Automatic detection of acute lymphoblastic leukaemia based on extending the multifractal features

IET Image ProcessingVolume 14, Issue 1 p. 132-137 Research ArticleFree Access Automatic detection of acute lymphoblastic leukaemia based on extending the multifractal features Correction(s) for this article Corrigendum: Automatic detection of acute lymphoblastic leukaemia based on extending the multifractal features Volume 14Issue 5IET Image Processing pages: 995-995 First Published online: March 18, 2020 Mohamadreza Abbasi, Mohamadreza Abbasi Isfahan University of Medical Sciences, P.O. Box 319, Hezar-Jerib Ave., Isfahan, 81746 73461 IranSearch for more papers by this authorSaeed Kermani, Corresponding Author Saeed Kermani kermani@med.mui.ac.ir Faculty of advanced Technologies, Isfahan University of Medical Sciences, P.O. Box 319, Hezar-Jerib Ave., Isfahan, 81746 73461 IranSearch for more papers by this authorArdeshir Tajebib, Ardeshir Tajebib School of Medicine, Isfahan University of Medical Sciences, P.O. Box 319, Hezar-Jerib Ave., Isfahan, 81746 73461 IranSearch for more papers by this authorMorteza Moradi Amin, Morteza Moradi Amin Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, IranSearch for more papers by this authorManije Abbasi, Manije Abbasi Islamic Azad University, North Tehran Branch, Vafadar Blvd., Shahid Sadoughi St., Hakimiyeh Exit, Shahid Babaee Highway, Tehran, 1651153311 IranSearch for more papers by this author Mohamadreza Abbasi, Mohamadreza Abbasi Isfahan University of Medical Sciences, P.O. Box 319, Hezar-Jerib Ave., Isfahan, 81746 73461 IranSearch for more papers by this authorSaeed Kermani, Corresponding Author Saeed Kermani kermani@med.mui.ac.ir Faculty of advanced Technologies, Isfahan University of Medical Sciences, P.O. Box 319, Hezar-Jerib Ave., Isfahan, 81746 73461 IranSearch for more papers by this authorArdeshir Tajebib, Ardeshir Tajebib School of Medicine, Isfahan University of Medical Sciences, P.O. Box 319, Hezar-Jerib Ave., Isfahan, 81746 73461 IranSearch for more papers by this authorMorteza Moradi Amin, Morteza Moradi Amin Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, IranSearch for more papers by this authorManije Abbasi, Manije Abbasi Islamic Azad University, North Tehran Branch, Vafadar Blvd., Shahid Sadoughi St., Hakimiyeh Exit, Shahid Babaee Highway, Tehran, 1651153311 IranSearch for more papers by this author First published: 01 January 2020 https://doi.org/10.1049/iet-ipr.2018.5910Citations: 1AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract The main purpose of this study is to introduce a new species of features to improve the diagnosis efficiency of acute lymphoblastic leukaemia from microscopic images. First, the authors segmented nuclei by the k-means and watershed algorithms. They extracted three sets of geometrical, statistical, and chaotic features from nuclei images. Six chaotic features were extracted by calculating the fractal dimension from five sub-images driven from the nuclei images, with their grey levels being modified. The authors classified the images into binary and multiclass types via the support vector machine algorithm. They conducted principal component analysis for dimensional reduction of feature space and then evaluated the proposed algorithm for the overfitting problem. The obtained overall results represent 99% accuracy, 99% specificity, and 97% sensitivity values in the classification of six-cell groups. The difference between the train and test errors was <3%, which proves that the classification performance had improved by using the multifractal features. 1 Introduction Approximately 0.1% of humans are diagnosed with acute lymphoblastic leukaemia (ALL). The number of new cases of ALL and deaths are reported to be 1.7 per 100,000 and 0.4 per 100,000 per year during a period of 2012–2014, respectively [1]. In the 1970s, a group of French, American, and British (FAB) leukaemia experts divided ALL into three subtypes (L1, L2, and L3) by observing the leukaemia cells under the microscope after routine staining [2]. The differences in nucleus size, chromatin tissue, nucleoli, and vacuoles are the details that help the physician to provide a differential diagnosis of morphological types of ALL, like L1, L2, and L3, and reactive types including atopic, normal, and reactive lymphocytes [3]. Although various computerised methods have been proposed so far to diagnose this kind of leukaemia, other types have not been provided to accurately identify its subgroups. 