SpectraClassifier 1.0: a user friendly, automated MRS-based classifier-development system
- Sandra Ortega-Martorell†1, 2,
- Iván Olier†3, 1,
- Margarida Julià-Sapé†2, 1 and
- Carles Arús1, 2Email author
© Ortega-Martorell et al; licensee BioMed Central Ltd. 2010
Received: 23 September 2009
Accepted: 24 February 2010
Published: 24 February 2010
SpectraClassifier (SC) is a Java solution for designing and implementing Magnetic Resonance Spectroscopy (MRS)-based classifiers. The main goal of SC is to allow users with minimum background knowledge of multivariate statistics to perform a fully automated pattern recognition analysis. SC incorporates feature selection (greedy stepwise approach, either forward or backward), and feature extraction (PCA). Fisher Linear Discriminant Analysis is the method of choice for classification. Classifier evaluation is performed through various methods: display of the confusion matrix of the training and testing datasets; K-fold cross-validation, leave-one-out and bootstrapping as well as Receiver Operating Characteristic (ROC) curves.
SC is composed of the following modules: Classifier design, Data exploration, Data visualisation, Classifier evaluation, Reports, and Classifier history. It is able to read low resolution in-vivo MRS (single-voxel and multi-voxel) and high resolution tissue MRS (HRMAS), processed with existing tools (jMRUI, INTERPRET, 3DiCSI or TopSpin). In addition, to facilitate exchanging data between applications, a standard format capable of storing all the information needed for a dataset was developed. Each functionality of SC has been specifically validated with real data with the purpose of bug-testing and methods validation. Data from the INTERPRET project was used.
SC is a user-friendly software designed to fulfil the needs of potential users in the MRS community. It accepts all kinds of pre-processed MRS data types and classifies them semi-automatically, allowing spectroscopists to concentrate on interpretation of results with the use of its visualisation tools.
Since the demonstration in 1989 that different brain tumour types displayed distinct spectral patterns , it became apparent that in order to determine whether in-vivo 1H-MRS had any clinical diagnostic value it was necessary first to gather a sufficiently large database of brain tumour 1H-MRS data and second, to perform statistical analysis of these multiple spectral features [2, 3], which is frequently known as Pattern Recognition analysis (PR) or classification.
It was shown later on that it was possible to carry out a successful PR of the four most common brain tumour types, on a multicentre database of in-vivo single-voxel (SV) 1H-MRS data acquired at 1.5T . The study was subsequently refined during the INTERPRET project , which successfully developed a PR-based decision-support system to assist radiologists in diagnosing and grading brain tumours using SV MRS data. However, the need for tools that allowed a rapid development of multiple classifiers for the already existing databases available  remained. This should also allow to rapidly test hypothesis that may surface during the lengthy process of data collection, especially in prospective studies . This is especially relevant in the case of studies on human subjects , for instance with multi-voxel (MV) tumour data [8–10].
Moreover, the ever-increasing amount of biological data generated by metabolomics techniques also requires a tool allowing quick hypothesis testing on data that are difficult and expensive to gather [11–14]. In this sense, the PR analysis becomes just one stage in the iterative process of data-driven biological knowledge discovery.
However, 1H-MRS data are commonly analysed with either commercial (SPSS , SAS , SIMCA-P+ ), non commercial (R ) or home-made programs running over statistical packages of MATLAB , and usually require a certain degree of mathematical expertise for testing each individual hypothesis [20–22]. Some other packages for PR and classifier development (AMIX  and Pirouette ) are less complex tools, but commercial. Furthermore, Pirouette is platform-limited, because it is designed specifically for the Windows operating system.
Therefore, in order to facilitate the development of MRS-based classifiers, we developed SpectraClassifier (SC), a Java software solution to design and implement classifiers based on MRS data. The main goal of SC is to allow a user with minimum background knowledge of multivariate statistics to perform a fully automated PR analysis, from the feature extraction and/or selection stage to the evaluation of the developed classifier.
The purpose of this report is to describe SC, from the algorithms implemented to its main functionalities, with a focus on the different MRS data types it is able to work with. In addition, a standard format for exchanging either SV, MV or high-resolution MRS data for pattern recognition studies will also be described.
Pattern recognition techniques
PR techniques aim to recognize and classify data (patterns) into different categories using the observed features. To do this, one of the possibilities is to base the development on a machine learning (ML) approach, in which a dataset is used to fit an adaptive model to solve the problem. ML provides the mathematical and computational mechanisms to infer knowledge in a formal model from specific data of a given domain .
Selecting and/or extracting features
Several Feature Selection (FS) or Feature Extraction (FE) methods based on pattern recognition have been applied to the significant part of the spectra (MRS frequencies, in this case), looking for a subset of relevant peak heights of typical resonances (ppm, "part per million") or a reduced representation set of combinations of them. By removing most irrelevant and redundant information from the data, the valuable selected features help to improve the performance of learning models [27, 28].
