Segmentation techniques for early cancer detection in red blood cells with deep learning‐based classifier—a comparative approach

IET Image ProcessingVolume 14, Issue 9 p. 1726-1732 Research ArticleFree Access Segmentation techniques for early cancer detection in red blood cells with deep learning-based classifier—a comparative approach Jeya Sudharsan Shemona, Corresponding Author shemona.js@gmail.com Department of Information and Communication Engineering, Anna University, Chennai, IndiaSearch for more papers by this authorAgees Kumar Chellappan, Department of EEE, Arunachala College of Engineering for Women, Tamil Nadu, IndiaSearch for more papers by this author Jeya Sudharsan Shemona, Corresponding Author shemona.js@gmail.com Department of Information and Communication Engineering, Anna University, Chennai, IndiaSearch for more papers by this authorAgees Kumar Chellappan, Department of EEE, Arunachala College of Engineering for Women, Tamil Nadu, IndiaSearch for more papers by this author First published: 22 May 2020 https://doi.org/10.1049/iet-ipr.2019.1067Citations: 3AboutSectionsPDF 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 onEmailFacebookTwitterLinked InRedditWechat Abstract Red Blood Corpuscles called Erythrocytes are the most important element in blood composition which is mainly responsible in all living cells. To detect the cancer cell various methods are employed. In this paper, proper identification of cancer cells from unaffected RBCs are detected. The proposed novel method called Online Region Based Segmentation (ORBS) method is done that is used to find the regions of corpuscles. By using properties, metric is formulated for determination of shape which is abnormal in blood cells. Overall accuracy of 96.9% is obtained using proposed ORBS method and deep learning classification (DLC) method has accuracy of 97.1% that helps to diagnose cancer cells using feature extraction process done automatically. Sensitivity, specificity and precision values of the proposed segmentation method is found to be 96.7%, 95.6% and 98.4% respectively. The computation time was found as 22 seconds. Closeness of Proposed method in relative to True Positive values at the ROC curve indicates the performance as higher. Comparative analysis is made with ResNet-50 based on the different testing and training data at rate of 90%−10%, 80%−20% and 70%−30% respectively, which proves the robustness of proposed research work. Experimental results prove proposed system effectiveness compared with other detection methods. 1 Introduction The most important cause for the increase in death rate in the world is blood cancer. The death rate by blood cancer estimated by the National Cancer Institute in the year 2018 was 6,09,640. Nearly 75% of people are seriously affected by blood cancer. The initial steps of red blood cell detection dates back to the 1950s. In the beginning, there was only manual detection by the radiologists which is time-consuming and hence a computer-aided design (CAD) system of diagnostic measure provides ease in the detection of cancer cells. This computerised examination of blood points prevents false diagnosis of the tumour to a great extent. The CAD diagnosis depicts the work done in the year 1975 by Bentley and Lewis [1]. Mean corpuscular state of the volume of data points to the erythrocyte size and also a range of haemoglobin corpuses. Automated development of slides in blood was developed by Rowan and England [2] in the year 1986, where the accuracy of segmentation and classification was 75.1 and 76.15%, respectively. Rowan and England in 1986 proposed a method to separate and also to classify the cell overlapping [2] by using the methodology of image processing. Accuracy of 74.7% at the step of segmentation and the classification accuracy is 76.5%. These approaches are developed by Kass et al. in 1987 [3]. Osher and Sethian proposed [4] a set form of approach, to prevent encountered with the snake level methods with the level of 74.3% accuracy. Vincent and Soille in 1991 [5] and Beucher and Meyer in 1992 [6] developed watershed-based segmentation at the improvised accuracy related to the segmentation and classification of 75.17 and 79.15%, respectively. Zhu and Yuille in 1996 [7] and Caselles et al. [8] mainly divided the process of detecting blood cancerous cells into two main categories for which segmentation at an energy-driven concept is proposed. Vese and Luminata in 2002 [9] proposed the level set at a multi-forced method that always tends to allow for the