Multispectral magnetic resonance image analysis using principal component and linear discriminant analysis

Convolutional neural networks to predict brain tumor grades and Alzheimer’s disease with MR spectroscopic imaging data

Purpose

To evaluate the value of convolutional neural network (CNN) in the diagnosis of human brain tumor or Alzheimer’s disease by MR spectroscopic imaging (MRSI) and to compare its Matthews correlation coefficient (MCC) score against that of other machine learning methods and previous evaluation of the same data. We address two challenges: 1) limited number of cases in MRSI datasets and 2) interpretability of results in the form of relevant spectral regions.

Methods

A shallow CNN with only one hidden layer and an ad-hoc loss function was constructed involving two branches for processing spectral and image features of a brain voxel respectively. Each branch consists of a single convolutional hidden layer. The output of the two convolutional layers is merged and fed to a classification layer that outputs class predictions for the given brain voxel.

Results

Our CNN method separated glioma grades 3 and 4 and identified Alzheimer’s disease patients using MRSI and complementary MRI data with high MCC score (Area Under the Curve were 0.87 and 0.91 respectively). The results demonstrated superior effectiveness over other popular methods as Partial Least Squares or Support Vector Machines. Also, our method automatically identified the spectral regions most important in the diagnosis process and we show that these are in good agreement with existing biomarkers from the literature.

Conclusion

Shallow CNNs models integrating image and spectral features improved quantitative and exploration and diagnosis of brain diseases for research and clinical purposes. Software is available at https://bitbucket.org/TeslaH2O/cnn_mrsi.

Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis

Seed aging detection and viable seed prediction are of great significance in alfalfa seed production, but traditional methods are disposable and destructive. Therefore, the establishment of a rapid and non-destructive seed screening method is necessary in seed industry and research. In this study, we used multispectral imaging technology to collect morphological features and spectral traits of aging alfalfa seeds with different storage years. Then, we employed five multivariate analysis methods, i.e., principal component analysis (PCA), linear discrimination analysis (LDA), support vector machines (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) to predict aged and viable seeds. The results revealed that the mean light reflectance was significantly different at 450~690 nm between non-aged and aged seeds. LDA model held high accuracy (99.8~100.0%) in distinguishing aged seeds from non-aged seeds, higher than those of SVM (87.4~99.3%) and RF (84.6~99.3%). Furthermore, dead seeds could be distinguished from the aged seeds, with accuracies of 69.7%, 72.0% and 97.6% in RF, SVM and LDA, respectively. The accuracy of nCDA in predicting the germination of aged seeds ranged from 75.0% to 100.0%. In summary, we described a nondestructive, rapid and high-throughput approach to screen aged seeds with various viabilities in alfalfa.

Comparison of Multispectral Image-Processing Methods for Brain Tissue Classification in BrainWeb Synthetic Data and Real MR Images

Accurate quantification of brain tissue is a fundamental and challenging task in neuroimaging. Over the past two decades, statistical parametric mapping (SPM) and FMRIB's Automated Segmentation Tool (FAST) have been widely used to estimate gray matter (GM) and white matter (WM) volumes. However, they cannot reliably estimate cerebrospinal fluid (CSF) volumes. To address this problem, we developed the TRIO algorithm (TRIOA), a new magnetic resonance (MR) multispectral classification method. SPM8, SPM12, FAST, and the TRIOA were evaluated using the BrainWeb database and real magnetic resonance imaging (MRI) data. In this paper, the MR brain images of 140 healthy volunteers (51.5 ± 15.8 y/o) were obtained using a whole-body 1.5 T MRI system (Aera, Siemens, Erlangen, Germany). Before classification, several preprocessing steps were performed, including skull stripping and motion and inhomogeneity correction. After extensive experimentation, the TRIOA was shown to be more effective than SPM and FAST. For real data, all test methods revealed that the participants aged 20–83 years exhibited an age-associated decline in GM and WM volume fractions. However, for CSF volume estimation, SPM8-s and SPM12-m both produced different results, which were also different compared with those obtained by FAST and the TRIOA. Furthermore, the TRIOA performed consistently better than both SPM and FAST for GM, WM, and CSF volume estimation. Compared with SPM and FAST, the proposed TRIOA showed more advantages by providing more accurate MR brain tissue classification and volume measurements, specifically in CSF volume estimation.

A novel approach for brain tumor detection by self-organizing map (SOM) using adaptive network based fuzzy inference system (ANFIS) for robotic systems

Purpose

One of the foremost research disciplines in medical image processing is to identify tumors, which is a challenging task practicing traditional methods. To overcome this, various research studies have been done effectively.

Design/methodology/approach

Medical image processing is evolving swiftly with modern technologies being developed every day. The advanced technologies improve medical fields in diagnosing diseases at the more advanced stages and serve to provide proper treatment.

Findings

Either the mass growth or abnormal growth concerning the cells in the brain is called a brain tumor.

