Robustness of local binary patterns in brain MR image analysis

Framework to Detect Schizophrenia in Brain MRI Slices with Mayfly Algorithm-Selected Deep and Handcrafted Features

Brain abnormality causes severe human problems, and thorough screening is necessary to identify the disease. In clinics, bio-image-supported brain abnormality screening is employed mainly because of its investigative accuracy compared with bio-signal (EEG)-based practice. This research aims to develop a reliable disease screening framework for the automatic identification of schizophrenia (SCZ) conditions from brain MRI slices. This scheme consists following phases: (i) MRI slices collection and pre-processing, (ii) implementation of VGG16 to extract deep features (DF), (iii) collection of handcrafted features (HF), (iv) mayfly algorithm-supported optimal feature selection, (v) serial feature concatenation, and (vi) binary classifier execution and validation. The performance of the proposed scheme was independently tested with DF, HF, and concatenated features (DF+HF), and the achieved outcome of this study verifies that the schizophrenia screening accuracy with DF+HF is superior compared with other methods. During this work, 40 patients’ brain MRI images (20 controlled and 20 SCZ class) were considered for the investigation, and the following accuracies were achieved: DF provided >91%, HF obtained >85%, and DF+HF achieved >95%. Therefore, this framework is clinically significant, and in the future, it can be used to inspect actual patients’ brain MRI slices.

Alzheimer's Disease Computer-Aided Diagnosis: Histogram-Based Analysis of Regional MRI Volumes for Feature Selection and Classification

This paper proposes a novel fully automatic computer-aided diagnosis (CAD) system for the early detection of Alzheimer's disease (AD) based on supervised machine learning methods. The novelty of the approach, which is based on histogram analysis, is twofold: 1) a feature extraction process that aims to detect differences in brain regions of interest (ROIs) relevant for the recognition of subjects with AD and 2) an original greedy algorithm that predicts the severity of the effects of AD on these regions. This algorithm takes account of the progressive nature of AD that affects the brain structure with different levels of severity, i.e., the loss of gray matter in AD is found first in memory-related areas of the brain such as the hippocampus. Moreover, the proposed feature extraction process generates a reduced set of attributes which allows the use of general-purpose classification machine learning algorithms. In particular, the proposed feature extraction approach assesses the ROI image separability between classes in order to identify the ones with greater discriminant power. These regions will have the highest influence in the classification decision at the final stage. Several experiments were carried out on segmented magnetic resonance images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) in order to show the benefits of the overall method. The proposed CAD system achieved competitive classification results in a highly efficient and straightforward way.

An investigation of CNN models for differentiating malignant from benign lesions using small pathologically proven datasets

Cancer has been one of the most threatening diseases to human health. There have been many efforts devoted to the advancement of radiology and transformative tools (e.g. non-invasive computed tomographic or CT imaging) to detect cancer in early stages. One of the major goals is to identify malignant from benign lesions. In recent years, machine deep learning (DL), e.g. convolutional neural network (CNN), has shown encouraging classification performance on medical images. However, DL algorithms always need large datasets with ground truth. Yet in the medical imaging field, especially for cancer imaging, it is difficult to collect such large volume of images with pathological information. Therefore, strategies are needed to learn effectively from small datasets via CNN models. To forward that goal, this paper explores two CNN models by focusing extensively on expansion of training samples from two small pathologically proven datasets (colorectal polyp dataset and lung nodule dataset) and then differentiating malignant from benign lesions. Experimental outcomes indicate that even in very small datasets of less than 70 subjects, malignance can be successfully differentiated from benign via the proposed CNN models, the average AUCs (area under the receiver operating curve) of differentiating colorectal polyps and pulmonary nodules are 0.86 and 0.71, respectively. Our experiments further demonstrate that for these two small datasets, instead of only studying the original raw CT images, feeding additional image features, such as the local binary pattern of the lesions, into the CNN models can significantly improve classification performance. In addition, we find that our explored voxel level CNN model has better performance when facing the small and unbalanced datasets.

Identification of the presence of ischaemic stroke lesions by means of texture analysis on brain magnetic resonance images

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Radiomics in conventionally segmented tissues can identify MRI scans that had a stroke.

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Patient’s advanced age can negatively influence classification results.

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Feature selection and stroke subtype influence but do not determine accuracy.

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Stroke subtype cannot be ascertained from texture analysis in brain tissues.

Background

The differential quantification of brain atrophy, white matter hyperintensities (WMH) and stroke lesions is important in studies of stroke and dementia. However, the presence of stroke lesions is usually overlooked by automatic neuroimage processing methods and the-state-of-the-art deep learning schemes, which lack sufficient annotated data. We explore the use of radiomics in identifying whether a brain magnetic resonance imaging (MRI) scan belongs to an individual that had a stroke or not.

