Yazar "Cinsdikici, Muhammed G." seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim Yok
    Öğe
    Robust activation detection methods for real-time and offline fMRI analysis
    (Elsevier Ireland Ltd, 2017) Oguz, Kaya; Cinsdikici, Muhammed G.; Gonul, Ali Saffet
    We propose two contributions with novel approaches to fMRI activation analysis. The first is to apply confidence intervals to locate activations in real-time, and second is a new metric based on robust regression of fMRI signals. These contributions are implemented in our four proposed methods; Instantaneous Activation Method (TAM), Instantaneous Activation Method with Past Blocks (TAMP) for real-time analysis, Task Robust Regression Distance Method (TRRD) for the new metric with robust regression and Instantaneous Robust Regression Distance Method (IRRD) for both contributions. For comparison, a statistical offline method called Task Activation Method (TAM) and a correlation analysis method are also implemented. The methods are initially evaluated with synthetic data generated using two different approaches; first using varying hemodynamic response function signals to simulate a wide range of stimuli responses, along with a Gaussian white noise, and second using no activity state data of a real fMRI experiment, which removes the need to generate noise. The methods are also tested with real fMRI experiments and compared with the results obtained by the widely used SPM tool. The results show that instantaneous methods reveal activations that are lost statistically in an offline analysis. They also reveal further improvements by robust fitting application, which minimizes the outlier effect. TRRD has an area under the ROC curve of 0,7127 for very noisy synthetic images, is reaching up to 0,9608 as the noise decreases, while the instantaneous score is in the range of 0,6124 to 0,8019 in the same noise levels. (C) 2017 Elsevier B.V. All rights reserved.
  • Küçük Resim Yok
    Öğe
    Three techniques for automatic extraction of corpus callosum in structural midsagittal brain MR images: Valley Matching, Evolutionary Corpus Callosum Detection and Hybrid method
    (Pergamon-Elsevier Science Ltd, 2014) Dagdeviren, Zuleyha Akusta; Oguz, Kaya; Cinsdikici, Muhammed G.
    Corpus callosum (CC) is an important structure for medical image registration. We propose three novel fully automated for the extraction of CC. Our first algorithm, Valley matching (VM), is based on fixed searched range in histogram processing and uses prior anatomical information for locating CC. The second one, Evolutionary CC Detection (ECD), based on genetic algorithm presents a new fitness function that uses anatomical ratios, instead of fixed prior knowledge without the need for preprocessing. The final one, called Evolutionary Valley Matching (EVM), takes advantages of the strong points of the first and second algorithms. The search space defined for ECD is reduced by VM which uses crowding method to find the peaks in the multi-modal histogram. Another important contribution of this study is that there is no existing method using genetic algorithm for extracting CC. Our proposed algorithms perform with the success rates up to 95.5%. (C) 2013 Elsevier Ltd. All rights reserved.
  • Küçük Resim Yok
    Öğe
    Vehicle-Classification Algorithm Based on Component Analysis for Single-Loop Inductive Detector
    (Ieee-Inst Electrical Electronics Engineers Inc, 2010) Meta, Soner; Cinsdikici, Muhammed G.
    This paper presents a novel vehicle-classification algorithm that uses the time-variable signal generated by a single inductive loop detector. In earlier studies, the noisy raw signal was fed into the algorithm by reducing its size with rough sampling. However, this approach loses the original signal form and cannot be the best exemplar vector. The developed algorithm suggests three contributions to cope with these problems. The first contribution is to clear the noise with discrete Fourier transform (DFT). The second contribution is to transfer the noiseless pattern into the Principal Component Analysis (PCA) domain. PCA is exploited not only for decorrelation but for explicit dimensionality reduction as well. This goal cannot be achieved by simple raw data sampling. The last contribution is to expand the principal components with a local maximum (L-max) parameter. It strengthens the classification accuracy by emphasizing the undercarriage height variation of the vehicle. These parameters are fed into the three-layered backpropagation neural network (BPNN). BPNN classifies the vehicles into five groups, and the recognition rate is 94.21%. This recognition rate has performed best, compared with the methods presented in published works.