topological representation. The level sets proposed by Zhang et al. in 2004 [10]. The accuracy at the time of segmentation is found as 79.85% and classification is 80.14%. It becomes worst when the cell overlaps with a group. Zimmer and Olivo-Marin in 2005 [11] based on contours that prevent the developing contours of the development of nuclei that make contact from becoming merged. Energy ranged functional was developed with an overlap that has a penalty between contours at the paired range. The proposed method by Lefevre in 2007 [12] requires spatial initialisation that determines the initial range of positions. Most of the earlier related work use MATLAB toolbox for dealing with the images' format of the algorithm by Lias in 2015 [13]. The entire set of blood count 'CBC ' or 'common blood count' process is fully automated techniques that are computerised with higher reliability with segmentation accuracy of 91.7% and classification accuracy of 92.14%. Yang et al. [14] used a trained convolutional neural network, which has an accuracy at the classification stage of 92.15% and segmentation of 92.61%. Elsalamony and Hany in 2018 proposed a method using circular Hough transform with watershed segmentation has an accuracy of 92.3% and a classification accuracy of 93.17% [15]. Jha and Dutta in 2019 [16] proposed hybrid modelling. Ferrauto et al. in 2019 [17] derived the tumour vascular value for segmentation. Ghassemi et al. in 2019 [18] found the post enrichment of the tumour cells and Selim Abu Lila and co-workers in 2019 [19] developed the system with cancer cell type with the cancer cell exosomes derived to each tumour cell but it leads to inaccuracies. Therefore, it is needed to develop the system which assists and also alleviates the suffering with the repetitive work of common physicians. In order to detect the cancer cells from normal cells, various segmentation concepts [20] are involved region-based segmentation [21], edge-based technique [22], thresholding technique [23]. The various classification method based on neural network classifier [24], support vector machine (SVM) classifier [25], and decision classifier [26]. The limitations related to the above classification method deals with the improper semantic segmentation, lower transparency, and complex calculations. 1.1 Research gap identified The segmentation in the existing methods is not accurate where there is an overlapping of the cells in the images. The region-based segmentation performs with local parameter extraction, leads to the blurring of the images. Optimal parameter determination is not easy as it leads to linearly separable data induced for training where the erroneous values are always high. Time consumption is high for larger datasets due to the non-linear segmentation of the blood cells and also due to improper boundary detection. The contribution of the proposed research includes the following: (i) The proposed map segmentation technique automatically provides segmentation of a block into sub-regions with definite textures or colour patches without knowing the number of regions in advance whereas the existing segmentation technique concentrates on texture as in [27]. (ii) The advantage of using the K-means clustering method in the proposed research is that it effectively determines the set of seed points, thereby avoiding the chances of overlap. The proposed clustering methods provide the grouping of similar pixel values together with similar intensity values while the existing methodology deals with the localised seed point clustering model as in [28]. (iii) Proposed online region-based segmentation (ORBS) is proposed that overcomes the limitations existing in [29] by proper clustering and the segmentation of the blood cells for the extraction of cancer cells. (iv) To demonstrate the effectiveness of the proposed technique, the ORBS-based segmentation is carried out for higher accuracy with better segmentation results when compared to the other segmentation methods [16, 22, 30]. (v) One of the most proven deep learning technique using Resnet-50 is used for classification to classify the tumour as benign or malignant that yields better accuracy by reducing the false positives, whereas most of the existing techniques provide a high false-positive rate as in [31], which in turn assists the radiologists to diagnose cancer at an early stage more accurately. (vi) An in-depth comparative analysis with various performance measures in case of both segmentation and classification is carried out to demonstrate