Materials and methods

We used 1800 3D sets of MRI data from three prospective studies: one of stroke mechanisms and two of cognitive ageing, evaluated 114 textural features in WMH, cerebrospinal fluid, deep grey and normal-appearing white matter, and attempted to classify the scans using a random forest and support vector machine classifiers with and without feature selection. We evaluated the discriminatory power of each feature independently in each population and corrected the result against Type 1 errors. We also evaluated the influence of clinical parameters in the classification results.

Results

Subtypes of ischaemic strokes (i.e. lacunar vs. cortical) cannot be discerned using radiomics, but the presence of a stroke-type lesion can be ascertained with accuracies ranging from 0.7 < AUC < 0.83. Feature selection, tissue type, stroke subtype and MRI sequence did not seem to determine the classification results. From all clinical variables evaluated, age correlated with the proportion of images classified correctly using either different or the same descriptors (Pearson r = 0.31 and 0.39 respectively, p < 0.001).

Conclusions

Texture features in conventionally automatically segmented tissues may help in the identification of the presence of previous stroke lesions on an MRI scan, and should be taken into account in transfer learning strategies of the-state-of-the-art deep learning schemes.

pyHIVE, a health-related image visualization and engineering system using Python

Background

Imaging is one of the major biomedical technologies to investigate the status of a living object. But the biomedical image based data mining problem requires extensive knowledge across multiple disciplinaries, e.g. biology, mathematics and computer science, etc.

Results

pyHIVE (a Health-related Image Visualization and Engineering system using Python) was implemented as an image processing system, providing five widely used image feature engineering algorithms. A standard binary classification pipeline was also provided to help researchers build data models immediately after the data is collected. pyHIVE may calculate five widely-used image feature engineering algorithms efficiently using multiple computing cores, and also featured the modules of Principal Component Analysis (PCA) based preprocessing and normalization.

Conclusions

The demonstrative example shows that the image features generated by pyHIVE achieved very good classification performances based on the gastrointestinal endoscopic images. This system pyHIVE and the demonstrative example are freely available and maintained at http://www.healthinformaticslab.org/supp/resources.php.

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

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.

Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma

Lung cancer is the most common cause of cancer-related deaths in the USA. It can be detected and diagnosed using computed tomography images. For an automated classifier, identifying predictive features from medical images is a key concern. Deep feature extraction using pretrained convolutional neural networks (CNNs) has recently been successfully applied in some image domains. Here, we applied a pretrained CNN to extract deep features from 40 computed tomography images, with contrast, of non-small cell adenocarcinoma lung cancer, and combined deep features with traditional image features and trained classifiers to predict short- and long-term survivors. We experimented with several pretrained CNNs and several feature selection strategies. The best previously reported accuracy when using traditional quantitative features was 77.5% (area under the curve [AUC], 0.712), which was achieved by a decision tree classifier. The best reported accuracy from transfer learning and deep features was 77.5% (AUC, 0.713) using a decision tree classifier. When extracted deep neural network features were combined with traditional quantitative features, we obtained an accuracy of 90% (AUC, 0.935) with the 5 best post-rectified linear unit features extracted from a vgg-f pretrained CNN and the 5 best traditional features. The best results were achieved with the symmetric uncertainty feature ranking algorithm followed by a random forests classifier.

Classifying dementia using local binary patterns from different regions in magnetic resonance images

Dementia is an evolving challenge in society, and no disease-modifying treatment exists. Diagnosis can be demanding and MR imaging may aid as a noninvasive method to increase prediction accuracy. We explored the use of 2D local binary pattern (LBP) extracted from FLAIR and T1 MR images of the brain combined with a Random Forest classifier in an attempt to discern patients with Alzheimer's disease (AD), Lewy body dementia (LBD), and normal controls (NC). Analysis was conducted in areas with white matter lesions (WML) and all of white matter (WM). Results from 10-fold nested cross validation are reported as mean accuracy, precision, and recall with standard deviation in brackets. The best result we achieved was in the two-class problem NC versus AD + LBD with total accuracy of 0.98 (0.04). In the three-class problem AD versus LBD versus NC and the two-class problem AD versus LBD, we achieved 0.87 (0.08) and 0.74 (0.16), respectively. The performance using 3DT1 images was notably better than when using FLAIR images. The results from the WM region gave similar results as in the WML region. Our study demonstrates that LBP texture analysis in brain MR images can be successfully used for computer based dementia diagnosis.

Classification and localization of early-stage Alzheimer's disease in magnetic resonance images using a patch-based classifier ensemble

INTRODUCTION

Classification methods have been proposed to detect Alzheimer’s disease (AD) using magnetic resonance images. Most rely on features such as the shape/volume of brain structures that need to be defined a priori. In this work, we propose a method that does not require either the segmentation of specific brain regions or the nonlinear alignment to a template. Besides classification, we also analyze which brain regions are discriminative between a group of normal controls and a group of AD patients.

METHODS

We perform 3D texture analysis using Local Binary Patterns computed at local image patches in the whole brain, combined in a classifier ensemble.We evaluate our method in a publicly available database including very mild-to-mild AD subjects and healthy elderly controls.

