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Algorithms, Volume 5, Issue 3 (September 2012), Pages 318-397

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Research

Open AccessArticle Mammographic Segmentation Using WaveCluster
Algorithms 2012, 5(3), 318-329; doi:10.3390/a5030318
Received: 2 June 2012 / Revised: 10 July 2012 / Accepted: 20 July 2012 / Published: 10 August 2012
Cited by 2 | PDF Full-text (582 KB) | HTML Full-text | XML Full-text
Abstract
Segmentation of clinically relevant regions from potentially noisy images represents a significant challenge in the field of mammography. We propose novel approaches based on the WaveCluster clustering algorithm for segmenting both the breast profile in the presence of significant acquisition noise and [...] Read more.
Segmentation of clinically relevant regions from potentially noisy images represents a significant challenge in the field of mammography. We propose novel approaches based on the WaveCluster clustering algorithm for segmenting both the breast profile in the presence of significant acquisition noise and segmenting regions of interest (ROIs) within the breast. Using prior manual segmentations performed by domain experts as ground truth data, we apply our method to 150 film mammograms with significant acquisition noise from the University of South Florida’s Digital Database for Screening Mammography. We then apply a similar segmentation procedure to detect the position and extent of suspicious regions of interest. Our approach was able to segment the breast profile from all 150 images, leaving minor residual noise adjacent to the breast in three. Performance on ROI extraction was also excellent, with 81% sensitivity and 0.96 false positives per image when measured against manually segmented ground truth ROIs. When not utilizing image morphology, our approach ran in linear time with the input size. These results highlight the potential of WaveCluster as a useful addition to the mammographic segmentation repertoire. Full article
(This article belongs to the Special Issue Machine Learning for Medical Imaging 2012)
Open AccessArticle Use of Logistic Regression for Forecasting Short-Term Volcanic Activity
Algorithms 2012, 5(3), 330-363; doi:10.3390/a5030330
Received: 15 May 2012 / Revised: 28 July 2012 / Accepted: 7 August 2012 / Published: 22 August 2012
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Abstract
An algorithm that forecasts volcanic activity using an event tree decision making framework and logistic regression has been developed, characterized, and validated. The suite of empirical models that drive the system were derived from a sparse and geographically diverse dataset comprised of [...] Read more.
An algorithm that forecasts volcanic activity using an event tree decision making framework and logistic regression has been developed, characterized, and validated. The suite of empirical models that drive the system were derived from a sparse and geographically diverse dataset comprised of source modeling results, volcano monitoring data, and historic information from analog volcanoes. Bootstrapping techniques were applied to the training dataset to allow for the estimation of robust logistic model coefficients. Probabilities generated from the logistic models increase with positive modeling results, escalating seismicity, and rising eruption frequency. Cross validation yielded a series of receiver operating characteristic curves with areas ranging between 0.78 and 0.81, indicating that the algorithm has good forecasting capabilities. Our results suggest that the logistic models are highly transportable and can compete with, and in some cases outperform, non-transportable empirical models trained with site specific information. Full article
Open AccessArticle Incremental Clustering of News Reports
Algorithms 2012, 5(3), 364-378; doi:10.3390/a5030364
Received: 29 June 2012 / Revised: 13 August 2012 / Accepted: 15 August 2012 / Published: 24 August 2012
Cited by 3 | PDF Full-text (224 KB) | HTML Full-text | XML Full-text
Abstract
When an event occurs in the real world, numerous news reports describing this event start to appear on different news sites within a few minutes of the event occurrence. This may result in a huge amount of information for users, and automated [...] Read more.
When an event occurs in the real world, numerous news reports describing this event start to appear on different news sites within a few minutes of the event occurrence. This may result in a huge amount of information for users, and automated processes may be required to help manage this information. In this paper, we describe a clustering system that can cluster news reports from disparate sources into event-centric clusters—i.e., clusters of news reports describing the same event. A user can identify any RSS feed as a source of news he/she would like to receive and our clustering system can cluster reports received from the separate RSS feeds as they arrive without knowing the number of clusters in advance. Our clustering system was designed to function well in an online incremental environment. In evaluating our system, we found that our system is very good in performing fine-grained clustering, but performs rather poorly when performing coarser-grained clustering. Full article
Open AccessArticle Monitoring Threshold Functions over Distributed Data Streams with Node Dependent Constraints
Algorithms 2012, 5(3), 379-397; doi:10.3390/a5030379
Received: 19 June 2012 / Revised: 8 September 2012 / Accepted: 11 September 2012 / Published: 18 September 2012
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Abstract
Monitoring data streams in a distributed system has attracted considerable interest in recent years. The task of feature selection (e.g., by monitoring the information gain of various features) requires a very high communication overhead when addressed using straightforward centralized algorithms. While most [...] Read more.
Monitoring data streams in a distributed system has attracted considerable interest in recent years. The task of feature selection (e.g., by monitoring the information gain of various features) requires a very high communication overhead when addressed using straightforward centralized algorithms. While most of the existing algorithms deal with monitoring simple aggregated values such as frequency of occurrence of stream items, motivated by recent contributions based on geometric ideas we present an alternative approach. The proposed approach enables monitoring values of an arbitrary threshold function over distributed data streams through stream dependent constraints applied separately on each stream. We report numerical experiments on a real-world data that detect instances where communication between nodes is required, and compare the approach and the results to those recently reported in the literature. Full article

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