2 Related research Mishra et al. [4] used the grey-level co-occurrence matrix and random forest (RF) for classification of ALL. Due to the circular shape of the nucleus, some calculated features are irrelevant and generate a feature matrix with very large dimensions, making it computationally expensive. The accuracy of RF classifier (i.e. 99.004%) is higher compared to other classifiers (i.e. 92%) with the same data [5]. Mohapatra et al. [6] used 44 shape, texture, and colour features of ALL images and classified them using the Ensemble of Classifiers (EOC). They showed that the sensitivity and accuracy of support vector machine (SVM) are lower but near to those of EOC classifier while the specificity value of SVM is higher than that of EOC. Putz and Di Ruberto [5], Putz et al. [7] used three groups of features (shape, colour, and texture) for the classification. They employed various kernels of SVM [7] and feature selection based on several classifiers such as SVM, neural network (NN), Tree, RF, and Relief algorithm [5]. Results showed that SVM with Gaussian kernel and feature selection based on classifier specification provide better results compared to the RF classifier. Mishra et al. [8] extracted higher order discrete cosine transform coefficient that has more information from normal and abnormal cells. They used a large number of features and SVM, NN, back-propagation NNs, k-nearest neighbors algorithm (KNN) classifiers for the classification. They showed that SVM had a higher specificity and accuracy in comparison with the other classifiers. Aimi et al. [9] extracted 42 features including shape, colour, and size and classified the cells using the multilayer perceptron (MLP) and simplified fuzzy artmap. The results show the good classification performance of the proposed features. Escalante et al. [10] classified ALL and acute myeloid leukaemia cells by extracting three feature set (i.e. morphological, statistical, and texture) and used the ensemble particle swarm model classifier. They showed that the accuracy of classification with a combined nucleus and cytoplasm features are better compared with the features of the cell. The method proposed in [10], compared to the one in [6], has better results and provides an alternative to expensive diagnostic methods. Neoh et al. [11] presented a new method for the segmentation so-called stimulating discriminant measures and extracted 80 features including colour, texture, and geometry of the separated nucleus and cytoplasm and evaluated the results using three MLP, SVM, and Dempster–Shafer classifiers. They achieved an accuracy of 96.72% for the Dempster–Shafer classifier. Rawat et al. [12] extracted 142 features including colour, texture, and geometry, followed by designing a hierarchical classifier in several steps with KNN, probabilistic NN, SVM, and artificial NN fuzzy inference system classifiers. The large computation load, use of many features, and huge mistakes in reporting the results are the negative aspects of this research. In our previous work, Moradi Amim et al. [13], we classified ALL cells in four groups of L1, L2, L3, and non-cancerous using a set of texture and geometrical features by different kernels of the SVM algorithm. Despite a large number of calculations caused by using the ensemble of SVM classifier and a large number of features, we received satisfactory results. Due to high variations in cell tissue, it seems that reaching optimum results in binary classification and careful discrimination of ALL subtypes need to compliment features and powerful classifier with various options. Individual mistakes, exhaustion, and a shortage of experts in pathological laboratories are the common problems in this regard, which forced us to propose an automatic system to a leukaemia diagnosis. The remainder of this paper is organised as follows: in the methods section, the segmentation and features extraction and fractal feature extension are described. Afterwards, we classified cells after dimensional reduction and calculate the mean square error (MSE) of training and test data. Also, we evaluated fractal features in comparison with other used features with t-test and ROC diagram and computational complexity of our approach and that's effect to obtained results was also discussed. Next, results are summarised and compared with those of previous studies. In the last section, conclusion and ideas for future research are expressed. 3 Method Our data set includes 600 microscopic images from 40 bone marrow and blood smear samples. The slides (Fig. 1a1) were acquired at pathology laboratories of Al-Zahra and Omid hospitals of Isfahan, Iran. Moreover, 1598 sub-images were taken from these samples. Due to the noise and artifact existing in the imaging process (e.g. changes in the light, vibration of the microscope, the available dust on the slider, and the quality of the coloured slider), images were converted from the RGB to the hue, saturation, value (HSV) colour space and histogram equalisation on V band in this space were applied (Fig. 1a2). In the following, using the k-means clustering algorithm, each image was converted into four clusters: nucleus, cytoplasm, background, and other components. We removed the additional components resulting from the clustering in the previous step by the morphological operations like imerode and immdilate. The Watershed Algorithm was employed for separating the nucleus stuck together, followed by saving the nucleus mask individually (Figs. 1a3 and a4) [14]. Fig. 1Open in figure viewerPowerPoint Automatic separation steps (a1) Input image, (a2) Image with histogram equalisation on V band in HSV