the effectiveness of the proposed system The organisation of the paper is as follows: in Section 2, the image acquisition method is described where the image is acquired from the scanners at the biomedical centres. The preprocessing stage is done where the resizing along with colour conversion occurs, followed by the histogram equalisation and segmentation involving the new method of proposed map segmentation followed by K-means clustering and the proposed ORBS method. The final step of classification is done with feature extraction and deep learning methodology. Section 3 provides performance measures, Section 4 explains the result analysis and Section 5 gives an evaluation of performance values. 2 Proposed methods The algorithm for the proposed flow has the following steps where the first stage is the acquisition of image followed by pre-processing of the image, with the platelets' removal along with leucocytes is undertaken. 2.1 Image acquisition The process of getting or acquiring the images by the means of sensors etc. The digital image involves the method of creation of a digital form of the encoded representation of the characteristics of the object undertaken. The scanner involved in the proposed research work uses the magnetic resonance imaging scanning technique (Fig. 1). The images are obtained from the BCCD dataset [32] with blood samples of 200 benign and 200 malignant images from 534 patients. These images are then sent to further processing for the detection of cancer cells from normal cells. Fig. 1Open in figure viewerPowerPoint Proposed methodology of the RBC cancer cell segmentation 2.2 Preprocessing The prior content of the above step is the development related to the quality of the image that is acquired. The method is used to pre-process the images with better resolution and appearance for further processing. (i) Colour conversion: The colour conversion involves the conversion of the colour intensity values from red–green–blue (RGB) to hue saturation value (HSV). This The hue is found by the following (1) and (2), where RGB indicates the colour combinations as follows: (1) (2) The saturation value for the HSV function is defined by the following equation: (3)where C is the chromatic intensity and B is the brightness value due to the pixels in an image. (ii) Equalisation of histogram: The general histogram equalisation formula is given as (4)where is the non-zero with the cumulative distributive function as a minimum value, M × N indicates the pixel numbering, L is the grey level numbering. The equalisation formula of scaling value 0–255 is given by (5)The new pixel level value is calculated for each of the brightness values in the original image as (6) 2.3 Segmentation Segmentation forms the method of segmentation with the process of extracting and representing the detailed range of information content from an image to other group of pixels paired together in various regions of measures of similarity. (i) Proposed map segmentation: In terms of the function related to the proposed map segmentation task, the maximised Markov random field function refers to the identification of feature values concerning labels in the image. This enables the masking features with definite prior values (7) (8) (9) The more penalised the object, the longer will be the boundary. K-means clustering: Clusters involve the grouping of similar pixel values. K initial centroids that are selected from the datasets are obtained from (10) that corresponds to the data assignment and (11) that indicates the centroid phase (10) (11) (ii) Region-based segmentation: The region-based method likely relies based on assumption with the range of pixels that acts as neighbours within a particular range of region that has the same values. (a) Proposed ORBS: In order to avoid the situation corresponding to the cancerous features a new ORGB segmentation approach for color imaging segmentation is proposed. After applying the local search and thresholding algorithms for RG method, the initial seeds and the optimal threshold. The source color image is classified into various regions of pixels where everyone corresponds to a certain seed. The term of Region growing is different from split and merge region approach. The concept of iteratively merging the small areas with the similarity constraints at the initial set is calculated for better results. An arbitrary seed pixel is chosen and the process is started and compared with nearest pixels. 