Meningioma Subtype Classification: A Survey Kiran Fatima, Hammad Majeed Department of Computer Science

National University of Computer & Emerging Sciences Islamabad, Pakistan

kiranfatima_54@hotmail.com, hammad.majeed@nu.edu.pk

Abstract—Meningioma subtype classification is a complex, real world and multi-class problem from the domain of histopathology. To solve this, various Computer Aided Techniques are employed in the past with a varying degree of success. This classification not only helps the technicians /doctors/pathologists to classify each sample correctly but also assists them in making the right decision for treatment.

The main challenge in solving this problem is the inherent complexity due to high intra-class variability and low inter-class variation in the texture of tumor samples. Many attempts have been made and numerous algorithms are introduced till this day. Unfortunately, all of them have shown inconsistent performance. By inconsistent we mean that an algorithm is classifying only a subset of the subtypes correctly, on the rest it’s not much accurate.

In this survey we have mentioned a few top of the line techniques introduced recently and analyzed them critically. At the end, on the basis of this survey, we have proposed the best suited environment for the use of each technique and a possible promising future direction.

Keywords-Texture analysis; feature extraction; multiresolution representation; wavelet packet; histopathological images;meningioma

I. INTRODUCTION

Histopathological diagnosis of tumors especially of the central nervous system requires human expertise for correct decision making. An expert pathologist is required for the examination of histological slides and this process is time consuming. Inter–observer variability is also noticed in the reported opinion. Due to these reasons quantitative techniques for computer assisted diagnosis are still needed to help histopathologists for tumor diagnosis. Accurate analysis can be done in most cases of meningioma by neuropathologists.Therefore this type of tumor is appropriate for testing diagnostic abilities of a computer aided decision system.

Meningiomas are the most common extra-axial tumors in the brain and spinal cord. They are solid lesions having well-defined boundaries, originate from the meninges (the protective membranes around the brain and spinal cord). These tumors account for approximately 20% of adult and 30.1% [1] of all primary brain tumors. They occur predominantly in middle-aged patients with a female preponderance [2]. The World Health Organization (WHO) classification categorized meningioma on the basis of morphological characteristics into three grades as given in table I [3].

TABLE I. DIFFERENT GRADES AND SUBTYPES OF MENINGIOMA.

Grade Subtypes

Grade I: Benign - (90%) Meningiothelial, Fibroblastic, Transitional,

Psammomatous, Angioblastic, Microcystic, Secretory, Metaplastic, Lymphoplasmacyte

Grade II: Atypical - (8%) Chordoid, Clear cell, Atypical

Grade III: Malignant - (2%) Papillary, Rhabdoid, Anaplastic

Meningiomas are graded from low to high. The risk of recurrence and aggressive growth is less for low grade tumors. Most meningiomas are considered benign or low grade tumors. However, unlike benign tumors elsewhere in the body, some of these brain tumors can cause disability and may sometimes be life threatening [4]. Atypical and anaplastic malignant meningiomas exhibit an increased risk of recurrence and premature death compared with benign tumors [5]. Under the microscope, there are a variety of patterns in meningioma subtypes as shown in Fig. 1 and described in table II [6].

Figure 1. Four subtypes of Grade-I benign meningioma. a. meningiothelial, b. fibroblastic, c. psammomatous, d. transitional [10].

Very limited research is observed in the literature on meningioma classification with considerable accuracy. The basic aim of all techniques is to improve the classification accuracy by extracting such texture features that make best analysis of characteristics of the examined specimen. Asupervised learning technique is used for classification of two meningioma subtypes using cell shape analysis and decision trees [15]. Wavelet packet transform and neural networks (unsupervised self organizing map) are used for the classification of meningioma texture features [10]. Some

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2010 6th International Conference on Emerging Technologies (ICET)

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compared the performance of features derived from wavelet packet transform to local binary patterns [11] and to co-occurrences methods [9]. Others embed the information from RGB color channels with the optimal combination of texture features to improve the classification accuracy significantly [13]. TABLE II. MAIN MORPHOLOGICAL FEATURES OF THE FOUR COMMON GRADE-I

MENINGIOMA SUBTYPES.

