Automated acute lymphoblastic leukaemia detection system using microscopic images

IET Image ProcessingVolume 13, Issue 13 p. 2548-2553 Research ArticleFree Access Automated acute lymphoblastic leukaemia detection system using microscopic images Komal Nain Sukhia, Corresponding Author Komal Nain Sukhia komalsukhia@students.mcs.edu.pk National University of Sciences and Technology (NUST), Islamabad, PakistanSearch for more papers by this authorAbdul Ghafoor, Abdul Ghafoor National University of Sciences and Technology (NUST), Islamabad, PakistanSearch for more papers by this authorMuhammad Mohsin Riaz, Muhammad Mohsin Riaz Center for Advanced Studies in Telecommunication (CAST), COMSATS Islamabad, Islamabad, PakistanSearch for more papers by this authorNaima Iltaf, Naima Iltaf National University of Sciences and Technology (NUST), Islamabad, PakistanSearch for more papers by this author Komal Nain Sukhia, Corresponding Author Komal Nain Sukhia komalsukhia@students.mcs.edu.pk National University of Sciences and Technology (NUST), Islamabad, PakistanSearch for more papers by this authorAbdul Ghafoor, Abdul Ghafoor National University of Sciences and Technology (NUST), Islamabad, PakistanSearch for more papers by this authorMuhammad Mohsin Riaz, Muhammad Mohsin Riaz Center for Advanced Studies in Telecommunication (CAST), COMSATS Islamabad, Islamabad, PakistanSearch for more papers by this authorNaima Iltaf, Naima Iltaf National University of Sciences and Technology (NUST), Islamabad, PakistanSearch for more papers by this author First published: 11 October 2019 https://doi.org/10.1049/iet-ipr.2018.5471Citations: 10AboutSectionsPDF 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 An automatic and novel approach for acute lymphoblastic leukaemia classification is proposed. The proposed scheme is based on pre-processing and segmentation of white blood cell nuclei using expectation maximisation algorithm, feature extraction, feature selection using principal component analysis and classification using sparse representation. The accuracy of the proposed scheme significantly outperforms the existing schemes in terms of acute lymphoblastic leukaemia classification. 1 Introduction Acute lymphoblastic leukaemia (ALL) is the most common type of blood and bone marrow cancer. The diagnosis of ALL holds great importance in the field of medicine. The microscopic and manual examination of the blood slides is biased which results in subjective and inconsistent results [1]. Therefore, an automated and robust system is required for leukaemia screening. The major aim of this paper is to deal with the stated problems regarding manual analysis by proposing an automated system for ALL classification. This research will help to consider the most distinctive characteristics of abnormal and normal white blood cells (WBCs) for ALL classification. Moreover, it will enable pathologists to automatically examine blood smear images for cancerous cell detection. In literature, various techniques have been proposed to detect ALL disease [1-19]. In [1], fuzzy-based colour segmentation method is used for the separation of WBC from background which further utilises morphological and texture features for the accurate recognition of leukaemia cells. However, classification using support vector machine (SVM) requires high computational resources. In [2], acute myelogenous leukaemia (AML) detection technique claims high accuracy by combining shape, texture and colour features, however, the accuracy has been computed for limited number of WBC samples. Amin et al. [3] proposed automatic recognition of ALL cells in microscopic blood smear images by combining nucleus segmentation, feature extraction and classification. However, limited features are extracted and the conventional k-means algorithm for segmentation often has limited performance. Raje et al. [4] present a system to accurately detect leukaemia in microscopic images using only geometrical features. Vincent et al. [5] present an approach for the classification of ALL and normal cells using grey-level co-occurrence matrix (GLCM) features and sequential neural network classifier. Although the approach performs well, however, it considers only textural features for classification purpose. In [6], ALL detection in blood smear images by employing shape and texture [i.e. Hausdorff dimension (HD) and contour signature] features has been proposed but the technique is not robust against extreme staining. Singhal et al. [7] proposed automatic classification of ALL using SVM over morphological shape and local binary pattern (LBP) features, however, the presented technique is inefficient. In [8, 9], a system is proposed to classify normal lymphocytes and lymphoblasts using nucleus and cytoplasm shape features. However, the proposed systems utilise only limited shape features for the classification purpose, therefore, accuracy is quite less. In [10], an automated technique to detect ALL and AML blast cells from blood cell images using