Joint Sparse Representation of Brain Activity Patterns Related to Perceptual and Cognitive Components of a Speech Comprehension Task

Ramezani, Mahdi; Abolmaesumi, Purang; Marble, Kris; MacDonald, H.; Johnsrude, Ingrid S.

doi:10.1109/prni.2012.35

Cited by 7 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fMRI data were preprocessed using Statistical Parametric Mapping software (SPM8, Wellcome Department 1 The same dataset with similar description is used in [17].…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Classification based on fMRI data is not trivial, due to the high dimensionality of the input feature space and small number of subjects (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), approximately) that are usually available. In order to overcome these problems, we used the sparse mixing coefficients as input features.…”

Section: B Automatic Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Classification of individuals based on Sparse Representation of brain cognitive patterns: A functional MRI study

Ramezani

Abolmaesumi

Marble

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Self Cite

View full text Add to dashboard Cite

Many neurological disorders can change patterns of brain activity observed in functional imaging studies. These functional differences may be useful for classification of individuals into diagnostic categories. However, due to the high dimensionality of the input feature space and small set of subjects that are usually available, classification based on fMRI data is not trivial. Here, we evaluate the use of a Sparse Representation Analysis method within a Fisher Linear Discriminant (FLD) classification method, taking functional patterns characteristic of different cognitive tasks as the data input. As a test dataset, with a clear 'gold-standard' classification, we attempt to classify individuals as young, or older, based only on functional activation patterns in a speech listening task. Thirty two young (age: 19-26) and older (age: 57-73) adults (16 each) were scanned while listening to noise and to sentences degraded with noise, half of which contained meaningful context that could be used to enhance intelligibility. Different functional contrast images were used within K-SVD to generate basis activation sources and their corresponding sparse modulation profiles. Sparse modulation profiles were used in a FLD framework to classify individuals into the young and older categories. The results demonstrate the feasibility of the general approach, and confirm the potential applicability of the proposed method for real-world diagnostic problems.

show abstract

“…The fMRI data were preprocessed using Statistical Parametric Mapping software (SPM8, Wellcome Department 1 The same dataset with similar description is used in [17].…”

Section: Data Preprocessingmentioning

confidence: 99%

Section: B Automatic Classificationmentioning

confidence: 99%

Classification of individuals based on Sparse Representation of brain cognitive patterns: A functional MRI study

Ramezani

Abolmaesumi

Marble

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Self Cite

View full text Add to dashboard Cite

show abstract

Joint Sparse Coding Spatial Pyramid Matching for Classification of Color Blood Cell Image

Chao

2013

Machine Learning in Medical Imaging

View full text Add to dashboard Cite

Abstract. In the Automatic recognition of blood cell images, the color blood cell images are usually transformed into grayscale images for feature extraction, which result in losing plenty of useful color information. Although the sparse coding based linear spatial pyramid matching (ScSPM) is popular in grayscale image classification, the sparse coding methods in ScSPM fail to extract color information. In this paper, we proposed a novel joint sparse coding SPM (JScSPM) method by using the joint trained joint codebook. The joint codebook is able to represent the inner color correlation among different color components, and the individual color information of each color channel as well. JScSPM method was then applied to classify color blood cell images. The experimental results showed that the proposed method achieved mean 3.1% and 6.6% improvements on classification accuracy, compared with the majority voting based ScSPM the original ScSPM, respectively. IntroductionThe analysis of blood cells in microscope images is one of the most important steps in clinical hematological procedures. Consequently, the image-based automatic counting and classification of blood cells have attracted lots of attention. In current classification system, the colored blood cell images are usually transformed into grayscale images for further feature extraction, in order to simplify algorithms and reduce the computational complexity. However, plenty of useful color information will be discarded. Furthermore, there are few special designated color feature descriptors for blood cell images.In recent years, the sparse coding technique has been successfully used in image classification task [1], not only directly as a classifier, but also embedded in the classification framework [2]. The sparse coding based linear spatial pyramid matching (ScSPM) is a popular sparse coding embedded classification method developed on spatial pyramid matching (SPM) [2,3]. It computes a spatial pyramid image representation with sparse coding instead the vector quantization [2,3]. The ScSPM and its variations [2,4,5] have achieved great success in image classification. The main improvements in ScSPM mainly focus on applying different regularization terms for

show abstract

Joint sparse coding based spatial pyramid matching for classification of color medical image

Zhu

Sun

et al. 2015

Computerized Medical Imaging and Graphics

View full text Add to dashboard Cite

Joint Sparse Representation of Brain Activity Patterns Related to Perceptual and Cognitive Components of a Speech Comprehension Task

Cited by 7 publications

References 16 publications

Classification of individuals based on Sparse Representation of brain cognitive patterns: A functional MRI study

Classification of individuals based on Sparse Representation of brain cognitive patterns: A functional MRI study

Joint Sparse Coding Spatial Pyramid Matching for Classification of Color Blood Cell Image

Joint sparse coding based spatial pyramid matching for classification of color medical image

Contact Info

Product

Resources

About