Subtype Characteristics

Meningiothelial Indistinct cytoplasmic boundaries, nuclear clearing, cellular whorls, Small clusters of cells with delicate round or oval nuclei

Fibroblastic Spindle shaped (fibrous) cells with rich collagen

Transitional Composed of synctial and fibroblastic cells with few psammoma bodies and abundant compact whorls

Psammomatous Variant of transitional type having numerous cystic spaces and psammoma bodies

All techniques discussed above yield discriminant features to perform classification of a given WHO Grade I benign meningioma image sample into one of the four subtypes. Although atypical and anaplastic meningiomas account for only 10% of all of meningiomas, it is also required to distinguish them from benign meningiomas on the basis of the grade of malignancy because it is also quite difficult for histopathologists to distinguish them by morphologic criteria alone.

In this survey, we investigate some of the above mentioned meningioma classification techniques. We also observe the limitations in these techniques and suggest some future directions. The subsequent sections of this survey are organized as follows. Section II highlights some challenges in meningioma subtype classification. Section III describes the process of image acquisition. Section IV introduces the basics of texture classification and reviews the techniques studied in this survey. Discussion on these techniques is given in Section V. Future directions are proposed in Section VII.

II. CHALLENGES

Classification of histopathological images of meningiomas is a real world complex pattern classification and detection problem. Meningioma textures are much different from synthetic textures which are acquired in controlled environment [16]. This problem exhibits inherent complexity due to high intra-class variability and low inter-class variation in the texture of tumor samples. Classification of meningioma subtypes is therefore a challenging task due to the issue of variation and non-homogeneity of texture samples.

Most subtypes of meningioma contain meningiothelial whorls, collagen matrix, psammoma bodies, and syncytial cells [6]. Due to such similar texture features, these subtypes can be misclassified, e.g. the meningiothelial image samples can be misclassified as fibroblastic and transitional and the transitional and fibroblastic can be misclassified as

mengiothelial [8]. Meningiothelial is the most common subtype of meningioma but quite complicated to classify.Transitional meningioma is also known as hybrid meningioma as it has characteristics of fibroblastic and meningiothelial types. Psammomatous is the most developed form and relatively easier to classify because it has abundant psammoma bodies of different sizes [8].

III. DATA ACQUISITION AND SETUP

Diagnostic tumor samples used in the meningioma classification techniques are obtained at the Bethel Department of Neurosurgery, Bielefeld, Germany from neurosurgical resections for therapeutic purposes. The routinely stained histological slides are analyzed on a Zeiss Axioskop2 plus microscope with a Zeiss Achroplan 40x/0.65 lens to obtain 1300x1030 pixels, 24 bit, true color RGB pictures [16]. The original images are truncated to 1024x1024 pixels and then each image is subdivided into images of different sizes for further analysis.

IV. TEXTURE CLASSIFICATION TECHNIQUES

The pattern classification problem is generally composed of two main stages. The first is the texture feature extraction and the second is the classification of patterns on the basis of extracted features. Performance of a classifier mainly depends upon the optimality of the selected feature set.

Optimal feature set can be obtained using segmentation and texture analysis approaches. Thresholding, region merging, edge detection, and template matching are the main techniques used for segmenting the structures of interest in the histology image samples [16].

Texture is a proficient measure to approximate the differences of structure, orientation, regularity, roughness, and smoothness of distinct sections of an image [7]. Textures can be defined as a repetition of patterns over an image area. Texture analysis approaches can be categorized into three major types; statistical, structural, and spectral. Statistical techniques make use of statistical characteristics captured from image specimen. These features can be extracted from grey level histograms (skewness and kurtosis) and grey level co-occurrence matrix (inertia, entropy, and energy). Structural approaches make analysis of texture geometry (size, shape, distance, and perimeter) and arrangement of structures over an image region. Spectral approaches make analysis of image textures in transformed domains (Fourier [14], Discrete cosine transform, Gabor, and Wavelet domain) [16].