2.4 Classification Classification of tumour cells is carried out with the features extracted from the blood sample and it is computed based on the following formula: (12) (13)The generic algorithm for the proposed map segmentation in an image is given below: (i) Define the factors of neighbourhood feature corresponding to each pixel that represents the random variable. The probabilities at each pixel are set for each feature found as 0 or set containing features depicted with the initial cluster set with a pixel value is defined. The metric for the proposed map segmentation given in (10) as where the above equation generally measures the shape of object. For perfect round shape, the value of M is found to be 1. (ii) Distribution at the marginal label scheme using the theorem of Bayes approach is defined and the calculation of statistics at the class is done at the earlier stage. The marginal Gaussian distribution model is used. (iii) The probability is calculated for each labelled class given with previously defined neighbourhood clique. (iv) Iteration is performed over new probabilities and also redefines the cluster such that maximum probability is done using different optimisation algorithms. The steps of clustering are as follows: (i) The centres of clusters are picked based on the randomly selected heuristic method. (ii) Assign the form of pixel with reference to the cluster corresponding to the image, which reduces the mean distance aligned between the assigned pixel that is chosen and centre of the cluster. (iii) Computation at centres of the cluster is developed by the process of the average stage that forms of all the pixels in cluster particles. (iv) Repeat the above steps denoted as (ii) and (iii) until the rate of convergence is attained. The proposed ORBS as (i) The seed pixels are selected at the image. (ii) From these pixels, a region is grown (a) After that, the region prototype for seed pixel is set. (b) The similarity between each of the prototype is calculated and the pixel candidate is chosen assuming that the samples are drawn from their centroids and the similarity can be formulated as the following equation: where refers to the cluster centroid that is nearest to the point, which is transformed , i.e. taken as the reference point. It solves both the clustering problem and the problem of registration. (c) Also, the similarity between the candidate along with its nearest neighbour is calculated in the region where the updation of the centroids of all the clusters is done sequentially given by the equation that depends on the condition if both the similarity measures are very much higher than the experiment described with the set thresholds (d) After that sequential updation is performed such that the region prototype is calculated along with the new component of principal analysis rigid transformation. (e) Finally, go to the next pixel to be examined by following (15) The shape of the object can be measured through skewness based on formula (14) (14) (15) (16)(i) Deep learning: By using deep learning techniques, automatic detection of cells under cancer affected regions are (Fig. 2) (17)where W denotes the input volume size, K is the size of the kernel field and S is the total number of pixels, and P is the padded values to the image. Fig. 2Open in figure viewerPowerPoint Deep learning architecture The value of P is given as (18) 3 Performance measures (i) Segmentation: For the calculation of the peak signal-to-noise ratio (PSNR), the mean squares error (MSE) has to be calculated as (19) (20) (21) (22)where and (23) (ii) Classifier: (24)where TP, FP, TN, and FN are the true positive, false positive, true negative, and false negative, respectively. (25) (26) 4 Results and discussion The proposed algorithm has been implemented using Matlab on a windows PC having a CPU speed of 2.6 GHz and 4 GB of RAM in MATLAB 2018a. A large set of experiments are performed on several datasets. The sample images are from the database. These images are obtained from the BCCD dataset. Here, some of the images are analysed and are given in the CAD system. Some samples are taken out to show that it has been used for analysing the output of the proposed system shown in Fig. 3. The following results are obtained using the samples taken from various patients with the red blood corpuscles (RBC) cancerous cells (Fig. 4). Fig. 3Open in figure viewerPowerPoint Different sample input images for processing (a) Sample 001, (b) Sample 023, (c) Sample 070 Fig. 4Open in figure viewerPowerPoint RGB to HSV colour conversion (a) Sample 001, (b) Sample 