Fuzzy neural network-based texture analysis of ultrasonic images

Pavlopoulos, S.; Kyriacou, Efthyvoulos; Koutsouris, Dimitris; Blekas, Konstantinos; Stafylopatis, Andreas; Zoumpoulis, P.S.

doi:10.1109/51.816243

Cited by 81 publications

(41 citation statements)

References 18 publications

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“…The principle cause of the further analysis is that we hope to segment more pathological change tissues from the abnormal regions. Thus, the proposed unsupervised segmentation algorithm plays an important role in the automatic computer aided diagnostic system, since the previous works about the diagnosis of ultrasonic liver image are all based on the supervised classification scheme [22][23][24][25][26][27][28]. The test sample is extracted from the region of interest that is given by experienced physician.…”

Section: Segmentation Of Ultrasonic Liver Imagementioning

confidence: 99%

“…Although they yield promising results to general texture analysis, they are unable to classify ultrasonic liver images adequately. Therefore, many researchers have fused several of these features to obtain an improved performance [10,[26][27][28][29]. Furthermore, Kadah et al [28] and Oosterveld et al [29] used the raw radio-frequency (RF) signal and conventional features to characterize liver diseases.…”

Section: Introductionmentioning

confidence: 99%

“…In the previous applications of these texture analysis algorithms that have been applied to characterize liver diseases, the supervised classification is adopted and the region of interest is given by experienced clinicians [18,[22][23][24][25][26][27][28]. Therefore, developing an unsupervised algorithm to decide the region of interest is essential in computer assisted diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised segmentation of ultrasonic liver images by multiresolution fractal feature vector

Lee

Chen

et al. 2005

Information Sciences

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Section: Segmentation Of Ultrasonic Liver Imagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Unsupervised segmentation of ultrasonic liver images by multiresolution fractal feature vector

Lee

Chen

et al. 2005

Information Sciences

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“…[1][2][3][4] Many methods are suggested to increase the contrast, eliminate scattered bright or dark pixels, and filter out noise and certain artifacts. Among the most popular are gray-level run techniques, 5 fast Fourier transforms, 6 and wavelet analysis. 7 None is universal in application, but individual user preference is commonly based on features of the data.…”

mentioning

confidence: 99%

Visualization of biological texture using correlation coefficient images

et al. 2006

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Abstract. Subsurface structural features of biological tissue are visualized using polarized light images. The technique of Pearson correlation coefficient analysis is used to reduce blurring of these features by unpolarized backscattered light and to visualize the regions of high statistical similarities within the noisy tissue images. It is shown that under certain conditions, such correlation coefficient maps are determined by the textural character of tissues and not by the chosen region of interest, providing information on tissue structure. As an example, the subsurface texture of a demineralized tooth sample is enhanced from a noisy polarized light image. Characterization of biological tissues from intrinsically noisy digital images is currently considered in the frames of several scientific approaches, i.e., pattern recognition, texture analysis, and image processing. [1][2][3][4] Many methods are suggested to increase the contrast, eliminate scattered bright or dark pixels, and filter out noise and certain artifacts. Among the most popular are gray-level run techniques, 5 fast Fourier transforms, 6 and wavelet analysis. 7 None is universal in application, but individual user preference is commonly based on features of the data. Texture analysis aims to characterize the image using a number of parameters that are sensitive to specific structural features of the object and especially to distinguish normal and abnormal biological tissues in the noisy images. For example, a few methods were developed for the detection of clustered microcalcifications from digital mammograms. [8][9][10] Texture enhancement is also required when imaging subsurface tissue structures with a diffusively backscattered polarized beam. Previously it was shown that imaging the degree of polarization can enhance the visibility of the hidden x-ray-induced early fibrosis of mouse skin.11 The analysis of Pearson correlation coefficients was also used to estimate local variations of the directionality and orientation of the structural elements. That work led us to visualize the hidden structures of biological tissues by imaging regions of statistical similarities using the correlation coefficient as the measure of comparison. The mathematical basis for this approach was established earlier by a mathematical proof that such a measure enabled one to reveal dependencies between random variables. 12,13 Biological tissues often exhibit characteristic regular features or ornamental patterns. Therefore, various regions of the tissue image can be statistically well correlated. By choosing a region of interest ͑ROI͒ and comparing it with other regions of the image through the correlation coefficient, it is possible to map the degree of statistical similarities. This mapping can carry valuable comparative information about the structural features of the tissue.Let us consider the main principles of image processing using correlation coefficients. To evaluate the spatial correlation in the raw image data, one has an intensity matrix G. Then two sim...

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“…Neural networks have a long history of success in classification problems, including disease characterization in ultrasound scans [17]. Neural networks have also been shown to be universal approximators, given a sufficient number of units in the network's hidden layers (groups of units between the input and output units).…”

Section: The Neural Approachmentioning

confidence: 99%

Classification and estimation of ultrasound speckle noise with neural networks

Wachowiak

Elmaghraby²,

Smolikova³

et al.

Proceedings IEEE International Symposium on Bio-Informatics and Biomedical Engineering

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This paper presents a neural-based approach to classifying and estimating the statistical parameters of speckle noise found in biomedical ultrasound images.Speckle noise, a very complex phenomenon, has been modeled an a variety of diflerent ways, and there is currently no clear consensus as to its precise statistical characteristics. I n this study, different neural network architectures are used to classify ultrasound images contaminated with three types of noise, based upon three one-parameter statistical distributions. At the same time, the parameter is estimated. It is expected that accurate characterization of ultrasound speckle noise will benefit existing post-processing methods, and may lead to new refinements in these techniques.

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Fuzzy neural network-based texture analysis of ultrasonic images

Cited by 81 publications

References 18 publications

Unsupervised segmentation of ultrasonic liver images by multiresolution fractal feature vector

Unsupervised segmentation of ultrasonic liver images by multiresolution fractal feature vector

Visualization of biological texture using correlation coefficient images

Classification and estimation of ultrasound speckle noise with neural networks

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