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This paper presents the analysis of multi-channel electrogastrographic (EGG) signals using the continuous wavelet transform based on the fast Fourier transform (CWTFT). The EGG analysis was based on the determination of the several signal parameters such as dominant frequency (DF), dominant power (DP) and index of normogastria (NI). The use of continuous wavelet transform (CWT) allows for better visible localization of the frequency components in the analyzed signals, than commonly used short-time Fourier transform (STFT). Such an analysis is possible by means of a variable width window, which corresponds to the scale time of observation (analysis). Wavelet analysis allows using long time windows when we need more precise low-frequency information, and shorter when we need high frequency information. Since the classic CWT transform requires considerable computing power and time, especially while applying it to the analysis of long signals, the authors used the CWT analysis based on the fast Fourier transform (FFT). The CWT was obtained using properties of the circular convolution to improve the speed of calculation. This method allows to obtain results for relatively long records of EGG in a fairly short time, much faster than using the classical methods based on running spectrum analysis (RSA). In this study authors indicate the possibility of a parametric analysis of EGG signals using continuous wavelet transform which is the completely new solution. The results obtained with the described method are shown in the example of an analysis of four-channel EGG recordings, performed for a non-caloric meal. [ABSTRACT FROM AUTHOR]

Due to the complex topological structure of the coronary artery and the uneven distribution of the contrast agent, the angiography images are inevitably blurred and has low contrast, which causes great difficulty in process of segmentation. For this problem, a two-steps segmentation algorithm based on Hessian matrix and level set is proposed in this paper. Firstly, potential blood vessels of coronary images are preliminary extracted via Hessian matrix eigenvalues feature vectors of the geometric features and the response function. Then a novel regularization and area constraint is introduced into the local data energy fitting functional. Finally, the precision of Coronary Artery image is obtained in the evolution of the level set function. Experiments show that our proposed algorithm has better performance to these comparison segmentation algorithms. [ABSTRACT FROM AUTHOR]

Melanoma is a life threading disease when it grows outside the corium layer of the skin. Mortality rates of the Melanoma cases are maximum among the skin cancer patients. The cost required for the treatment of advanced melanoma cases is very high and the survival rate is low. Numerous computerized dermoscopy systems are developed based on the combination of shape, texture and color features to facilitate early diagnosis of melanoma. The availability and cost of the dermoscopic imaging system is still an issue. To mitigate this issue, this paper presented an integrated segmentation and Third Dimensional (3D) feature extraction approach for the accurate diagnosis of melanoma. A multi-atlas method is applied for the image segmentation. The patch-based label fusion model is expressed in a Bayesian framework to improve the segmentation accuracy. A depth map is obtained from the Two-dimensional (2D) dermoscopic image for reconstructing the 3D skin lesion represented as structure tensors. The 3D shape features including the relative depth features are obtained. Streaks are the significant morphological terms of the melanoma in the radial growth phase. The proposed method yields maximum segmentation accuracy, sensibility, specificity and minimum cost function than the existing segmentation technique and classifier. [ABSTRACT FROM AUTHOR]

In the ongoing days, the development of sight and sound substance and information stockpiling produces colossally. Clients can extricate any kind of data they require from recordings. This outcomes in quick development of video information, client's discover complexity while procurement their important data. To address this, EBR (Event Based Ranking) propose another way to deal with group and rank mixed media occasions based recordings as per client's advantage. Clients are generally keen on the best positioned and occasion pertinent recordings of returned query output. An occasion based positioning methodology which empowers clients to iteratively peruse the video as per their inclination. The proposed conspire has new way to deal with order and rank occasions based recordings. It improves the exactness of video recovery which incorporates certain functionalities for customized look. The data of clients' input is used in re-positioning technique to additionally enhance the recovering exactness. It gives the customized lastly re-positioned pertinent outcomes to shape a brought together precise query output. EBR is more precise in foreseeing and positioning client particular inclinations and diminishes the time many-sided quality. This Paper proposed a calculation comprises of: video rank calculation, occasion term suggestion, and client criticism and client session. [ABSTRACT FROM AUTHOR]

Meningioma is the one of the most common type of brain tumor, it as arises from the meninges and encloses the spine and the brain inside the skull. It accounts for 30% of all types of brain tumor. Meningioma's can occur in many parts of the brain and accordingly it is named. In this paper, a mixture model based classification of meningioma brain tumor using MRI image is developed. The proposed method consists of four stages. In the first stage, with respect to the cells' boundary, it is necessary to further processing, which ensures the boundary of some cells is a discrete region. Mathematical Morphology brings a fancy result during the discrete processing. Accurate cancer cell nucleus segmentation is necessary for automated cytological image analysis. Thresholding is a crucial step in segmentation..An adaptive binarization technique is an important step for medical image analysis.Finally, a novel hybrid Fuzzy SVM is designed in the classification stage meningioma brain tumor. The tumor classification results of proposed feature extraction with SVM is 74.24%, MM with FSVM is 82.67% and MM with RBF is 62.71% and our proposed method MM with Hybrid SVM is 91.64%. [ABSTRACT FROM AUTHOR]