Application of independent component analysis to magnetic resonance imaging for enhancing the contrast of gray and white matter

Nakai, Toshiharu; Muraki, Shigeru; Bagarinao, Epifanio; Miki, Yukio; Takehara, Yasuo; Matsuo, Keizo; Kato, Chikako; Sakahara, Harumi; Isoda, Haruo

doi:10.1016/j.neuroimage.2003.08.036

Cited by 50 publications

(42 citation statements)

References 19 publications

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“…This coincides with the idea of contrasting reflected in many color systems [10]. Meanwhile, it has been proven that independent component analysis can be applied to images for enhancing the contrast of different objects [26]. This paper describes a novel color independent components based SIFT descriptor (termed CIC-SIFT).…”

Section: Introductionsupporting

confidence: 53%

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Color Independent Components Based SIFT Descriptors for Object/Scene Classification

Han

Ruan

et al. 2010

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, we present a novel color independent components based SIFT descriptor (termed CIC-SIFT) for object/scene classification. We first learn an efficient color transformation matrix based on independent component analysis (ICA), which is adaptive to each category in a database. The ICA-based color transformation can enhance contrast between the objects and the background in an image. Then we compute CIC-SIFT descriptors over all three transformed color independent components. Since the ICA-based color transformation can boost the objects and suppress the background, the proposed CIC-SIFT can extract more effective and discriminative local features for object/scene classification. The comparison is performed among seven SIFT descriptors, and the experimental classification results show that our proposed CIC-SIFT is superior to other conventional SIFT descriptors.

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Section: Introductionsupporting

confidence: 53%

“…Toshiharu Nakai has shown that independent component analysis (ICA) can be applied to images for enhancing the contrast of different objects [26]. The keypoint of ICA is that we need use similar images (the flower), which have similar statistical properties, as training samples to learn the transformation matrix (the basis).…”

Section: Color Space Transformationmentioning

confidence: 99%

Color Independent Components Based SIFT Descriptors for Object/Scene Classification

Han

Ruan

et al. 2010

IEICE Trans. Inf. & Syst.

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“…In this study, we use the Infomax ICA Algorithm (Jung et al, 2001a), in which components are obtained through minimization of mutual information among output components. ICA has recently been used to separate mixed information into spatially stationary and temporally independent subcomponents in some ERP studies (Jung et al, 2001b;Makeig et al, 1999Makeig et al, , 2002 and in other branches of neuroscience and medicine (Brown et al, 2001;Calhoun et al, 2001;Nakai et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Combined ICA-LORETA analysis of mismatch negativity

Marco-Pallarés

Grau

Ruffini³

2005

NeuroImage

222

152

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A major challenge for neuroscience is to map accurately the spatiotemporal patterns of activity of the large neuronal populations that are believed to underlie computing in the human brain. To study a specific example, we selected the mismatch negativity (MMN) brain wave (an event-related potential, ERP) because it gives an electrophysiological index of a bprimitive intelligenceQ capable of detecting changes, even abstract ones, in a regular auditory pattern. ERPs have a temporal resolution of milliseconds but appear to result from mixed neuronal contributions whose spatial location is not fully understood. Thus, it is important to separate these sources in space and time. To tackle this problem, a two-step approach was designed combining the independent component analysis (ICA) and low-resolution tomography (LORETA) algorithms. Here we implement this approach to analyze the subsecond spatiotemporal dynamics of MMN cerebral sources using trial-by-trial experimental data. We show evidence that a cerebral computation mechanism underlies MMN. This mechanism is mediated by the orchestrated activity of several spatially distributed brain sources located in the temporal, frontal, and parietal areas, which activate at distinct time intervals and are grouped in six main statistically independent components. D 2004 Elsevier Inc. All rights reserved.

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“…Hyperspectral imaging has recently emerged as an advanced technique in remote sensing to deal with many issues that cannot be resolved by multispectral imaging, specifically, subpixel target detection and mixed pixel classification [9]. Its applications to MRI classification have been also explored in [10][11][12][13][14][15]. However, it seems that using the concept of hyperspectral imaging techniques for WMH detection in brain MRI has not been investigated.…”

Section: Introductionmentioning

confidence: 99%

A Hyperspectral Imaging Approach to White Matter Hyperintensities Detection in Brain Magnetic Resonance Images

et al. 2017

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White matter hyperintensities (WMHs) are closely related to various geriatric disorders including cerebrovascular diseases, cardiovascular diseases, dementia, and psychiatric disorders of elderly people, and can be generally detected on T2 weighted (T2W) or fluid attenuation inversion recovery (FLAIR) brain magnetic resonance (MR) images. This paper develops a new approach to detect WMH in MR brain images from a hyperspectral imaging perspective. To take advantage of hyperspectral imaging, a nonlinear band expansion (NBE) process is proposed to expand MR images to a hyperspectral image. It then redesigns the well-known hyperspectral subpixel target detection, called constrained energy minimization (CEM), as an iterative version of CEM (ICEM) for WMH detection. Its idea is to implement CEM iteratively by feeding back Gaussian filtered CEM-detection maps to capture spatial information. To show effectiveness of NBE-ICEM in WMH detection, the lesion segmentation tool (LST), which is an open source toolbox for statistical parametric mapping (SPM), is used for comparative study. For quantitative analysis, the synthetic images in BrainWeb provided by McGill University are used for experiments where our proposed NBE-ICEM performs better than LST in all cases, especially for noisy MR images. As for real images collected by Taichung Veterans General Hospital, the NBE-ICEM also shows its advantages over and superiority to LST.

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Application of independent component analysis to magnetic resonance imaging for enhancing the contrast of gray and white matter

Cited by 50 publications

References 19 publications

Color Independent Components Based SIFT Descriptors for Object/Scene Classification

Color Independent Components Based SIFT Descriptors for Object/Scene Classification

Combined ICA-LORETA analysis of mismatch negativity

A Hyperspectral Imaging Approach to White Matter Hyperintensities Detection in Brain Magnetic Resonance Images

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