Originality/value

The brain tumor can be categorized into two significant varieties, non-cancerous and cancerous. The carcinogenic tumors or cancerous is termed as malignant and non-carcinogenic tumors are termed benign tumors. If the cells in the tumor are healthy then it is a benign tumor, whereas, the abnormal growth or the uncontrollable growth of the cell is indicated as malignant. To find the tumor the magnetic resonance imaging (MRI) is carried out which is a tiresome and monotonous task done by a radiologist. In-order to diagnosis the brain tumor at the initial stage effectively with improved accuracy, the computer-aided robotic research technology is incorporated. There are numerous segmentation procedures, which help in identifying tumor cells from MRI images. It is necessary to select a proper segmentation mechanism to detect brain tumors effectively that can be aided with robotic systems. This research paper focuses on self-organizing map (SOM) by applying the adaptive network-based fuzzy inference system (ANFIS). The execution measures are determined to employ the confusion matrix, accuracy, sensitivity, and furthermore, specificity. The results achieved conclusively explicate that the proposed model presents more reliable outcomes when compared to existing techniques.

A novel method for the nondestructive classification of different-age Citri Reticulatae Pericarpium based on data combination technique

The quality of Citri Reticulatae Pericarpium (CRP) is closely correlated with the aging time. However, CRPs in different storage ages are similar in appearance, and the young CRP may be labeled as the aged one to obtain the excess profit by some unscrupulous traders. Most traditional analysis methods are laborious and time-consuming, and they can hardly realize the nondestructive classification. In this paper, a novel method based on near-infrared diffuse reflectance spectroscopy (NIRDRS) and data combination technique for the nondestructive classification of different-age CRPs was proposed. The CRPs in different storage ages (5, 10, 15, 20, and 25 years) were measured. The near-infrared spectra of outer skin and inner capsule were obtained. Principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and Fisher's linear discriminant analysis (FLD), with different data pretreatment methods, were used for the classification analysis. Data combination of the outer skin and inner capsule spectra was discussed for further improving the classification results. The results show that multiple sensors provide more useful and complementary information than a single sensor does for improving the prediction accuracy. With the help of data combination strategy, 100% prediction accuracy can be obtained with both second-order derivative-FLD and continuous wavelet transform-multiplicative scatter correction-FLD methods.

Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes

The INTERPRET project was a multicentre European collaboration, carried out from 2000 to 2002, which developed a decision-support system (DSS) for helping neuroradiologists with no experience of MRS to utilize spectroscopic data for the diagnosis and grading of human brain tumours. INTERPRET gathered a large collection of MR spectra of brain tumours and pseudo-tumoural lesions from seven centres. Consensus acquisition protocols, a standard processing pipeline and strict methods for quality control of the aquired data were put in place. Particular emphasis was placed on ensuring the diagnostic certainty of each case, for which all cases were evaluated by a clinical data validation committee. One outcome of the project is a database of 304 fully validated spectra from brain tumours, pseudotumoural lesions and normal brains, along with their associated images and clinical data, which remains available to the scientific and medical community. The second is the INTERPRET DSS, which has continued to be developed and clinically evaluated since the project ended. We also review here the results of the post-INTERPRET period. We evaluate the results of the studies with the INTERPRET database by other consortia or research groups. A summary of the clinical evaluations that have been performed on the post-INTERPRET DSS versions is also presented. Several have shown that diagnostic certainty can be improved for certain tumour types when the INTERPRET DSS is used in conjunction with conventional radiological image interpretation. About 30 papers concerned with the INTERPRET single-voxel dataset have so far been published. We discuss stengths and weaknesses of the DSS and the lessons learned. Finally we speculate on how the INTERPRET concept might be carried into the future.

Classification of organic and biological materials with deep ultraviolet excitation

We show that native fluorescence can be used to differentiate classes or groups of organic molecules and biological materials when excitation occurs at specific excitation wavelengths in the deep ultraviolet (UV) region. Native fluorescence excitation-emission maps (EEMs) of pure organic materials, microbiological samples, and environmental background materials were compared using excitation wavelengths between 200-400 nm with emission wavelengths from 270 to 500 nm. These samples included polycyclic aromatic hydrocarbons (PAHs), nitrogen- and sulfur-bearing organic heterocycles, bacterial spores, and bacterial vegetative whole cells (both Gram positive and Gram negative). Each sample was categorized into ten distinct groups based on fluorescence properties. Emission spectra at each of 40 excitation wavelengths were analyzed using principal component analysis (PCA). Optimum excitation wavelengths for differentiating groups were determined using two metrics. We show that deep UV excitation at 235 (+/-2) nm optimally separates all organic and biological groups within our dataset with >90% confidence. For the specific case of separation of bacterial spores from all other samples in the database, excitation at wavelengths less than 250 nm provides maximum separation with >6sigma confidence.

Improved temporal clustering analysis method for detecting multiple response peaks in fMRI

PURPOSE

To develop an improved temporal clustering analysis (TCA) method for detecting multiple active peaks by running the method once.

MATERIALS AND METHODS

Two cases of simulation data and a set of actual fMRI data from nine subjects were used to compare the traditional TCA method with the new method, termed extremum TCA (ETCA). The first case of simulation data simulated event-related activation and block activation in one cerebral area, and the second case simulated event-related activation and block activation in two cerebral areas. An in vivo visual stimulating experiment was performed on a 1.5T MR scanner. All imaging data were processed using both traditional TCA and the new method.

RESULTS

The results of both the simulated and actual fMRI data show that the new method is more sensitive and exact than traditional TCA in detecting multiple response peaks.

CONCLUSION

The new method is effective in detecting multiple activations even when the timing and location of the brain activation are completely unknown.