RESULTS

For the subject cohort including only mild AD subjects, the best results are obtained using a combination of large (30×30×30 and 40×40×40 voxels) patches. A spatial analysis on the best performing patches shows that these are located in the medial-temporal lobe and in the periventricular regions. When very mild AD subjects are included in the dataset, the small (10×10×10 voxels) patches perform best, with the most discriminative ones being located near the left hippocampus.

CONCLUSION

We show that our method is able not only to perform accurate classification, but also to localize dis-criminative brain regions, which are in accordance with the medical literature. This is achieved without the need to segment-specific brain structures and without performing nonlinear registration to a template, indicating that the method may be suitable for a clinical implementation that can help to diagnose AD at an earlier stage.

ADHD classification by a texture analysis of anatomical brain MRI data

The ADHD-200 Global Competition provides an excellent opportunity for building diagnostic classifiers of Attention-Deficit/Hyperactivity Disorder (ADHD) based on resting-state functional MRI (rs-fMRI) and structural MRI data. Here, we introduce a simple method to classify ADHD based on morphological information without using functional data. Our test results show that the accuracy of this approach is competitive with methods based on rs-fMRI data. We used isotropic local binary patterns on three orthogonal planes (LBP-TOP) to extract features from MR brain images. Subsequently, support vector machines (SVM) were used to develop classification models based on the extracted features. In this study, a total of 436 male subjects (210 with ADHD and 226 controls) were analyzed to show the discriminative power of the method. To analyze the properties of this approach, we tested disparate LBP-TOP features from various parcellations and different image resolutions. Additionally, morphological information using a single brain tissue type (i.e., gray matter (GM), white matter (WM), and CSF) was tested. The highest accuracy we achieved was 0.6995. The LBP-TOP was found to provide better discriminative power using whole-brain data as the input. Datasets with higher resolution can train models with increased accuracy. The information from GM plays a more important role than that of other tissue types. These results and the properties of LBP-TOP suggest that most of the disparate feature distribution comes from different patterns of cortical folding. Using LBP-TOP, we provide an ADHD classification model based only on anatomical information, which is easier to obtain in the clinical environment and which is simpler to preprocess compared with rs-fMRI data.

Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification

Dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) of the breast has emerged as an adjunct imaging tool to conventional X-ray mammography due to its high detection sensitivity. Despite the increasing use of breast DCE-MRI, specificity in distinguishing malignant from benign breast lesions is low, and interobserver variability in lesion classification is high. The novel contribution of this paper is in the definition of a new DCE-MRI descriptor that we call textural kinetics, which attempts to capture spatiotemporal changes in breast lesion texture in order to distinguish malignant from benign lesions. We qualitatively and quantitatively demonstrated on 41 breast DCE-MRI studies that textural kinetic features outperform signal intensity kinetics and lesion morphology features in distinguishing benign from malignant lesions. A probabilistic boosting tree (PBT) classifier in conjunction with textural kinetic descriptors yielded an accuracy of 90%, sensitivity of 95%, specificity of 82%, and an area under the curve (AUC) of 0.92. Graph embedding, used for qualitative visualization of a low-dimensional representation of the data, showed the best separation between benign and malignant lesions when using textural kinetic features. The PBT classifier results and trends were also corroborated via a support vector machine classifier which showed that textural kinetic features outperformed the morphological, static texture, and signal intensity kinetics descriptors. When textural kinetic attributes were combined with morphologic descriptors, the resulting PBT classifier yielded 89% accuracy, 99% sensitivity, 76% specificity, and an AUC of 0.91.

Quantitative analysis of pulmonary emphysema using local binary patterns

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 k 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 |r| = 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.

Local structure-based region-of-interest retrieval in brain MR images

The aging population and the growing amount of medical data have increased the need for automated tools in the neurology departments. Although the researchers have been developing computerized methods to help the medical expert, these efforts have primarily emphasized to improve the effectiveness in single patient data, such as computing a brain lesion size. However, patient-to-patient comparison that should help improve diagnosis and therapy has not received much attention. To this effect, this paper introduces a fast and robust region-of-interest retrieval method for brain MR images. We make the following various contributions to the domains of brain MR image analysis, and search and retrieval system: 1) we show the potential and robustness of local structure information in the search and retrieval of brain MR images; 2) we provide analysis of two complementary features, local binary patterns (LBPs) and Kanade-Lucas-Tomasi feature points, and their comparison with a baseline method; 3) we show that incorporating spatial context in the features substantially improves accuracy; and 4) we automatically extract dominant LBPs and demonstrate their effectiveness relative to the conventional LBP approach. Comprehensive experiments on real and simulated datasets revealed that dominant LBPs with spatial context is robust to geometric deformations and intensity variations, and have high accuracy and speed even in pathological cases. The proposed method can not only aid the medical expert in disease diagnosis, or be used in scout (localizer) scans for optimization of acquisition parameters, but also supports low-power handheld devices.