Classification of texture features can be performed by supervised and unsupervised learning techniques. Artificial neural networks (self organizing maps, learning vector quantization, multi-layer perceptron etc.), support vector machines (SVM), k-nearest neighbor (KNN) and Bayesian classifiers etc. can be employed for the classification task [14].

In this section, we present a review of some texture-based meningioma subtype classification techniques recentlyintroduced in the literature.

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A. Discriminant Wavelet Packets and Learning Vector Quantization (DWP & LVQ)This is a texture-based novel technique for the

classification of certain subtypes of meningioma histology images [8]. This technique divides the task of pattern classification and detection into three major steps of (i) pattern feature extraction, (ii) training of neural network, and (iii) recognition of test samples as shown in Fig. 2.

In the first step for the extraction of inherent features, the full wavelet packet transform of each input image is computed by decomposing it into its respective low and high subbands at four levels by using the Daubechies 4 tap filter. In this technique, the most discriminant wavelet packet subbands are selected by calculating the discrimination power of each subband and Hellinger distance is used between different classes for the purpose [8]. Statistical parameters (mean, kurtosis, energy, and entropy) of the first few most discriminant wavelet packet subbands are used as features for the classification task. In the second step of the training of neural network, the supervised network learning vector quantization (LVQ) is trained to discriminate between diverse meningioma image textures. Extracted statistical features are used as input vectors for the training of four LVQs. Four feature vectors of different lengths are employed on the basis of the efficiency of the results. A weight vector Wi as a reference for each class is selected in the training phase. These weight vectors are adjusted at each training iteration. Output units are adapted through supervised learning during training phase. In the final stage of this technique, the meningioma test samples are recognized through a competitive process. For each test sample, closest class is computed according to the reference vector on the basis of Euclidean distance by each LVQ. The final class label is selected by the majority voting.

Training

DiscriminantWavelet PacketTransform

FeatureExtraction

NeuralNetwrok

Classification

Testing

DiscriminantWavelet PacketTransform

FeatureExtraction

NeuralNetwrok

Classification

ImageData

Final WeightVector

ImageData

Figure 2. Flowchart of the Discriminant Wavelet Packets and Learning Vector Quantization [8].

The histology image dataset used for experimentation consists of 84 images of four subtypes in total. 5 samples of each subtype for training of LVQ net are selected and remaining samples are used for testing purpose.

This technique achieved promising results for the classification of two important subtypes, meningiothelial and psammomatous. LVQ trained on the kurtosis feature vector of the most discriminant subbands classify the subtype meningiothelial with 95% accuracy.This technique presents great limitation in discriminating between fibroblastic and transitional type of meningiomas. This scheme becomes able to achieve only 50% classification accuracy for both subtypes.

B. Adaptive Discriminant Wavelet Packet Transform and Local Binary Patterns (ADWPT & LBP) This is a combined image analysis approach for

meningioma subtype classification which employs wavelet packet transform based ADWPT and local binary pattern (LBP) features [11]. This paper also presents the comparative analysis of subband texture (macro) and micro-texture features and their combined effect on the classification performance employing several statistical classifiers and dimensionality reduction techniques. The framework of this scheme is shown in Fig. 3.

