Robustness of Local Binary Patterns in Brain MR Image Analysis

Ünay, Devrim; Ekin, Ahmet; Çetin, Müjdat; Jasinschi, Radu; Erçil, Aytül

doi:10.1109/iembs.2007.4352735

Cited by 46 publications

(35 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LBP unify structural and statistical information by a histogram of LBP codes that correspond to microstructures in the image at different scales. LBP have shown promising results in various applications in computer vision and have successfully been applied in a small number of other medical image analysis tasks, e.g., in mammographic mass detection [22] and magnetic resonance image analysis of the brain [23]. In [19], we showed that histogram dissimilarity measures between LBP feature histograms in a nearest neighbor classifier [24] can discriminate between emphysematous tissue and NT.…”

Section: Introductionmentioning

confidence: 89%

Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns

Srensen

Shaker

Bruijne

2010

IEEE Trans. Med. Imaging

335

246

View full text Add to dashboard Cite

Abstract-We aim at improving quantitative measures of emphysema in computed tomography (CT) images of the lungs. Current standard measures, such as the relative area of emphysema (RA), rely on a single intensity threshold on individual pixels, thus ignoring any interrelations between pixels. Texture analysis allows for a much richer representation that also takes the local structure around pixels into account. This paper presents a texture classification-based system for emphysema quantification in CT images. Measures of emphysema severity are obtained by fusing pixel posterior probabilities output by a classifier. Local binary patterns (LBP) are used as texture features, and joint LBP and intensity histograms are used for characterizing regions of interest (ROIs). Classification is then performed using a nearest neighbor classifier with a histogram dissimilarity measure as distance. A 95.2% classification accuracy was achieved on a set of 168 manually annotated ROIs, comprising the three classes: normal tissue, centrilobular emphysema, and paraseptal emphysema. The measured emphysema severity was in good agreement with a pulmonary function test (PFT) achieving correlation coefficients of up to = 0 79 in 39 subjects. The results were compared to RA and to a Gaussian filter bank, and the texture-based measures correlated significantly better with PFT than did RA.

show abstract

Section: Introductionmentioning

confidence: 89%

Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns

Srensen

Shaker

Bruijne

2010

IEEE Trans. Med. Imaging

335

246

View full text Add to dashboard Cite

show abstract

“…We represent this method as an Advanced Local Binary Pattern of Sign and Magnitude from Three Orthogonal Planes (ALBPSM-TOP). ALBPSM-TOP also implemented the utilizing rotation invariant and uniform approach [12,13]. An illustration of ALBPSM-TOP can be seen in Figure 2.…”

Section: Feature Extraction Using the Advanced Local Binary Patternmentioning

confidence: 99%

Texture and Gene Expression Analysis of the MRI Brain in Detection of Alzheimer’s Disease

Bustamam

Sarwinda

Ardenaswari

2017

Journal of Artificial Intelligence and Soft Computing Research

View full text Add to dashboard Cite

Alzheimer's disease is a type of dementia that can cause problems with human memory, thinking and behavior. This disease causes cell death and nerve tissue damage in the brain. The brain damage can be detected using brain volume, whole brain form, and genetic testing. In this research, we propose texture analysis of the brain and genomic analysis to detect Alzheimer's disease. 3D MRI images were chosen to analyze the texture of the brain, and microarray data were chosen to analyze gene expression. We classified Alzheimer's disease into three types: Alzheimer's, Mild Cognitive Impairment (MCI), and Normal. In this study, texture analysis was carried out by using the Advanced Local Binary Pattern (ALBP) and the Gray Level Co-occurrence Matrix (GLCM). We also propose the bi-clustering method to analyze microarray data. The experimental results from texture analysis show that ALBP had better performance than GLCM in classification of Alzheimer's disease. The ALBP method achieved an average value of accuracy of between 75% -100% for binary classification of the whole brain data. Furthermore, Biclustering method with microarray data shows good performance gene expression, where this information show influence Alzheimer's disease with total of bi-cluster is 6.

show abstract

“…Local Binary Pattern (LBP) is an easy-to-compute, robust local texture descriptor, and it has been shown to be promising in the computer vision field, including industrial inspection, motion analysis, and face recognition. In this paper, we show that LBP can solve iris feature extraction according the inherent intensity-related texture problem, is robust to some illumination and interference, and has potential for pattern recognitions [W.W. Boles, 1998;Devrim Unay, 2007; T. Ahonen, 2006]. For instance, in iris region, image intensity smoothly varies across an image.…”

Section: Iris Feature Extraction Based On Local Binary Patternmentioning

confidence: 99%