FE works by combining the existing data features into new ones that best describe the whole dataset according to a given criterion. It is, therefore, mostly a dimensionality reduction approach. FS, on the other hand, provides a selected subset of frequencies sufficient to classify tumour cases with reasonable accuracy.
SC implements two sequential FS methods based on a "hill climbing" search (Greedy Stepwise approach), either forward or backward, and evaluates the selected features with a CFS (Correlation-based Feature Subset) evaluator . This Java class evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them. Subsets of features that are highly correlated with the class while having low intercorrelation are preferred.
In addition to these FS methods, a FE method is also implemented: PCA (Principal Component Analysis). PCA performs a principal components analysis and transformation of the data, used in conjunction with a Ranker search (for ranking attributes by their individual evaluations). Dimensionality reduction is accomplished by choosing enough eigenvectors to account for a predefined percentage of the variance in the original data (we set it by default to cover a 95% of the variance, but this value can be modified by the user). Attribute noise can be filtered by transforming to the principal component space, eliminating some of the worst eigenvectors, and then transforming back into the original space.
Creating a classifier
The purpose of creating a classifier is to separate data vectors into one of two or more classes based on a set of features that better describe the data (features selected and/or extracted). In general, we assign a data vector to one of a number of classes based on observations made on the data. These classes are already known or predetermined.
At the moment, SC uses Fisher Linear Discriminant Analysis (Fisher LDA) as the technique of choice for distinguishing cases between two, three or four classes. Fisher LDA is a fundamental and widely used technique, that provides a reasonable way of reducing the dimensionality of the problem . With the software, the user can assign each class to a different tumour type, or to a super-class generated by grouping tumour types .
As the original version of Fisher LDA does not assume any probability distribution to define the model, the limitation of Fisher LDA for estimating the probability of a case of belonging to a class, has been overcome by approximating the resulting projections through spherical Gaussian distributions, one for each class. The centre of each distribution has been assumed as the class mean estimated from data and the standard deviation common to all. Therefore, the probability of membership of every case to each class is estimated applying the Bayes' theorem over these distributions .
Confusion matrix: each row of the matrix represents the members in a predicted class, while each column represents the actual value of members in the original class. It is a visualisation tool mainly used in supervised learning.
Cross-validation: one round of cross-validation involves partitioning a dataset into complementary subsets, performing the training on one subset, and validating the model on the other subset. In K-fold cross-validation, the original dataset is partitioned into K subsamples. Of the K subsamples, a single subsample is retained as testing data for testing the model, and the remaining K-1 subsamples are used as training data. The cross-validation process is then repeated K times (the folds), with each of the K subsamples used exactly once as testing data. The K results from the folds can then be averaged to produce a single estimation of prediction accuracy . In SC, the K value can be set by the user. It is typically used in scenarios where the goal is prediction, and it is desirable to estimate how accurately a predictive model will perform in practice.
Leave-One-Out (LOO): is a special case of a K-fold cross-validation. It uses a single case from the original dataset as testing data, and the remaining cases as training data. This is repeated such that each case in the dataset is used once as testing data. This is the same as a K-fold cross-validation with K being equal to the number of cases in the original dataset.
Bootstrapping: it is implemented by constructing a number N of bootstrap cases of the observed dataset (and of equal size to the training dataset), each of which is obtained by random sampling with replacement from the original dataset (there is nearly always duplication of individual cases in a bootstrap dataset). The N results from the bootstrap samples can then be averaged to produce a single estimation . In SC, we set N equal to 1000 by default, but this value can be modified by the user. Bootstrapping could be better at estimating error rates in a linear discriminant problem, outperforming simple cross-validation .
Receiver Operating Characteristic (ROC) curve: is a graphical plot of the sensitivity (True Positive Rate, TPR) vs. 1-specificity (False Positive Rate, FPR) for a binary classifier system as its discrimination threshold is varied .
SC is aimed at being an intuitive and user-friendly software tool. It has been built using the Java programming language (Java Platform, Standard Edition 6), ensuring the platform independence of SC. JFC/Swing classes were used to provide a Graphical User Interface (GUI) for the program. Java Runtime Environment 1.6 or later version is required to run the program.
In addition to the implementations of the methods described above, SC uses some open-source and well-known libraries, such as: Weka , a collection of machine learning algorithms, used in SC for selecting and extracting features; JavaStat , developed for performing basic statistics, used in SC for the implementation of the classification method (specifically the Discriminant Analysis class ); and KiNG (Kinemage, Next Generation) , which is an interactive system for three-dimensional vector graphics, and is used in SC to visualise the canonical variables or the components projection.