Full wavelet packet transform of input image is computed by using Daubechies 8-tab filter. For the calculation of most discriminant basis, Pseudo probability function for each subband is obtained using the normalized energy of the subband coefficients. The pairwise discriminating power of the subbands is calculated using the Hellinger distance [11]. For the selection of best basis, the wavelet decomposition tree is pruned in a bottom-up fashion by making comparison of the discrimination power of the subbands. Extraction and ordering of subbands from the best basis based upon their discrimination power is performed to obtain 64 most discriminant subbands. Grey level co-occurrence matrix (GLCM) features (contrast, correlation, energy, and homogeneity) from the most discriminant subbands are extracted. Another set of micro features is obtained by extracting LBPs by encoding the image in a single 3x3 neighborhood per pixel. To cope with bias variance dilemma, linear (Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA)) and non linear (Diffusion Maps) dimensionality reduction techniques are employed. Various statistical classifiers such as Bayesian classifier, k-nearest neighbor (KNN) classifier, and support vector machines (SVM) with Gaussian kernel are utilized for the task of pattern classification.

In this technique 4 or 5-fold cross validation has been performed by leaving out one patient image data per subtype for testing and remaining 4 patients data for training. The

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histology image data set consists of 320 images in total of four subtypes. Each subtype has equal number of images of five different patients.

SVM classifier using a Gaussian kernel presents best classification results when trained with ADWPT feature set having 64 most discriminant features. It achieves overall classification accuracy of 82.10 %. Combined feature set of ADWPT and LBP features (9 features) gives 94% classification accuracy for subtype meningiothelial with SVM classifier and Diffusion Maps. Dimensionality reduction using PCA followed by SVM classifier gives good classification accuracy for ADWPT and combined feature set. For LBP feature set, Bayesian classifier performs slightly better than SVM classifier. Table III shows that this technique achieves only 60.90% accuracy for meningiothelial and 76.60% for transitional meningioma with SVM for ADWPT features.

ImageData

Full WaveletPacket Transform

Feature Extraction(Micro features)

Subbands Extraction& Ordering

DimensionalityReduction

(PCA, LDA, DM)

Feature Extraction(Macro Features)

Classification(Bayesian, SVM,

KNN)

DimensionalityReduction

(PCA, LDA, DM)

Classification(Bayesian, SVM,

KNN)

Figure 3. Process followed in Adaptive Discriminant Wavelet Packet Transform and Local Binary Patterns [11].

C. Robust Adaptive Wavelet-Based Method (RADWPT)

A multi-resolution analysis technique to resolve the issue of intra-class texture variation on the basis of stability of adaptive discriminant wavelet packet transform (ADWPT) is introduced in [12]. The task of classification is performed into following major steps as shown in Fig. 4.

For each image, the full wavelet packet transform up to four levels is computed. Multi-resolution wavelet based texture templates (MWTT) are obtained for each sample class by computing pseudo-averaging of the pseudo-probability density estimates (ppde) [12] of a specific subband for all the training samples of a particular subtype. For the calculation of discrimination power, Fisher [12] distance is employed. The overall discrimination power of a particular subband across all texture classes is utilized for the selection of best basis. For the selection of most stable basis, the 256 decompositions obtained in each test trial are combined to produce a union decomposition. Fisher distance yields 16

diverse decompositions. The mechanism of union of basis can be expressed as in (1),

Where is the total number of decompositions obtained

in a given trial and is the ith decomposition. The stability of a subband in a test trial run is calculated as in (2),

Sd,p,q = Nd,p,q/NT (2)Where Nd,p,q is the total count, the subband Wd,p,q is found in the decompositions obtained for the several trial runs utilizing different combinations of patient’s data. NT is equal to the total number of trial runs (256). Small values of stability degrade the classification accuracy. A high value of stability of a subband means it is most

discriminant, robust, and stable. , the set of most stable subbands is obtained as,

= {Wd,p,qq | Sd,p,q= 1} (3)Gray Level Co-occurrence Matrix (GLCM) based features are used to extract two feature sets, one from the set of

union basis and the other from the set of most stable

basis . SVM classifier is employed for the training and recognition purpose.

Full WaveletPacket Transform

Texture Templateconstruction

Best BasisSelection

UnionDecompositionProduction

Calculation ofMost Stable Basis

Feature Extraction(GLCM)

Classification(SVM)

Meningioma DataSet

Classification(SVM)

Feature Extraction(GLCM)

Figure 4. Flow chart of the Robust Adaptive Wavelet-Based Method [12].