different pre- and post-processing techniques has been presented but the technique fails to segment severe staining images accurately. Bare-bones particle swarm optimisation (BBPSO) method helps to uniquely classify the normal and blast cells, however, it has high computational complexity [12]. Shape and texture features, when combined together, extract the discriminative properties of lymphoblasts and normal cells [11], however, colour-based features must be used to further improve the detection accuracy. Whole blood smear images having multiple nuclei are utilised to classify normal and blast cells in [13, 14], however, both techniques lack efficiency. In [15], a novel stimulating discriminant measures algorithm is presented to improve the segmentation of nucleus and cytoplasm in cells, however, the overall process is time consuming. Mishra et al. [16] presented a scheme for identification and classification of lymphoblasts by using texture feature and random forest classifier. However, performance can be enhanced by the addition of colour and morphological features. Automated hybrid hierarchical classification method [17] helps to differentiate cancerous cells from the non-cancerous ones and further distinguish between the various subtypes of ALL. However, the strength and classification accuracy can be further improved by utilising other significant features. Geometric, colour and texture features, when combined with the neural network (NN) model yield better accuracy [18]. However, the overall scheme is not computationally efficient. A combination of shape-based features and fuzzy classifier [19] provides considerable results in less time. However, it fails to accurately detect subtypes of ALL in certain cases. An automatic and accurate ALL classification technique for WBC images is proposed. The proposed scheme mainly contains pre-processing, segmentation, feature extraction as well as selection and sparse classification. The proposed technique achieves superior accuracy in differentiating the ALL lymphoblast cells from normal lymphocytes as compared to the existing techniques. The paper is structured as follows. Section 2 presents the proposed technique for ALL classification. Simulation results and discussion are described in Section 3. Lastly, Section 4 concludes the paper. The flow of the proposed technique is shown in Fig. 1. Fig. 1Open in figure viewerPowerPoint Flowchart of the proposed technique 2 Proposed methodology Let be the colour band of input image with dimensions , where shows red, green and blue, and show the rows and columns, respectively. The blood cells and their background vary according to the colour and intensity to a great extent. The main factors behind this variation are illumination changes, camera settings and staining. Therefore, segmentation of cell can be made robust against such factors by conversion of image from RGB to YCbCr. The Y channel, i.e. luminance intensity plane helps to notice very minute colour difference value. The conversion of RGB to YCbCr colour space is (1) (2) (3)The image Y, i.e. luminance intensity plane is used to perform contrast stretching for enhancement of detail information, i.e. (4)where is the enhanced image. and represent the highest and lowest intensity value range from the input image, whereas and represent the desired highest and lowest intensity value range of the resulting image. returns the greyscale transformation image that maps grey levels in the image Y to grey levels in and then is again converted to RGB colour space. The resultant image comes out to be . The resultant coloured image is passed through diffused expectation maximisation (DEM) algorithm [20]. DEM models WBC coloured image as a finite Gaussian mixture, in which each Gaussian mixture component refers to a region class and parameters of each class are estimated by using a maximum likelihood approach in order to justify the spatial dependency between the pixels. DEM is used to obtain two threshold values and identifies three classes (i.e. WBCs, red blood cells and background) given as (5)where c = 3 shows the number of classes, and iterations. The threshold values and are used to obtain the WBC nucleus segmented image W, i.e. (6)where is the resultant image obtained after WBC nucleus segmentation. The results of nucleus segmentation are shown in Fig. 2. Fig. 2Open in figure viewerPowerPoint Input image and segmented nucleus image obtained by applying DEM algorithm based on Gaussian distribution (a), (c) Input image, (b), (d) Segmented nucleus image After the nucleus segmentation, the segmented nucleus image may contain small irrelevant objects which should be removed. Morphological opening operation of image by structuring elements and is performed, i.e. (7)where is dilation operator and is erosion operator. Erosion and dilation have been performed using disk-based structuring elements having size = 5 and = 3, respectively. Moreover, guided filter [21] is used for texture smoothing to preserve edges