most used imaging modalities. In addition, the effectiveness and drawbacks of each methodology as well as future researches concerning the segmentation and classification of the atherosclerotic lesions are also discussed. Preprint submitted to Journal of L A T E X Templates

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Ultrasound Image Enhancement using Dynamic Filtering

Cited by 9 publications

References 6 publications

Novel Fuzzy Technique for Cancer Detection in Noisy Breast Ultrasound Images

Novel Fuzzy Technique for Cancer Detection in Noisy Breast Ultrasound Images

Comparative Evaluation of Ultrasound Kidney Image Enhancement Techniques

A review of computational methods applied for identification and quantification of atherosclerotic plaques in images

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