colour space, (a3) Separating nucleus with the Watershed Algorithm, (a4) Nucleus mask Fig. 2Open in figure viewerPowerPoint Lymphocyte and lymphoblast cells (a1) L1, (a2) L2, (a3) L3, (a4) Normal, (a5) Reactive, (a6) Atypical b 3.1 Feature extraction Using the relevant features plays a significant role in the efficiency of the classification stage and detection results. Due to different characteristics of these six groups of cells (i.e. ALL cells), the simple and meaningful features are required to help the physician to identify them. Three sets of features were extracted in this step from the segmented nucleus; i.e. the geometrical features including area, eccentricity, solidity, extent, perimeter, convex area, equivalent diameter, minor axis length, minor axis length, minor axis length, orientation; statistical features: colour spaces including RGB, HSV, and Lab [15]; and statistical features including mean, standard deviation, energy, entropy, skewness, kurtosis, and chaotic features Fig. 2. 3.2 Chaotic features In fractal geometry, dimension (the least coordinates necessary to describe a point in the space), as a feature representing the roughness in the image texture, is not an integer [16]. In this paper, we used a new method consisting of the combination of the differential box-counting (DBC) [17] and the fractional Brownian motion model [18] for estimation of fractal dimension (FD). First, we filter an M × N image with a mask (w) of size m × n in a sliding manner, which resulted in the Nth dimensional matrix Nd, representing the number of boxes needed to cover the entire surface of the image. (1)where r = 2.3, …, j and gmax and gmin are the maximum and minimum intensity of the pixels in the mask. b in (2) specify centroid of the mask. Theoretically, r represents how much a specific structure of pixels are self-similar to its surrounding. if we extend the range of r much further that would certainly change the accuracy of the calculated FD value. Similarly choosing a smaller range would result in insufficient surrounding pixels to correctly estimate the FD value. Finally, the slope of the linear regression line of Nd (x, y, d) and r would represent the FD of that pixel. (2)where Nd is a multidimensional matrix, with each dimension equal to the initial filtered image with a mask with size r × r, where r = 2, 3, …, 7. After log operation on the Nd, the slope of the regression line is computed between six filtered images as described in [19] to produce a fractal image. Finally, by calculating the average of all pixel value of the fractal image, the FD is then obtained. The lacunarity index is another feature that is completed FD. This feature is extracted from a fractal mass and indicates its homogeneity. In fact, the FD shows the amount of data occupancy in a given space while the lacunarity represents how the data fill the space [18-20]. This feature is expressed by the following equation: (3)Which is equal to the value of the variance to the mean of the fractal mass. M and N are the sizes of the image. 3.3 Extending the fractal features The general description of the image texture with an FD is not a convincing criterion. Different combinations and orientation in various texture may lead to the same FD. Hence, we extend the fractal features by modifying the initial images and calculating their FD and lacunarity index. These features include the FD and the lacunarity index of the initial image, image with high-grey value and low-grey value, the smoothed image in the horizontal and vertical directions, and the FD and the lacunarity index based on the singularity exponent [21, 22].‏ 3.3.1 Features 2 and 3 To compute the FD described in the previous section for features 2 and 3, we used (4) and (5), respectively. (4) (5)where L1 = gmin + (av/2) and L2 = gmax − (av/2). In these relations, gmin represents the minimum grey level, gmax is the maximum grey level, and av is the mean grey level in the image I1. 3.3.2 Features 4 and 5 Whatever image texture is smoother; it has a smaller FD. For a highly orientated texture, the effect of FD calculation is the least if the texture is smoothed along the dominant orientation [23]. If the image is smoothed along the vertical axis, the FD value is significantly reduced. On the other hand, calculating the FD in texture with a low degree of anisotropy does not much affect the image smoothing direction [24]. Therefore, we will smooth the original image in the horizontal and vertical direction as follows: (6) (7) 3.3.3 Feature 6 Most textures do not have an ideal self-similar distribution like the real textures. The multi-fractal concept provides a better description than the fractal concept. On the other hand, the gre- level image gives more information than the binary image, which is used in the fractal analysis. The final feature is defined based on the singularity exponent as follows: (8)where is the distribution in sphere of radius r around the point x. Also, the partition function x (q, r) is defined as follows (9)This function finds all possible singularities of the image by