of the WBC image . The parameters used for the guided filter are radius of the filter, i.e. r and regularisation parameter, i.e. , where and (8)It is possible to visually distinguish normal lymphocytes and lymphoblasts by some characteristics such as the shape of nuclei, their colour and texture. Fig. 3 shows the features extracted for the classification purpose. Morphological features mainly include the shape features of the WBC nucleus such as perimeter, area, eccentricity, orientation, solidity and elongation. Other shape features which are calculated include compactness, ratio between nucleus area to perimeter and number of nucleus edge pixels. Table 1 presents the shape-based differences between the normal and blast cells. Table 1. Shape features of normal and blast cells Features Normal Lymphoblast images area 9497 10,057 perimeter 355.85 397.89 elongation 1.25 1.71 circularity 0.94 0.80 eccentricity 0.59 0.81 area/perimeter 26.68 25.27 edge pixels 423 483 orientation −84.04 −57.47 solidity 0.98 0.85 Fig. 3Open in figure viewerPowerPoint Feature extraction Texture features are also utilised to classify lymphocytes and lymphoblasts. In proposed scheme, LBP [22] is used as a texture feature over image in which each pixel is substituted by a binary pattern acquired from the neighbourhood of the pixel [23, 24]. In addition, HD is considered as an important texture feature for classification. We have used HD [25] measurement using box counting method in which the segmented nucleus image is superimposed by a grid of boxes as (9)where t represents the number of boxes present in the grid and shows the number of boxes occupied. High value of HD indicates roughness of larger degree. An example of HD is illustrated in Fig. 4. Moreover, segmentation-based fractal texture analysis (SFTA) [26] is used as an efficient method which consists of two steps: decomposition of the input WBC image into various binary images and for each binary image, WBC boundaries are used to compute fractal dimensions. We have set value of , where represents the number of thresholds. The resultant binary image has three feature values, i.e. area, mean grey level and fractal dimension of boundaries. Table 2 shows the detailed values. Table 2. SFTA [26] features of normal and blast cells Features Normal Lymphoblast images area 760 799 mean 71.89 85.41 fractal dimension 1.11 1.19 Fig. 4Open in figure viewerPowerPoint Results of HD [25] (a), (b) Non-cancer cell image with HD = 1.098, (c), (d) Cancer cell image with HD = 1.155 The excessive pigmentation in the lymphoblast nucleus is another important characteristic for classification of normal and abnormal cells. The abnormality of chromatin leads to high pigmentation of nuclei. The lymphoblast nuclei pigmentation appears in the form of colour intensity changes. Such changes from normal to abnormal cell can be measured in the form of mean colour intensity in hue saturation value and RGB colour spaces. Therefore, six features are calculated to represent the change in colour, i.e. mean intensity for H, S and V as well as R, G and B components. For the purpose of feature selection, principal component analysis (PCA) [27] has been used for selecting a significant feature set. All features are combined in a feature vector and fed into PCA as an input (10)where is the resultant vector obtained after the application of PCA. Further, we have used sparse representation classifier [28], which so far has not been used for ALL classification, in order to classify the extracted features (in the form of resultant vector ) into two classes normal lymphocytes (non-cancerous) and lymphoblasts (cancerous). Test image has been represented in the form of an overcomplete dictionary where training images are used as base elements. Moreover, optimisation problem has been solved using -minimisation. The -norm has been minimised using a primal–dual algorithm based on [29, 30]. For any given test image, its sparse representation is computed using -minimisation. Due to some modelling and noise errors, non-zero entries may be linked with multiple classes, therefore, we classify the test image depending upon the degree to which the coefficients related to all the training images of each class reproduce the test image. Final classification of test image has been done by assigning it to the class that can minimise the residual between the original test image and its approximation. 