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首页> 外文期刊>Neural computing & applications >Morphological segmenting and neighborhood pixel-based locality preserving projection on brain fMRI dataset for semantic feature extraction: an affective computing study
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Morphological segmenting and neighborhood pixel-based locality preserving projection on brain fMRI dataset for semantic feature extraction: an affective computing study

机译：语义分段和基于邻居像素的基于像素的脑部FMRI DataSet的位置提取：情感计算研究

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摘要

Two specific chemical receptive fields of brain, namely the amygdala and the orbital-frontal cortex, are related to valence and arousal in medical experiments. Functional magnetic resonance imaging (fMRI), which is a noninvasive, repeatable, and atomical tool for medical imaging in clinic system, was widely used in affective computing; however, it faces its dataset processing difficulty for dimensional reduction as well as for decreasing the computational complexity. In addition, features extraction from those de-dimensionality datasets is a challenging issue. The current work solved the de-dimensionality issue by using some preprocessing algorithms including clustering, morphological segmenting, and locality preserving projection. In order to keep useful information in fMRI dataset for reduction process, improved neighborhood pixel-based locality preserving projection (NP-LPP) algorithm was addressed and continuously for feature extraction operating using Otsu weighted sum of histogram. Furthermore, a modified covariance power spectral density (MC-PSD) separately in an fMRI Valence-Arousal experiments was measured. The results were analyzed and compared with affective norms English words system. The experiments established that the proposed methods of NP-LPP effectively simplified high complexity of fMRI, and Otsu weighted sum of histogram exhibited superior performance for features extraction compared to the MC-PSD through the calculation root mean standard error. The current proposed method provided a potential application and promising research direction on human semantic retrieval through medical imaging dataset.
机译：脑的两种特定的化学接受领域，即Amygdala和orbital-Frontal Cortex，与医学实验中的价和唤醒有关。功能性磁共振成像（FMRI）是临床系统中的非侵入性，可重复的和用于医学成像的原子工具，广泛用于情感计算;但是，它面临其数据集处理难度的尺寸减少以及降低计算复杂性。此外，来自这些去维性数据集的特征提取是一个具有挑战性的问题。目前的工作通过使用包括聚类，形态分割和定位投影的一些预处理算法解决了去维性问题。为了使有用的信息在FMRI数据集中进行用于还原过程，所改善的基于邻域像素的位置保存投影（NP-LPP）算法被寻址和连续用于使用OTSU加权的直方图操作的特征提取。此外，测量了在FMRI价值唤起实验中分开的改进的协方差功率谱密度（MC-PSD）。分析结果并与情感规范英语单词系统进行了分析。该实验确定，NP-LPP的提出方法有效地简化了FMRI的高度复杂性，并且通过计算根平均标准误差与MC-PSD相比，Totsu加权总和对特征提取的优异性能。目前提出的方法提供了通过医学成像数据集的人类语义检索的潜在应用和有前途的研究方向。

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