Wyniki wyszukiwania - BazTech

1

Bayesian HCS-based multi-SVNN: A classification approach for brain tumor segmentation and classification using Bayesian fuzzy clustering

Raju A. R., Suresh P., Rao R. R.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 3

646--660

EN

Brain tumor segmentation and classification is the interesting area for differentiating the tumerous and the non-tumerous cells in the brain and to classify the tumerous cells for identifying its level. The conventional methods lack the automatic classification and they consumed huge time and are ineffective in decision-making. To overcome the challenges faced by the conventional methods, this paper proposes the automatic method of classification using the Harmony-Crow Search (HCS) Optimization algorithm to train the multi-SVNN classifier. The brain tumor segmentation is performed using the Bayesian fuzzy clustering approach, whereas the tumor classification is done using the proposed HCS Optimization algorithm-based multi-SVNN classifier. The proposed method of classification determines the level of the brain tumor using the features of the segments generated based on Bayesian fuzzy clustering. The robust features are obtained using the information theoretic measures, scattering transform, and wavelet transform. The experimentation performed using the BRATS database conveys proves the effectiveness of the proposed method and the proposed HCS-based tumor segmentation and classification achieves the classification accuracy of 0.93 and outperforms the existing segmentation methods.

2

Automatic segmentation of infant brain MR images: With special reference to myelinated white matter

Devi C. N., Chandrasekharan A., Sundararaman V. K., Alex Z. C.

Biocybernetics and Biomedical Engineering

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2017

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Vol. 37, no. 1

143--158

EN

Automatic segmentation of infant brain images is faced with numerous challenges like poor image contrast, motion artifacts, and changes caused by progressive myelination of the infant brain. Since timely myelination points to normal brain maturity, monitoring the progress and degree of myelination is clinically significant. However, most of the existing segmentation methods do not segment myelinated portions of the infant brain. In this paper, we propose a segmentation approach focused on segmenting the myelinated white matter tissue in T1-weighted magnetic resonance images of the infant brain. The novelty of the algorithm lies in the introduction of a weighted localized Tsallis entropy based thresh-olding method. The proposed method is also tested on older babies beyond the one-year age mark to verify its utility and robustness. It is seen that the mean Dice coefficients obtained for myelin segmentation by the proposed weighted localized method are higher than that of the other methods, namely, the conventional Tsallis entropy thresholding and modified localized method.

3

Automatic contrast enhancement of brain MR images using Average Intensity Replacement based on Adaptive Histogram Equalization (AIR-AHE)

Isa I. S., Sulaiman S. N., Mustapha M., Karim N. K. A.

Biocybernetics and Biomedical Engineering

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2017

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Vol. 37, no. 1

24--34

EN

Medical imaging is the most established technique of visualizing the interior of the human body without the risk of the non-invasive effect. This technology is designed to produce images, and it is also capable of representing information about the screening location. In MRI imaging, the poor image quality particularly the low contrast image may provide insufficient data for the visual interpretation of such affected locations. Therefore, the need of image enhancement arises to improve image visions and also to computationally support the image processing technique. In general, conventional contrast enhancement methods may work well for some images. However, in MRI brain image, there are often more complex situations where the WMH signal is high but it may mistakenly be considered as other brain tissues such as CSF. With the motivation to classify the most possible WMH regions, this paper proposes a novel image contrast algorithm of WMH enhancement for MRI image. This algorithm is also known as the Average Intensity Replacement – Adaptive Histogram Equalization (AIR-AHE). The proposed algorithm is applied to the FLAIR image based on the intensity adjustment and contrast mapping techniques. The proposed algorithm for the image enhancement is superior to the existing methods by using image evaluation quantitative methods of PSNR, average gradient values and MSE. Furthermore, the edge information pertaining to the potential WMH regions can effectively increase the accuracy of the results.

4

Analysis and comparison of symmetry based lossless and perceptually lossless algorithms for volumetric compression of medical images

Chandrika B. K., Aparna P., Sumam D. S.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

147--154

EN

Modern medical imaging techniques produce huge volume of data from stack of images generated in a single examination. To compress them several volumetric compression techniques have been proposed. Performance of these compression schemes can be improved further by considering the anatomical symmetry present in medical images and incorporating the characteristics of human visual system. In this paper a volumetric medical image compression algorithm is presented in which perceptual model is integrated with a symmetry based lossless scheme. Symmetry based lossless and perceptually lossless algorithms were evaluated on a set of three dimensional medical images. Experimental results show that symmetry based perceptually lossless coder gives an average of 8.47% improvement in bit per pixel without any perceivable degradation in visual quality against the lossless scheme.

5

MRI image analysis in patients with a tumor of the central nervous system : an attempt of developing a management algorithm

Śniegocki M., Nowacka A., Fisz J., Śniegocka A., Buczkowski M.

Zeszyty Naukowe. Telekomunikacja i Elektronika / Uniwersytet Technologiczno-Przyrodniczy w Bydgoszczy

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2011

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z. 15

49-61

EN

Magnetic resonance imaging study is currently the reference method for the detection and diagnosis of the central nervous system tumors. A large number of tumors, especially high-grade, has a higher water content in the cells, which results in prolongation of MRI T1 and T2 what appearance as increased signal intensity in in T2-weighted images and the reduction in T1-weighted images. MRI can be performed with administration of contrast agent, which shortens T1 and increases signal on T1-weighted sequences. This allows to identify areas of increased angiogenesis), which is the exponent of the cancer malignancy degree and its biological activity. Obtained MRI images are analyzed and evaluated by a radiologist and a clinician. Most of the time it is the "by the eye" analysis, which is based on the MRI image evaluation by the generally accepted radiological standards. However, this method is relatively inaccurate. which in turn can bring to the wrong diagnosis of the disease and implementation or even lack of implementation of appropriate treatment. More and more researches are conducted in this area, but developed methods are usually very complicated and difficult to carry out by the "layman" which is the clinician. That is why the attempt is made, to develop a simple and clear algorithm for MRI image analysis in patients with the central nervous system tumors, allowing for quick and objective evaluation of magnetic resonance imaging study.

PL

Badanie metodą rezonansu magnetycznego jest aktualnie metodą referencyjną przy wykrywaniu i diagnozowaniu nowotworów centralnego układu nerwowego. Duża część nowotworów, zwłaszcza o wysokim stopniu złośliwości, charakteryzuje się większą zawartością wody w komórkach, co w badaniu MRI skutkuje wydłużeniem T1 i T2, uwidocznionym jako nasilenie sygnału w obrazach T2-zależnych oraz jego obniżeniem w obrazach T1-zależnych. MRI można przeprowadzić z podaniem środka kontrastowego, co powoduje skrócenie czasu T1 i podniesienie