The image data set consists of 960 image samples. There are five unique cases per subtype and four subtypes in total. There are four original images of each patient. Each individual image is partitioned into 12 sub-images. In this scheme, 5-fold cross validation is employed by utilizing data of four patients per subtype for training and one patient data for testing.

This technique found the feature set of most stable basis optimal for texture recognition because it offers significant improvement in the classification accuracies. This scheme achieved substantial reduction of 50% in feature space and 6% overall improvement in classification accuracy. As mentioned in table III, in comparison with technique (ADWPT & LBP), this technique obtained 28% improvement in meningiothelial and 8% in transitional meningiomas.

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This technique differentiate fibroblastic and psammomatous meningiomas with less accuracy as compared to the scheme proposed in [11]. The total number of subbands used for feature extraction is also substantially increased.

D. Texture Measures Combination for Improved Meningioma Classification (TMC)

An optimum texture measure combination scheme is proposed in [13] to provide assistance to histopathologists for the correct classification of meningioma benign tumors. The task of classification is divided into two main stages of (i) optimal color component selection and morphological processing, and (ii) feature extraction and classification. The general framework of this technique is shown in Fig. 5.

In the first stage, four colored images (one from each subtype) are randomly selected and decomposed into red, green, and blue color components. Segmentation for each color channel is performed on pixel-by-pixel basis toselect the channel which provides best segmentation results. The Bhattacharyya distance is used to evaluate the quality of segmentation [13]. The color channel, which best discriminates the cell nuclei from the background, is selected for morphological processing. Morphological gradient of all training and testing samples was calculated to segment the nuclei and to eliminate the possible noise. In the second stage, four diverse texture measures (two model-based and two statistical-based) were employed for the extraction of representative characteristics of the tumor subtypes. Random fields (RF) and fractals are used as model-based texture measures. Seven Gaussian Markov random field (GMRF) parameters are derived for third order Markov neighbors. First order statistical features (mean, variance, skewness, and kurtosis) are calculated from fractal dimension image of each meningioma image. Co-occurrence matrices (CMs) and run-length matrices (RLMs) are used as statistical-based texture measures. Eight second order statistical features from CMs and eleven statistical features from RLMs are estimated. Highly correlated or redundant features are discarded to obtain optimal feature set by fusing the features having highest divergence value in all possible combinations. Probabilistic naive Bayesian classifier is employed for classification.

The data set consists of 320 images of four subtypes. There are five unique cases per subtype and four images of each case. Each individual image is partitioned into four sub-images of 512x512 pixels. Holdout validation approach is employed for classification validation by selecting four patients per subtype for training and one patient for testing.

The combination of GMRF and RLM texture measures achieve significant classification accuracy of 92.50% as shown in table III. Individual texture measures provide best

classification accuracy of 83.75%. GMRF texture measure showed nearly consistent response throughout all image samples and not much affected by the noise added due to cracks (white regions) in the biopsy samples during the procedure of slides preparation. Blue color component best defines the borders of the region of interest (cell nuclei) from the background (cytoplasm) for three of the four subtypes. The estimation of segmentation separability on four randomly selected samples is needed once for the selection of optimal color component.

Meningiomadata set

Sample ImageSelection

RGBDecomposition

StructureSegmentation

Training set

Testing set

Optimal ColorChannel Selection

MorphologicalProcessing

Feature Extraction

Optimal FeatureSelection

BayesianClassifier

ClassificationResult

Figure 5. Framework of Texture Measure Combination for Meningioma Classification [13].

V. DISCUSSION

Techniques proposed in [11, 12] employed SVM classifier for classification task. SVM is basically a binary classifier but it is extendable for multi-class problems like meningioma subtype classification. SVM performs very well in higher dimensional spaces and even when training data is limited. It is computationally much less expensive especially as compared to neural networks. SVM shows good performance even when data is noisy as there is varying amounts and types of noise in histopathological images. SVM does not suffer much from the curse of dimensionality [7].Technique proposed in [11] achieved the best classification results (82.10%) with SVM (Gaussian) when no dimensionality reduction technique was used.

