Online Update Scheme Research Articles

Online updating of context-aware landmark detectors for prostate localization in daily treatment CT images.

In image guided radiation therapy, it is crucial to fast and accurately localize the prostate in the daily treatment images. To this end, the authors propose an online update scheme for landmark-guided prostate segmentation, which can fully exploit valuable patient-specific information contained in the previous treatment images and can achieve improved performance in landmark detection and prostate segmentation. To localize the prostate in the daily treatment images, the authors first automatically detect six anatomical landmarks on the prostate boundary by adopting a context-aware landmark detection method. Specifically, in this method, a two-layer regression forest is trained as a detector for each target landmark. Once all the newly detected landmarks from new treatment images are reviewed or adjusted (if necessary) by clinicians, they are further included into the training pool as new patient-specific information to update all the two-layer regression forests for the next treatment day. As more and more treatment images of the current patient are acquired, the two-layer regression forests can be continually updated by incorporating the patient-specific information into the training procedure. After all target landmarks are detected, a multiatlas random sample consensus (multiatlas RANSAC) method is used to segment the entire prostate by fusing multiple previously segmented prostates of the current patient after they are aligned to the current treatment image. Subsequently, the segmented prostate of the current treatment image is again reviewed (or even adjusted if needed) by clinicians before including it as a new shape example into the prostate shape dataset for helping localize the entire prostate in the next treatment image. The experimental results on 330 images of 24 patients show the effectiveness of the authors' proposed online update scheme in improving the accuracies of both landmark detection and prostate segmentation. Besides, compared to the other state-of-the-art prostate segmentation methods, the authors' method achieves the best performance. By appropriate use of valuable patient-specific information contained in the previous treatment images, the authors' proposed online update scheme can obtain satisfactory results for both landmark detection and prostate segmentation.

IJARCCE - Computer and Communication Engineering

We describe data structures and an update strategy for e practical implementation of inverted indexes. The context of our discussion is the construction of a dedicated index engine for a distributed full-text information retrieval system but the results have wider applications. Retrieval operations require a single disk access per query term. The online update strategy guarantees the consistency of on-disk data structures. Index compression integrates smoothly.

Machine Condition Prediction Based on Adaptive Neuro–Fuzzy and High-Order Particle Filtering

Machine prognosis is a significant part of condition-based maintenance and intends to monitor and track the time evolution of a fault so that maintenance can be performed or the task can be terminated to avoid a catastrophic failure. A new prognostic method is developed in this paper using adaptive neuro-fuzzy inference systems (ANFISs) and high-order particle filtering. The ANFIS is trained via machine historical failure data. The trained ANFIS and its modeling noise constitute an mth-order hidden Markov model to describe the fault propagation process. The high-order particle filter uses this Markov model to predict the time evolution of the fault indicator in the form of a probability density function. An online update scheme is developed to adapt the Markov model to various machine dynamics quickly. The performance of the proposed method is evaluated by using the testing data from a cracked carrier plate and a faulty bearing. Results show that it outperforms classical condition predictors.

Segmenting CT prostate images using population and patient‐specific statistics for radiotherapy

In the segmentation of sequential treatment-time CT prostate images acquired in image-guided radiotherapy, accurately capturing the intrapatient variation of the patient under therapy is more important than capturing interpatient variation. However, using the traditional deformable-model-based segmentation methods, it is difficult to capture intrapatient variation when the number of samples from the same patient is limited. This article presents a new deformable model, designed specifically for segmenting sequential CT images of the prostate, which leverages both population and patient-specific statistics to accurately capture the intrapatient variation of the patient under therapy. The novelty of the proposed method is twofold: First, a weighted combination of gradient and probability distribution function (PDF) features is used to build the appearance model to guide model deformation. The strengths of each feature type are emphasized by dynamically adjusting the weight between the profile-based gradient features and the local-region-based PDF features during the optimization process. An additional novel aspect of the gradient-based features is that, to alleviate the effect of feature inconsistency in the regions of gas and bone adjacent to the prostate, the optimal profile length at each landmark is calculated by statistically investigating the intensity profile in the training set. The resulting gradient-PDF combined feature produces more accurate and robust segmentations than general gradient features. Second, an online learning mechanism is used to build shape and appearance statistics for accurately capturing intrapatient variation. The performance of the proposed method was evaluated on 306 images of the 24 patients. Compared to traditional gradient features, the proposed gradient-PDF combination features brought 5.2% increment in the success ratio of segmentation (from 94.1% to 99.3%). To evaluate the effectiveness of online learning mechanism, the authors carried out a comparison between partial online update strategy and full online update strategy. Using the full online update strategy, the mean DSC was improved from 86.6% to 89.3% with 2.8% gain. On the basis of full online update strategy, the manual modification before online update strategy was introduced and tested, the best performance was obtained; here, the mean DSC and the mean ASD achieved 92.4% and 1.47 mm, respectively. The proposed prostate segmentation method provided accurate and robust segmentation results for CT images even under the situation where the samples of patient under radiotherapy were limited. A conclusion that the proposed method is suitable for clinical application can be drawn.

Efficient Prefix Updates for IP Router Using Lexicographic Ordering and Updatable Address Set

Dynamic IP router table schemes, which have recently been proposed in the literature, perform an IP lookup or an online prefix update in O(log2|T|) memory accesses (MAs). In terms of lookup time, they are still slower than the full expansion/compression (FEC) scheme (compressed next-hop array/code word array (CNHA/CWA)), which requires exactly (at most) three MAs, irrespective of the number of prefixes |T| in a routing table T. The prefix updates in both FEC and CNHA/CWA have a drawback: Inefficient offline structure reconstruction is arguably the only viable solution. This paper solves the problem. We propose the use of lexicographic ordered prefixes to reduce the offline construction time of both schemes. Simulations on several real routing databases, run on the same platform, show that our approach constructs FEC (CNHA/CWA) tables in 2.68 to 7.54 (4.57 to 6) times faster than that from previous techniques. We also propose an online update scheme that, using an updatable address set and selectively decompressing the FEC and CNHA/CWA structures, modifies only the next hops of the addresses in the set. Recompressing the updated structures, the resulting forwarding tables are identical to those obtained by structure reconstructions, but are obtained at much lower computational cost. Our simulations show that the improved FEC and CNHA/CWA outperform the most recent O(log2|T|) schemes in terms of lookup time, update time, and memory requirement.

Industrial application of Wavelet Transform to the on-line prediction of side draw qualities of crude unit

In this paper, Wavelet Transform is applied to improve the on-line prediction of crude oil distillation (COD) qualities. First, the wavelets-based multi-scale analysis method was applied to treat the process data effectively. According to their unique characteristics with multi-scale Wavelet Transform, different variations are detected and modified, such as steps, peaks, noises, abnormal sudden changes, and so on. Using the process data with noises discarded and abnormal sudden changes treated effectively, the COD quality prediction can be sure of safety and high accuracy. Next, for some COD units on-line quality analyzers are available, and a new correcting strategy is taken in this paper to give effective update. The update is accomplished based on process data trends extraction. At last, the data processing and the on-line update strategy are introduced to a prediction system of COD product qualities to improve its prediction accuracy. The improved prediction system has been applied in a real plant, and the results are satisfactory.