023, (c) Sample 070 Fig. 5 provides the histogram equalised image, histogram equalisation finds the intensity pixel values in the input image such that the output contains intensity distribution which is uniform. Fig. 5Open in figure viewerPowerPoint Histogram equalisation (a) Sample 001, (b) Sample 023, (c) Sample 070 The histogram equalised image is then fed to the segmentation mapping given at Fig. 6 for the proper mapping of unaffected pixel to the cancer cell pixel. Fig. 6Open in figure viewerPowerPoint Proposed map segmentation (a) Sample 001, (b) Sample 023, (c) Sample 070 The clustering model in Fig. 7 indicates the same value of the pixel intensity to be grouped together so that they can be easily detected. Fig. 7Open in figure viewerPowerPoint Clustering (a) Sample 001, (b) Sample 023, (c) Sample 070 K-means segmentation in Fig. 8 shows better segmentation so that they can be aligned for the proper interpretation. This segmentation always segments the cancer cells even at the smaller area in the blood cell. Fig. 8Open in figure viewerPowerPoint K-means segmentation (a) Sample 001, (b) Sample 023, (c) Sample 070 In Fig. 9, the proposed ORBS is used for its proper detection. Fig. 9Open in figure viewerPowerPoint Proposed ORBS (a) Sample 001, (b) Sample 023, (c) Sample 070 The deep learning classifier provides 100% accuracy for the benign tumours and 97.14% in malignant tumours as shown in Fig. 10. Fig. 10Open in figure viewerPowerPoint Confusion matrix for deep learning classifier 5 Performance evaluation By following the above steps, the performance is evaluated. The cluster shade with the prominence value, probability, correlation is compared for a different set of samples, which provides the cancerous cell detection (Table 1). Table 1. Analysis of feature values Contrast Cluster shade Cluster prominence Correlation Dissimilarity Probability 1 0.456 −23.075 354.089 0.9645 0.356 0.1954 23 0.541 −19.067 368.147 0.8546 0.284 0.1594 70 0.266 −22.978 295.654 0.8745 0.375 0.1459 278 0.498 −18.546 270.954 0.9651 0.422 0.1654 400 0.537 −21.886 345.942 0.7344 0.312 0.1845 The optimised solution is obtained with the proposed research work detects the cancerous cells from the blood cells in the image. The results reveal that the proposed method gives the best results (Tables 2 and 3). Table 2. Performance metrics Metrics Watershed method [33] Region-based method [22] Proposed methodology PSNR 92 94.5 97.6 MSE 0.0845 0.0984 0.0258 statistical shape model 0.84 0.91 0.90 correlation energy 0.28 0.45 0.98 smoothness 5.49 8.54 9.87 Table 3. Comparative analysis of the proposed research work with existing methods Study Method Segmentation accuracy, % Zhang and co-workers [10] active appearance model 84.3 Zhang and Wu [23] multilevel thresholding 86.58 Wu [25] SVM learning 87.14 Elsalamony and Hany [15] circular Hough transform with watershed segmentation 92.3 Gao et al. [34] radial tracing method 95.1 proposed method ORBS segmentation with deep learning classifier 97.1 The comparison in Table 4 indicates the performance of various classifiers. The SVM is most commonly used but the proposed classifier exhibits better performance especially in the reduction of time values. Table 4. Comparison of classifier performance Classifier BRT SVM KNN ANN Proposed sensitivity, % 90.4 94.6 93.1 92.3 96.7 specificity, % 94.8 95.4 83.6 93.5 95.6 precision, % 96.5 91.3 85.3 84.7 98.4 accuracy, % 91.7 94.7 91 92.7 97.1 time, s 45 31 26 34 22 BRT, boosted regression trees; KNN, K-nearest neighbour; ANN, artificial neural network. From Table 5, it is inferred that the true positive values of the image should always be higher for better results and the false negative should be as low as possible (Fig. 11). Table 5. Comparison of classifier values concerning the confusion matrix Classifier TP, % FP, % FN, % TN, % SVM 41.9 57.7 42.3 58.1 KNN 45.6 56.1 43.9 54.4 ANN 44.9 56.6 43.4 55.1 BRT 43.7 58.2 41.8 56.3 proposed 48.5 64.9 35.1 51.5 Fig. 11Open in figure viewerPowerPoint Comparison of various parameters related to the existing and proposed method The receiver operating characteristic (ROC) curve depicts the comparison of the various classifiers along with the proposed classifiers. The true positive value is high which implies that the method outperforms the other method for easy analysis of the cancerous cells from the RBC cells (Fig. 12). Fig. 12Open in figure viewerPowerPoint ROC curve for deep learning classifier 6 Conclusion This study presents a novel approach that integrates the edge detection method