Performance of technique proposed in [8] may be degraded to some extent due to presence of noise in histological image data as neural netwok does not perform well with noisy data [7].

Techniques proposed in [8, 11, 12] obtained a basic template for each subtype of meningioma which may be useful in a way that subtypes are different from each other to a great extent due to their basic texture and layout. So obtaining a basic pattern or template as a representative of a class or subtype and then extracting texture features is a suitable idea. These techniques perform the multi-resolution analysis of

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histology samples by decomposing the image texture at several frequency and spatial resolutions. This can be helpful in solving the problem of texture complexity as every subband in decomposition depicts certain textural characteristics.

These three techniques [8,11,12] calculate full wavelet packet transform of each image in the data set by decomposing images into their respective low and high subbands at all levels, so these techniques may be computationally expensive.

The technique proposed in [13] used color information to segment cell nuclei from the background (cytoplasm). Dyes used for staining give cell nuclei a purple color and the cytoplasm a pink color. The dominance of one color on the other can affect segmentation performance. On the other hand, this technique uses the cell-nuclei shape to find the original subtype. It may be beneficial because it seems that the nuclei shape differs greatly among different meningioma subtypes.

All above mentioned classification schemes have performed supervised learning. These techniques have presented classification results by using 4 or 5-fold cross validation. In our opinion, these techniques seem to overfit the training data. It may be needed to test these techniques for unseen meningioma samples. TABLE III. CLASSIFICATION ACCURACIES FOR FOUR SUBTYPES OFMENINGIOMA (M=MENINGIOTHELIAL, F=FIBROBLASTIC, T=TRANSITIONAL,

P=PSAMMOMATOUS) ACHIEVED BY STUDIED CLASSIFICATIONTECHNIQUES.

Technique Classifier M (%) F (%) T (%) P (%) Avg(%)

DWP & LVQ

ANN (LVQ)

95 50 50 100 75.00

ADWPT & LBP

SVM (Gaussian)

60.90 92.20 76.60 98.50 82.10

RADWPT SVM (Gaussian)

89 80 85 97 88.00

TMC Naïve Bayesian

95 90 95 90 92.50

VI. CONCLUSIONS

Classification of histological images of meningiomas is a real world complex pattern detection and classification problem. Meningioma textures are much different from synthetic textures which are acquired in controlled environment. This problem exhibits inherent complexity due to high intra-class variability and low inter-class variation in the texture of tumor samples. Meningioma subtypes classification is therefore a challenging task due to the variation and heterogeneity issues of texture samples.

In this research study, we have explored some of the texture classification techniques for histopathological images. The basic objective of all studied techniques was to achieve significant classification accuracy for all variants of meningioma. Classification results mentioned in table III show that most of the techniques remain unable to accurately differentiate all subtypes. LVQ neural net [8] classified

meningiothelial with 95% and psammomatous with 100% accuracy but failed badly to classify other two subtypes. SVM [11] classified fibroblastic subtype with 92.20% accuracy but showed poor performance (60.90%) for meningiothelial. SVM classifier with most stable subbands [12] showed satisfactory performance (88% on average) for almost all subtypes of meningioma. Bayesian classifier [13] showed very good performance (95%) for meningiothelial and transitional. Therefore, it is needed to investigate the reason of the misclassification for certain subtypes.

VII. FUTURE DIRECTIONS

Different kinds of neural networks and other classifiers can be investigated in hybrid mode for better classification because in most cases a single classifier fails to achieve significant accuracy for all variants of meningioma. Color features may be employed for better analysis of basic structures (cytoplasm and nuclei) of meningioma image samples. Some alternate methods for wavelet packet subband selection and optimal color selection can be investigated.

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