studying all q moment. The function is defined as the small r behaviour of X(q, r). (10)The sixth feature is defined as (11) 3.4 Dimensional reduction Principle component analysis (PCA), as the oldest and most well-known method of the dimensional reduction, was originally developed by Hoteling Transform. This method, by solving the eigenvalue-eigenvector problem in the covariance matrix, transforms the feature space into a smaller dimensional space based on the maximum variance of the initial feature. In this research, a special type of implementation of this algorithm called Niplas PCA was used [25]. The steps of this algorithm are presented in Fig. 3. Fig. 3Open in figure viewerPowerPoint Niplas PCA algorithm pseudo code 4 Overfitting In classification applications, overfitting happens when a model learns the detail in the training data it negatively impacts the performance of the model on new data. [26, 27]. In order to detect this error, the data set is divided into two parts of the train and test and the MSEs are calculated based on the classifier properties. If the error difference between the train and test data is slight, no error would occur. Equations (12) and (13) present the relations used for calculating the train and test errors. (12) (13)Relation (12) shows test error, which is the prediction error over an independent test sample, and relation (13) indicates training error, which is the average loss of the training sample. In this relations, Y is the target variable, is the prediction model, T is the training set, and L is the loss function [28]. 5 Discussion After segmenting nucleus from microscopic images, we extracted three groups of the feature (i.e. geometrical, statistical, and chaotic) from the sub-images. We classified cells using the SVM classifier in the binary (cancerous and non-cancerous) and multiclass (L1, L2, L3, At, N, Re) [13, 14]. The classification was done using the Gaussian kernel and the Penalty Parameter (Box Constraint) 100. Classification evaluation was performed by five criteria including sensitivity, specificity, accuracy, false negative rate, and positive predictive value with the following relationships: (14) (15) (16) (17) (18)TP is the number of correct cells belonging to the target group, TN is the number of correct cells belonging to other groups, FP is the number of wrong cells belonging to the target group, FNis the number of wrong cells belonging to other groups. The sensitivity, specificity, accuracy, FNR, and precision values in the binary classification were calculated to be 99.67, 99. 67, 99.73, 1.17, and 99.69, respectively. From 1107 lymphoblast cells two cases and from the 491 lymphocyte cells one case was falsely detected. Compared to [4], we obtain better results with a larger data set. The error rate between the training and test data is 0.73%, which proves the comprehensiveness of our classification model. The computational complexity, which is due to the use of the Ensemble classifier, is the disadvantage of other methods, which make our work superior [6]. The main innovation in this study is to demonstrate the superiority of fractal features by expanding them in the detection of pathological textures with a similar resemblance to statistical and geometrical features. fractal features were introduced in most of the previous studies as complementary features with other features in applications such as image classification. In order to increase the effect of fractal features, we extended the fractal features and the lacunarity estimated from the local method. Using the local method for estimating the FD is due to the high similarity of the texture of six cell groups in this study. The extension of the FD features that the Sarkar used in his research to image segmentation was used to enhance the FD's impact on classification results. Briefly, the introduction of features that changes robustly such as changing the colour space that best describes the texture of an image are the main goals of this research. In order to evaluate features, we use the t-test and their effect on the classification results used the ROC diagram. t-test returns a test decision for the null hypothesis that the data from independent random samples from normal distributions with equal means and equal but unknown variances. We divide the data into two target cell and the remaining cells and apply the t-test to them, and we do this for the six-cell groups. As shown in Table 1, f1–f5 are lacunarity, entropy on h band, Skewness on s band, energy on v band in HSV colour space and the entropy on a band on Lab colour space with the p-value <0.05 in t-test were achieved. The computed lacunarity of the second fractal feature has the least p-value in five from six test decision of the six groups of cells. Another interesting point is about the entropy feature in two different bands in the HSV colour space, which is of a chaotic nature and can distinguish between the six cell groups. Table 1. t-test result in six cell groups L1 and others L2 and others L3 and others At and others Re and others N and