Book Chapter10.1007/3-540-45365-2_29
A Distributed Genetic Algorithm for Parameters Optimization to Detect Microcalcifications in Digital Mammograms
Alessandro Bevilacqua,Renato Campanini,Nico Lanconelli +2 more
- 18 Apr 2001
- pp 278-287
23
TL;DR: This paper sets up a method for the detection of clustered microcalcifications in digital mammograms, based on statistical techniques and multiresolution analysis by means of wavelet transform, and finds out parameters not influencing performance at all.
read more
Abstract: In this paper, we investigate the improvement obtained by applying a distributed genetic algorithm to a problem of parameter optimization in medical images analysis. We setup a method for the detection of clustered microcalcifications in digital mammograms, based on statistical techniques and multiresolution analysis by means of wavelet transform. The optimization of this scheme requires multiple runs on a set of 40 images, in order to obtain relevant statistics. We aim to evaluate how fluctuations of some parameters values of the detection method influence the performance of our system. A distributed genetic algorithm supervising this process allowed to improve of some percents previous results obtained after having "hand tuned" these parameters for a long time. At last, we have been able to find out parameters not influencing performance at all.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
The Applications of Genetic Algorithms in Medicine.
TL;DR: The genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedic medicine, neurology, pharmacotherapy, and health care management.
165
Parallel genetic algorithms: advances, computing trends, applications and perspectives
Z. Konfrst
- 26 Apr 2004
TL;DR: This article gives a brief overview of theoretical advances, computing trends, applications and future perspectives in parallel genetic algorithms, and explains basic terms and behavior of (parallel) genetic algorithms.
117
Decision optimization of case-based computer-aided decision systems using genetic algorithms with application to mammography.
TL;DR: Experimental results show that the proposed optimization framework significantly improves the overall performance of the CAD system as well as its average specificity for high breast mass detection rates.
34
Recognition of blue-green algae in lakes using distributive genetic algorithm-based neural networks
TL;DR: Experimental results confirm that the proposed algorithm is superior to conventional GAs in terms of the convergence speed and solution precision, and is also capable of generating neural networks with significantly improved generalization performance.
27
Optimizing the automatic segmentation of the left ventricle in magnetic resonance images
E Angelié,de Pjh Patrick Koning,Mikhail G. Danilouchkine,van Hc Hans Assen,Gerhard Koning,van der Rj Rob Geest,Jhc Johan Reiber +6 more
TL;DR: Genetic algorithms are very suitable to automatically tune the parameters of a border detection algorithm and can be generalized to other optimization problems in medical image processing.
23
References
Analysis of mammographic microcalcifications using gray-level image structure features
TL;DR: A set of image structure features for classification of malignancy is defined and various features in each category were correlated with the biopsy examination results of 191 "difficult-to-diagnose" cases for selection of the best set of features representing the complete gray-level image structure information.
227
A genetic algorithm-based method for optimizing the performance of a computer-aided diagnosis scheme for detection of clustered microcalcifications in mammograms.
TL;DR: An automated method is developed for the determination of the parameter values that maximize the performance of a mammographic CAD scheme that utilizes a genetic algorithm to search through the possible parameter values, and provides the set of parameters that minimize a cost function which measures theperformance of the scheme.
62
Image feature analysis and computer‐aided diagnosis in mammography: Reduction of false‐positive clustered microcalcifications using local edge‐gradient analysis
TL;DR: To improve the performance of a computerized scheme for detection of clustered microcalcifications in digitized mammograms, causes of detected false-positivemicrocalcification signals were analyzed and the edge-gradient analysis and the linear-pattern analysis can reduce the false- positive detection rate, while maintaining a high level of the sensitivity.
60
•Journal Article
A Dynamic Load Balancing Method On A Heterogeneous Cluster Of Workstations.
TL;DR: A method to obtain load balancing for data parallel applications through dynamic data assignment and a simple priority mechanism on a heterogeneous cluster of workstations assuming no prior knowledge about the workload is introduced.
40
System for automatic detection of clustered microcalcifications in digital mammograms
Armando Bazzani,Dante Bollini,Rosa Brancaccio,Renato Campanini,Nico Lanconelli,D. Romani,Alessandro Bevilacqua +6 more
TL;DR: This paper investigates the performance of a Computer Aided Diagnosis (CAD) system for the detection of clustered microcalcifications in mammograms with a sensitivity of 91.4% with 0.4 false positive cluster per image on the 40 images of the Nijmegen database.
16