For exchanging data between applications, the development of a standard format capable of storing all the information needed for a dataset in a readable way was required. The selected language to create this format was XML (Extensible Markup Language) , the meta-markup language developed by the World Wide Web Consortium (W3C) which provides a general method of representing structured documents and data in the form of lexical trees.
Main capabilities of SC
In this section, the standard format definition for exchanging data and the main capabilities of SC will also be described. The development of a classifier will also be illustrated through computer screenshots. At the end of this section, some annotations about the validation with real data and computational consumption of the SC will be provided. For more detailed technical information about SC, please see the help and manual of the software in the Additional file 1.
Standard format definition for data exchange
The development of a three-class classifier using SC is demonstrated throughout this report, using for this example a short TE SV training dataset of MR brain tumour data from INTERPRET [5, 6, 38]. In the example, "Class 1" is named low-grade m and contains 58 cases of the meningiomas (mm) type; "Class 2" is named aggressive and contains 86 cases of the glioblastoma multiforme (gl) type and 38 cases of the metastases (me) type; and "Class 3" is named low-grade g and contains 22 cases of the low grade astrocytomas (a 2) type, 6 cases of the oligoastrocytomas (oa) type and 7 of the oligodendrogliomas (od) type.
Importing and exporting datasets
In-vivo SV data, usually with a low number of points per spectrum (512-2048):
1.1. File with extension .txt or .art in the INTERPRET  canonical format, with 512 points in the [7.2; -2.8] ppm range, which only contains the information of one spectrum in one row. Similarly, files with extension .dat, exported with SPSS or similar, and composed by rows of 514 tokens, where the first row is columns labels (not used in SC), and the rest of rows correspond to cases (similar to the INTERPRET canonical format), having the following information each: identifier of the class, identifier of the case, and 512 points of the spectrum.
In-vivo MV data, also with a low number of points per spectrum (512-2048), but with a large number of spectra per acquisition (n × n). SC treats each acquisition as one dataset:
High resolution data, usually with a large number of points per spectrum (16-32 K points).
3.1. File with extension .txt, for HRMAS. The original file having been processed with TopSpin  or similar and exported as text file. The number of points accepted is variable; the most commonly used are from 1600 to 3200, with a [4.5; 0.5] ppm range. Each file only contains the information of one spectrum in one column.
The implementation of this visualisation uses the KiNG library, which is called from SC to load a preformatted kinemage made automatically with the information of the data visualisation. In cases of one or two dimensions (when creating two-class or three-class classifiers), the boundaries of the classes will be calculated and displayed (Figure 8).
Classifier evaluation and Reports
The Classifier history tab can store the main description of the classifiers chosen by the user. It can be used to compare these classifiers, checking variations of results obtained when developing classifiers with different parameters.
Validation with real data
Comparing results for the validation of SC, using PCA prior to LDA.
Long TE - 
Long TE - SC
Short TE - 
Short TE - SC
AUC ± SE
AUC ± SE
AUC ± SE
AUC ± SE
1 vs. 2
0.953 ± 0.031 (8)
0.977 ± 0.016 (8)
0.956 ± 0.028 (4)
0.923 ± 0.028 (4)
1 vs. 3
0.593 ± 0.104 (6)
0.757 ± 0.054 (6)
0.591 ± 0.097 (4)
0.688 ± 0.056 (4)
1 vs. 4
0.918 ± 0.063 (7)
0.941 ± 0.025 (7)
0.966 ± 0.029 (3)
0.962 ± 0.019 (3)
2 vs. 3
0.961 ± 0.038 (5)
0.970 ± 0.028 (5)
0.954 ± 0.044 (4)
0.972 ± 0.021 (4)
2 vs. 4
0.931 ± 0.073 (10)
0.999 ± 0.003 (10)
0.997 ± 0.009 (11)
1.000 ± 0.000 (11)
3 vs. 4
0.961 ± 0.053 (4)
0.995 ± 0.010 (4)
0.986 ± 0.025 (2)
0.979 ± 0.025 (2)
Comparing results for the validation of SC, using FS prior to LDA.
Accuracy ± standard deviation in
Accuracy ± standard deviation in SC
88.82 ± 4.51
90.73 ± 1.97
82.50 ± 5.31
85.12 ± 2.51
Long + Short TE
88.71 ± 4.54
90.31 ± 2.16
The computing time needed to develop a classifier depends on the dataset size. For example, in a 3 GHz CPU and 2 GB RAM personal computer, the typical performance values for INTERPRET 512-point files (188 points in the range of interest) in a classification problem with 217 cases and three classes are 4 seconds for feature selection with the sequential forward method and 3 seconds for Fisher LDA. For the same problem with concatenated spectra (i.e. 1024 points, 376 points in the range of interest, and 195 cases), times are 50 and 3 seconds, respectively. For HRMAS spectra of 1639 points (1310 points in the range of interest), feature selection takes 30 min with the same conditions. The computing time should be quite reduced using a high performance server.