others f1 1.03 × 10−17 2.22 × 10−9 0.0002 4.09 × 10−12 3.98 × 10−22 0.0003 f2 0.0360 4.69 × 10−7 0.0344 1.77 × 10−6 2.61 × 10−7 0.0552 f3 0.0291 0.0001 1.74 × 10−9 0.0286 3.67 × 10−10 0.0034 f4 6.70 × 10−5 0.0003 0.0220 0.0354 2.66 × 10−6 0.7452 f5 1.34 × 10−13 0.0345 0.0045 0.0075 0.0002 0.0012 In the next step, the classification results for the diagnosis of acute lymphocytic leukaemia cells by two groups of fractal and geometrical and statistical features are analysed using the ROC diagram. First, we divided the existing features with the lowest p-values obtained from t-test in the previous section into two categories of fractal feature and geometrical and statistical features, and train SVM classifier through these two groups. At the end compute the standard ROC curve using the scores from the SVM model. As you see in Fig. 4, although fractal feature produces better ROC values about the 0.9867 in comparison to geometrical and statistical features 0.9520 but in a multi-class classification, the classification accuracy for each class is more important than the overall classification accuracy, so in order to achieve maximum accuracy, we use the combination of all the features mentioned in the final classification. Fig. 4Open in figure viewerPowerPoint ROC diagram for evaluating classifier output of fractal in comparison with the geometrical and statistical features The time complexity of the FD DBC algorithm is O(N2), so it is considered a fast algorithm. This value obtained fromtwo loop in relation (1) and fractal feature results of applying this relation to modified images it has the same value for all six fractal features. According to the ROC diagram, the classification accuracy obtained from fractal features and time complexity, the superiority of fractal features, is proven in all respects. Table 2 present the results of the multiclass classification (L1, L2, L3, At, N, Re) while Table 3 shows its confusion matrix. In a study conducted by Rawat et al. [12], the results of hierarchical classifier compared to our research showed a lower accuracy and specificity for L2 and L3 groups. Moreover, the computational complexity and mistakes in reporting the results are the disadvantages of their research. In our previous work [13], we employed the Ensemble classifier with a different kernel of SVM classifier and found that the Gaussian kernel provides better results compared to the other classifiers. The fractal features provide an appropriate supplement to enhance classification efficiency. As shown in Tables 2 and 3, sensitivity, specificity and accuracy values in five groups of six cell groups have increased significantly. This increase in these parameters can be justified by increasing the number of data due to the addition of fractal features and the use of a modified classifier. The error rate between the training and test data is 2.71% (likewise the binary classification), suggesting that the algorithm is far from the overfitting error. The results of using the NIPLAS PCA algorithm showed that classification by 10 and 40 features leads to the efficacy levels of 80 and 99%, respectively, in a four-class classification that makes our algorithm more optimum. Table 2. Multiclass classification L1 L2 L3 At N Re sensitivity 97.90 94.90 96.40 96.50 98.80 98.20 specificity 99.73 99.41 99.38 99.76 99.75 99.93 accuracy 99.54 98.94 99.07 99.42 99.65 99.74 FNR 2.10 5.10 3.60 3.50 1.20 1.80 precision 97.70 94.99 94.79 97.96 97.92 99.39 Table 3. Confusion matrix of the multiclass classification Output of classifier L1 L2 L3 At N Re L1 481 1 0 1 1 0 L2 1 467 0 0 3 1 L3 1 2 147 1 0 0 At 0 1 0 211 2 0 N 0 1 0 0 140 0 Re 1 1 1 0 0 133 6 Conclusion In this research, a fully automated diagnostic system of ALL was developed based on k-means, watershed algorithms, and morphological operations. In the feature extraction step, three sets of statistical, geometrical, and chaotic features were extracted from the nuclei. Despite several other similar studies in this field, it is a high necessity to introduced new features while further investigating the data and improving the obtained results. The main objective of this study was to extend the chaotic features by introducing them as the features least affected by the segmentation stage, which would be appropriate to classify other types of leukaemia. The algorithm used for segmentation consists of simple parts and provides high accuracy. These simple features were selected according to expert opinion. The feature dimensions were reduced by the recursive algorithm that is based on PCA and has a better performance than its previous versions. The satisfactory results obtained from this study is proportional to the high number of data used. The main advantage of the present study is its high sensitivity, specificity, accuracy, precision, and FNR (all above 97%), which distinguish it from other works. 7 Acknowledgments This research was financially supported by the Isfahan University of Medical Sciences, Isfahan, Iran (Grant no. 3941050). 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