2 research outputs found
Detection of prostate cancer using multi-parametric magnetic resonance
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (leaves 26-28).A multi-channel statistical classifier to detect prostate cancer was developed by combining information from 3 different MR methodologies: T2-weighted, T2-mapping, and Line Scan Diffusion lmaging(LSDI). From these MR sequences, 4 sets of image intensities were obtained: T2-weighted(T2W) from T2-weighted imaging, Apparent Diffusion Coefficient(ADC) from LSDI, and Proton Density (PD) and T2 (T2Map) from T2-mapping imaging. Manually- segmented tumor labels from a radiologist were validated by biopsy results to serve as tumor "ground truth." Textural features were derived from the images using co-occurrence matrix and discrete cosine transform. Anatomical location of voxels was described by a cylindrical coordinate system. Statistical jack-knife approach was used to evaluate our classifiers. Single-channel maximum likelihood(ML) classifiers were based on 1 of the 4 basic image intensities. Our multi-channel classifiers: support vector machine (SVM) and fisher linear discriminant(FLD), utilized 5 different sets of derived features. Each classifer generated a summary statistical map that indicated tumor likelihood in the peripheral zone(PZ) of the gland. To assess classifier accuracy, the average areas under the receiver operator characteristic (ROC) curves were compared. Our best FLD classifier achieved an average ROC area of 0.839 (±0.064) and our best SVM classifier achieved an average ROC area of 0.761 (±0.043). The T2W intensity maximum likelihood classifier, our best single-channel classifier, only achieved an average ROC area of 0.599 (± 0.146). Compared to the best single-channel ML classifier, our best multi-channel FLD and SVM classifiers have statistically superior ROC performance with P-values of 0.0003 and 0.0017 respectively from pairwise 2-sided t-test. By integrating information from the multiple images and capturing the textural and anatomical features in tumor areas, the statistical summary maps can potentially improve the accuracy of image-guided prostate biopsy and enable the delivery of localized therapy under image guidance.by Ian Chan.M.Eng
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A unified framework for optimal resource allocation in multiuser multicarrier wireless systems
textNext-generation broadband wireless standards, e.g. IEEE 802.16e and Third Generation
Partnership Project – Long Term Evolution (3GPP-LTE), use Orthogonal
Frequency Division Multiple Access (OFDMA) as the preferred physical layer multiple
access scheme, esp. for the downlink. Due to the limited resources available at
the base station, e.g. bandwidth and power, intelligent allocation of these resources
to the users is crucial for delivering the best possible quality of service (QoS) to the
consumer with the least cost.
The problem of allocating time slots, subcarriers, rates, and power to the different
users in an OFDMA system has been an area of active research in recent years.
Previous research efforts in OFDMA resource allocation have typically focused on
maximizing instantaneous performance, i.e. the allocation decisions are performed
for the current time instant subject to the current resource constraints, which is
unable to fully utilize the time-varying nature of the wireless channel to improve
the communication performance of the system. This dissertation focuses instead on
maximizing time-averaged rates, allowing us to exploit the temporal dimension to
improve performance.
Furthermore, due to the difficult combinatorial nature of the problem, many
researchers in the past have focused on developing sub-optimal heuristic algorithms.
This dissertation proposes a unified algorithmic framework based on dual optimization
techniques that have complexities that are linear in the number of subcarriers
and users, and that achieve negligible optimality gaps in standards-based numerical
simulations. Adaptive algorithms based on stochastic approximation techniques
are also proposed, which are shown to achieve similar performance with even much
lower complexity.
Finally, it was assumed in previous work that perfect channel state information
(CSI) is available at the transmitter, which is quite unrealistic due to inevitable
channel estimation errors and feedback delay. This dissertation develops algorithms
assuming that only imperfect CSI is available, such that allocation decisions are
made while explicitly considering the error statistics of the CSI.Electrical and Computer Engineerin