Colonic Polyp Detection in CT Colonography with Fuzzy Rule Based 3D Template Matching

Kılıç, Niyazi; Uçan, Osman N.; Osman, Onur

doi:10.1007/s10916-008-9159-3

Cited by 13 publications

(5 citation statements)

References 20 publications

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“…The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drive s biological evolution. In recent years, the genetic algorithms are used in many medical applications [31].…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

3D Image Analysis and Artificial Intelligence for Bone Disease Classification

et al. 2009

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International audienceIn order to prevent bone fractures due to disease and ageing of the population, and to detect problems while still in their early stages, 3D bone micro architecture needs to be investigated and characterized. Here, we have developed various image processing and simulation techniques to investigate bone micro architecture and its mechanical stiffness. We have evaluated morphological, topological and mechanical bone features using artificial intelligence methods. A clinical study is carried out on two populations of arthritic and osteoporotic bone samples. The performances of Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Machines (SVM) and Genetic Algorithm (GA) in classifying the different samples have been compared. Results show that the best separation success (100 %) is achieved with Genetic Algorithm

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Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

3D Image Analysis and Artificial Intelligence for Bone Disease Classification

et al. 2009

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“…Template matching techniques were used to distinguish between abnormal lesions and FPs [10][11][12][13][14]. For example, such techniques were employed for the detection of nodules in chest computed tomographic (CT) scans [10], the detection of masses in mammograms [12], the detection of colon in abdominal CT scans [14], and the identification of the same patient [15].…”

Section: Introductionmentioning

confidence: 99%

“…For example, such techniques were employed for the detection of nodules in chest computed tomographic (CT) scans [10], the detection of masses in mammograms [12], the detection of colon in abdominal CT scans [14], and the identification of the same patient [15]. Although conventional template matching is useful, it has two pitfalls: (1) It needs to maintain a large number of templates to improve the detection performance, and (2) calculation of the cross-correlation (CC) coefficient with those templates is time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Eigenspace Template Matching for Detection of Lacunar Infarcts on MR Images

Uchiyama¹,

Abe

Muramatsu

et al. 2014

J Digit Imaging

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Detection of lacunar infarcts is important because their presence indicates an increased risk of severe cerebral infarction. However, accurate identification is often hindered by the difficulty in distinguishing between lacunar infarcts and enlarged Virchow-Robin spaces. Therefore, we developed a computer-aided detection (CAD) scheme for the detection of lacunar infarcts. Although our previous CAD method indicated a sensitivity of 96.8% with 0.71 false positives (FPs) per slice, further reduction of FPs remained an issue for the clinical application. Thus, the purpose of this study is to improve our CAD scheme by using template matching in the eigenspace. Conventional template matching is useful for the reduction of FPs, but it has the following two pitfalls: (1) It needs to maintain a large number of templates to improve the detection performance, and (2) calculation of the cross-correlation coefficient with these templates is time consuming. To solve these problems, we used template matching in the lower dimension space made by a principal component analysis. Our database comprised 1,143 T1- and T2-weighted images obtained from 132 patients. The proposed method was evaluated by using twofold cross-validation. By using this method, 34.1% of FPs was eliminated compared with our previous method. The final performance indicated that the sensitivity of the detection of lacunar infarcts was 96.8% with 0.47 FPs per slice. Therefore, the modified CAD scheme could improve FP rate without a significant reduction in the true positive rate.

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“…Applications include dyad pattern finding in DNA sequences (Zare-Mirakabad et al 2009), chemical structure-activity relationship analysis (Abu Hammad and Taha 2009), colonic polyp detection in CT colonography (Kilic et al 2009), structural properties of nanoalloys (Chen and Johnston 2008), automated identification of dementia using images (Xia et al 2008), automated protein crystal recognition (Po and Laine 2008), biochemical networks (Maurya et al 2009), and evolving k-nearest neighbor classifiers (Gil-Pita and Yao 2008).…”

Section: Introductionmentioning

confidence: 99%

Covariance matrix self-adaptation evolution strategies and other metaheuristic techniques for neural adaptive learning

Peterson

2010

Soft Comput

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A covariance matrix self-adaptation evolution strategy (CMSA-ES) was compared with several metaheuristic techniques for multilayer perceptron (MLP)-based function approximation and classification. Function approximation was based on simulations of several 2D functions and classification analysis was based on nine cancer DNA microarray data sets. Connection weight learning by MLPs was carried out using genetic algorithms (GA-MLP), covariance matrix self-adaptation-evolution strategies (CMSA-ES-MLP), back-propagation gradientbased learning (MLP), particle swarm optimization (PSO-MLP), and ant colony optimization (ACO-MLP). During function approximation runs, input-side activation functions evaluated included linear, logistic, tanh, Hermite, Laguerre, exponential, and radial basis functions, while the output-side function was always linear. For classification, the input-side activation function was always logistic, while the output-side function was always regularized softmax. Self-organizing maps and unsupervised neural gas were used to reduce dimensions of original gene expression input features used in classification. Results indicate that for function approximation, use of Hermite polynomials for activation functions at hidden nodes with CMSA-ES-MLP connection weight learning resulted in the greatest fitness levels. On average, the most elite chromosomes were observed for MLP (MSE ¼ 0:4977), CMSA-ES-MLP (0.6484), PSO-MLP (0.7472), ACO-MLP (1.3471), and GA-MLP (1.4845). For classification analysis, overall average performance of classifiers used was 92.64% (CMSA-ES-MLP), 92.22% (PSO-MLP), 91.30% (ACO-MLP), 89.36% (MLP), and 60.72% (GA-MLP).We have shown that a reliable approach to function approximation can be achieved through application of MLP connection weight learning when the assumed function is unknown. In this scenario, the MLP architecture itself defines the equation used for solving the unknown parameters relating input and output target values. A major drawback of implementing CMSA-ES into an MLP is that when the number of MLP weights is large, the OðN 3 Þ Cholesky factorization becomes a bottleneck for performance. As an alternative, feature reduction using SOM and NG can greatly enhance performance of CMSA-ES-MLP by reducing N: Future research into the speeding up of Cholesky factorization for CMSA-ES will be helpful in overcoming time complexity problems related to a large number of connection weights.

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Colonic Polyp Detection in CT Colonography with Fuzzy Rule Based 3D Template Matching

Cited by 13 publications

References 20 publications

3D Image Analysis and Artificial Intelligence for Bone Disease Classification

3D Image Analysis and Artificial Intelligence for Bone Disease Classification

Eigenspace Template Matching for Detection of Lacunar Infarcts on MR Images

Covariance matrix self-adaptation evolution strategies and other metaheuristic techniques for neural adaptive learning

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