This paper considers the problem of ventricular segmentation and visualisation from dynamic (4D) MR cardiac data covering an entire patient cardiac cycle, in a format that is compatible with the web. Four different me...
详细信息
This paper considers the problem of ventricular segmentation and visualisation from dynamic (4D) MR cardiac data covering an entire patient cardiac cycle, in a format that is compatible with the web. Four different methods are evaluated for the process of segmentation of the objects of interest: The k-means clustering algorithm, the fuzzy k-means (FkM) algorithm, self-organizing maps (SOMs) and seeded region growing algorithm. The technique of active surface is then subsequently applied to refine the segmentation results, employing a deformable generalised cylinder as geometric primitive. The final ventricular models are presented in VRML 2.0 format. The same process is repeated for all the 3D volumes of the cardiac cycle. The radial displacement between end systole and end diastole is calculated for each point of the active surface and is encoded in colour on the VRML vertex, using the RGB colour model. Using the VRML 2.0 specifications, morphing is performed showing all cardiac phases in real time. The expert has the ability to view the objects and interact with them using a simple internet browser. Preliminary results of normal and abnormal cases indicate that very important pathological situations (such as infarction) can be visualised and thus easily diagnosed and localised with the assistance of the proposed technique. (C) 1999 Elsevier Science B.V. All rights reserved.
The ASIC for multi-speaker speech recognition is design in this paper. The LPC-derived cepstral coefficients are chosen as speech features. Templates are trained by k-means clustering algorithm. Two stage recognition ...
详细信息
ISBN:
(纸本)7543909405
The ASIC for multi-speaker speech recognition is design in this paper. The LPC-derived cepstral coefficients are chosen as speech features. Templates are trained by k-means clustering algorithm. Two stage recognition system can not only improve recognition accuracy, but also reduce the delay. The first stage of recognition system uses speech spectrum difference(SSD) algorithm. The second stage uses DTW. The whole recognition system is design into ASIC on high level with VHDL and simulated in Powerview.
A feasibility study was conducted to segment 1.5T fMRIs into gray matter and large veins using individual pixel intensity and temporal phase delay as two correlated parameters in gradient echo images. The time-course ...
详细信息
ISBN:
(纸本)0819417815
A feasibility study was conducted to segment 1.5T fMRIs into gray matter and large veins using individual pixel intensity and temporal phase delay as two correlated parameters in gradient echo images. The time-course of each pixel in gradient echo images acquired during visual stimulation with a checkerboard flashing at 8Hz was correlated to the stimulation 'on'-'off' sequence to identify activated pixels. The temporal delay of each activated pixels was estimated by fitting its time-course to a reference sinusoidal function. The mean signal intensity difference of the activated pixels was computed by subtracting the average of the 'on' images from the average of the 'off' images. After replacing each activated pixel with 2D features (i.e., intensity and time-delay), a clustering method based on a k-meansalgorithm was employed to classify vein and tissue pixels. Good demarcation between large veins and activated gray matter was achieved with this method.
In agriculture, paddy crop monitoring placed a crucial role because it supports food security control. Water shortage, high cost of fertilizers, and soil deterioration were identified as some of the difficulties encou...
详细信息
In agriculture, paddy crop monitoring placed a crucial role because it supports food security control. Water shortage, high cost of fertilizers, and soil deterioration were identified as some of the difficulties encountered when monitoring rice crops through satellite images acquired by remote sensing. This study developed a deep learning method-assisted clusteringalgorithm (DLCA) which helps to improve the paddy growth identification process and enables the transparency of agricultural activity. Convolution neural network (CNN) has been utilized to identify crop growth which helps to understand drip irrigation and water scarcity for a particular crop. The experimental research shows that the proposed model is improved in identifying the paddy growth, soil availability, high cost of fertilizers, and soil degradation in monitoring paddy crop production through the satellite image process. Overall, the findings of the experiments have been carried out, and the proposed DLCA to achieve a lower error rate of 0.03 and high accuracy of 98.52%, MCC attains 98.43%, and F1-score 99.02% compared to other popular methods.
暂无评论