Discussion and conclusions
SpectraClassifier is a user-friendly software for performing PR of MRS data, which has been designed to fulfil the needs of potential users in the MRS community. It works with all types of MRS, i.e. SV, MV and high-resolution data (HRMAS). In addition, it also supports two concatenated spectra of the same resolution and number of points, since it had been previously shown that combination of data from two different TEs can provide useful additional information for classification [44, 45].
SC allows easy data exploration, with four different spectra visualisers through which individual cases, class mean, standard deviation, as well as the selected classification features in each experiment can be explored. Classification results are shown both visually and numerically. The data visualisation tab allows feedback on classification errors through potential outlier analysis by using the four spectra visualisers.
The software is limited in two aspects: first, only very basic PR techniques have been implemented yet and second, at the moment its data reading capabilities span a few formats (i.e. data files that can be read by jMRUI).
With respect to the first limitation, it has been shown  for in-vivo SV 1H-MRS data, in a multicentre multiproject evaluation of classification methods for brain tumours that in fact most methods give comparable results. This has been as well shown in other PR challenges [20, 21]. For this reason, we consider that the low number of methods implemented should not be considered as a drawback of SC.
Other widely used softwares [12–14] have also this limitation, such as SIMCA-P+, AMIX, and Pirouette, offering methods such as PCA, Partial Least Square (PLS), Soft Independent Modelling of Class Analogy (SIMCA), Principal Component Regression (PCR), and Classical Least Square (CLS).
With respect to the second limitation, i.e. format reading, in fact the lack of a common exchange format affects all areas of work of the MRS community, especially for clinical scanners. Although a standard DICOM had been defined , its implementation in output formats from in-vivo human scanners at 1.5 T (the most common in the clinic) is still far from being general. For this reason, we decided to leave the pre-processing step to the user (Figure 1) in order to minimise the number of different formats that have to be understood by SC. The program has been made compatible with the INTERPRET DSS software for in-vivo MRS data, which is accessible at no cost, upon signature of a disclaimer form . Since INTERPRET developed a canonical format for in-vivo MRS data at 1.5T, it would therefore be possible for users with their own databases of 1.5T data to process data from different manufacturers and sweep widths and number of points with the DSS itself , and to export those into SC. At the same time, it is also possible to enter jMRUI-processed data into SC. jMRUI is able to read most existing clinical scanner formats. The future version of jMRUI (v 4.0), which will be able to accept plug-ins, should allow jMRUI and SC to connect with one another, adding the pre-processing step to the pipeline for MRS data analysis through the developed XML format. In this way, SC relies on existing processing software for format conversion and pre-processing and concentrates on the PR process.
Future SC developments include testing the software performance with other PR problems and with different data types (MV and HRMAS).
In conclusion, SC is a software that accepts all kinds of pre-processed MRS data types and classifies them semi-automatically, allowing spectroscopists to concentrate on interpretation of results with the use of its visualisation tools. The classifiers created can be exported as XML files for their easy implementation into decision-support systems (DSS), such as the INTERPRET DSS [5, 47].
Availability and requirements
Project name: SpectraClassifier (SC)
Project home page: http://gabrmn.uab.es/sc
Operating system(s): Platform independent
Programming language: Java
Other requirements: Java virtual machine 1.6.0 or higher
License: Available free of charge
Any restriction to use by non-academics: Subject to the signature of a disclaimer and user agreement text available at the project homepage.
Area Under the Curve
Correlation-based Feature Subset
Classical Least Square
Digital Imaging and Communication in Medicine
Decision Support System
False Positive Rate
Java Foundation Classes
Linear Discriminant Analysis
Magnetic Resonance Spectroscopy
Principal Component Analysis
Principal Component Regression
Partial Least Square
Receiver Operating Characteristic
Soft Independent Modelling of Class Analogy
True Positive Rate
Extensible Markup Language.
This work was funded by CIBER-BBN (an initiative of the Spanish ISCiii) and projects (MEDIVO2: SAF2005-03650) from Ministerio de Educación y Ciencia; and (PHENOIMA: SAF2008-03323) from Ministerio de Ciencia e Innovación in Spain. Partial funding also contributed by the European Commission: eTUMOUR (contract no. FP6-2002-LIFESCIHEALTH 503094), and HealthAgents (contract no.: IST2004-27214).
The authors also thank former INTERPRET partners: Carles Majós (IDI-Bellvitge), Àngel Moreno-Torres (CDP-IAT) John Griffiths and Franklyn Howe (SGUL), Arend Heerschap (RU), Witold Gajewicz (MUL) and Jorge Calvar (FLENI) for access to data.
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