3 Results and discussion For evaluation purpose, the proposed and existing NN [5], SVM [11], BBPSO [12] schemes are simulated on ALL-IDB2 [31] database. The database contains a total of 260 WBC including 130 normal lymphocytes and 130 lymphoblasts. Figs. 5 and 6 present the multiple cases of normal lymphocytes and lymphoblast WBCs. The quantitative comparison is performed using true positive (TP), false positive (FP), true negative (TN), false negative (FN), specificity, sensitivity and accuracy. TP measures the number of correctly identified cancerous cells. TN specifies the number of correctly identified non-cancerous cells. FP calculates the number of non-cancerous cells identified as cancerous. FN quantitatively measures the number of cancerous cells identified as non-cancerous. Sensitivity parameter has been defined here as the probability of a positive diagnosis test among persons that have the disease (ALL). Specificity has been defined as the probability of a negative diagnosis test among persons that do not have the disease (ALL). Accuracy is a measure to reflect the closeness of the real value and the output produced by the classifier. Fig. 5Open in figure viewerPowerPoint Multiple cases of normal WBCs from ALL-IDB2 database Fig. 6Open in figure viewerPowerPoint Multiple cases of ALL WBCs from ALL-IDB2 database Out of 260 WBC images, 160 images are used for training of sparse classifier whereas 100 images are used for validation/testing purpose. Validation has been performed by taking unknown WBC test images as input of the trained sparse classifier. Results are evaluated using quantitative measures. Parameters setting of NN [5], SVM [11] and BBPSO [12] techniques used for comparison purpose is given in Table 3. The proposed technique has also been tested by conducting a performance evaluation of individual features considering ALL-IDB2 dataset over 100 test images in Table 4. After the analysis of individual features shown in Table 4, it can be concluded that SFTA texture feature has the highest classification accuracy and holds greatest importance in accurately differentiating lymphocytes and lymphoblasts. Other texture features such as LBP and HD also possess comparable performance. The classification accuracy of texture features, i.e. LBP, HD and SFTA is high as compared to the colour and shape features. Quantitative results of lymphocytes and lymphoblasts classification over test images compared with the existing techniques NN [5], SVM [11], BBPSO [12] using sensitivity, specificity and accuracy are listed in Table 5. Table 5 clearly shows that the proposed technique outperformed our referred publications NN [5], SVM [11], BBPSO [12] and provides better accuracy rate, when compared with the existing techniques (Fig. 6). Table 3. Parameters setting Technique Parameters NN [5] GLCM feature vector length: 4 (energy, entropy, contrast, correlation) HD feature vector length: 1 combined feature vector length: 5 SVM [11] Haralick GLCM [32] feature vector length (WBC nucleus + WBC cytoplasm): 13 + 13 (energy, entropy, sum entropy, contrast, correlation, angular second moment, homogeneity, variance, sum variance, difference variance, difference entropy, inverse difference moment, maximal correlation coefficient) shape feature vector length (WBC cytoplasm): 11 combined feature vector length: 37 BBPSO [12] centre composite design (CCD) for SVM: scaling factor for low(−1): 2 scaling factor for medium(0): 4 scaling factor for high(+1): 8 soft margin constant for low(−1): 2 soft margin constant for medium(0): 4 soft margin constant for high(+1): 8 shape feature vector length: 16 texture feature vector length: 54, i.e. (Haralick GLCM [32]: 13 (in 0, 45, 90, and 135 angles) + skewness, kurtosis) colour feature vector length: 10 Table 4. Performance of individual features considering ALL-IDB2 dataset over 100 test images using the proposed technique Parameter Shape LBP HD SFTA Colour TP 37 43 39 42 35 FP 13 7 11 8 15 TN 39 40 41 44 36 FN 11 10 9 6 14 accuracy 76% 83% 80% 86% 71% Table 5. Performance of the proposed and existing techniques considering ALL-IDB2 dataset over 100 test images Parameter Specificity, % Sensitivity, % Accuracy, % NN [5] 83.33 82.85 83 SVM [11] 89.39 85.93 87.69 BBPSO [12] 96.82 89.55 93.07 proposed 94.91 92.95 94 4 Conclusion The proposed technique that is a combination of preprocessing, segmentation, feature extraction, appropriate feature selection using PCA and classification of WBC using sparse representation classifier yields accurate results as compared to the existing techniques NN [5], SVM [11] and BBPSO [12]. Quantitative comparisons with the existing techniques [5, 11, 12] are provided to show the superior accuracy of the proposed technique. 5 Acknowledgment This research has been evaluated by the Department of Pathology at Fazaia Medical College, Islamabad, Pakistan. Their comments about the research are: ALL is the most common type of leukaemia in children (newborn to 14 years of age). Laboratory diagnosis of ALL requires routine conventional tests along with some special tests for the diagnosis and further classification into early Pre B-cell, Pre-B-cell and Pre-T-cell ALL. This process is time consuming, expensive and requires specialised diagnostic unit. Development of an accurate automated diagnostic and statistically significant classification technique by utilising multiple relevant reproduce-able features will improve the patient management by reducing the time of diagnosis along with reduction in the cost of treatment. 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