with the new method of the proposed ORBS algorithms which increase the performance of segmenting colour images. Therefore, the proposed map segmentation and ORBS methods are utilised for the classification of these regions depending on similarity between the pixels. The proposed ORBS segmentation provides a rate of 96.9% in comparison with other methods. The accuracy value is 97.1%, which is higher when compared to other methods. The obtained results reveal the robustness of selecting seeds and optimal thresholds. The ROC curve implies that the method outperforms the other existing method based on the closeness factors related to true positive values. The proposed method provides higher benefits that help the radiologists for better classification. Experimental results prove that the proposed deep learning ResNet-50 provides improved performance with high accuracy. This study mainly finds application in the medical field which helps the radiologists to detect the cancer cells from the blood samples. As future work, the classification of blood cancer cells at the nanoscale images will be performed. Availability of data: This file contains the input data sets that are obtained from the BCCD dataset of red blood cells with tumours. 7 Acknowledgments The authors thank the management of Arunachala College of Engineering for Women, for their continuous support and encouragement throughout the work. The authors also acknowledge their critical role in the creation of the free public available BCCD database used in this study. Finally, we thank the anonymous reviewers for helping to strengthen this paper. 8 References 1Bentley S.A., and Lewis M.S.: 'The use of an image analysing computer for the quantitation of red cell morphological characteristics', Br. J. Haematol., 1975, 29, (1), pp. 81– 88Wiley Online LibraryCASPubMedWeb of Science®Google Scholar 2Rowan R., and England J.M.: 'Automated examination of the peripheral blood smear', Autom. Qual. Assur. Hematol., 1986, 1, pp. 129– 177Google Scholar 3Kass M., Witkin A., and Terzopoulos D.: 'Snakes: active contour models', Int. J. Comput. Vis., 1988, 1, (4), pp. 321– 331CrossrefWeb of Science®Google Scholar 4Cloppet F., Oliva J.M., and George S.: 'Angular bisector network, a simplified generalized voronoi diagram: application to processing complex intersections in biomedical images', IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, (1), pp. 120– 128CrossrefWeb of Science®Google Scholar 5Vincent L., and Soille P.: 'Watersheds in digital spaces: an efficient algorithm based on immersion simulations', IEEE Trans. Pattern Anal. Mach. Intell., 1991, 6, pp. 583– 598CrossrefWeb of Science®Google Scholar 6Beucher S., and Meyer F.: 'The morphological approach to segmentation: the watershed transformation',Math. morphology Image Process.', 1993, 34, pp. 433– 481Google Scholar 7Zhu S.C., and Yuille A.: 'Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation', IEEE Trans. Pattern Anal. Mach. Intell., 1996, 9, pp. 884– 900Google Scholar 8Caselles V., Kimmel R., and Sapiro G.: 'Geodesic active contours', Int. J. Comput. Vis., 1997, 22, (1), pp. 61– 79CrossrefWeb of Science®Google Scholar 9Vese T.F., and Luminita A.: 'A multiphase level set framework for image segmentation using the Mumford and Shah model', Int. J. Comput. Vis., 2002, 50, (3), pp. 271– 293CrossrefWeb of Science®Google Scholar 10Cai R., Wu Q., and Zhang R. et al: ' Red blood cell segmentation using active appearance model'. 2012 IEEE 11th Int. Conf. on Signal Processing (ICSP), Beijing, People's Republic of China, October 2012, Vol. 3, pp. 1641– 1644CrossrefGoogle Scholar 11Zimmer C., and Olivo M.: 'Coupled parametric active contours', IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, (11), pp. 1838– 1842CrossrefPubMedWeb of Science®Google Scholar 12Lefèvre S.: ' Knowledge from markers in watershed segmentation'. Int. Conf. on Computer Analysis of Images and Patterns, Berlin, Germany, 2007, pp. 579– 586CrossrefGoogle Scholar 13Lias J.: ' Analysis of red blood cell (RBC) classification using via vision builder AI', PhD dissertation, Universiti Tun Hussein Onn Malaysia, 2015Google Scholar 14Yang X., Liu C., and Wang Z. et al: 'Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI', Med. Image Anal., 2017, 42, pp. 212– 227CrossrefPubMedWeb of Science®Google Scholar 15Elsalamony H., and Hany A.: 'Detection of anaemia disease in human red blood cells using cell signature, neural networks and SVM', Multimedia Tools Appl., 2018, 77, (12), pp. 15047– 15074CrossrefWeb of Science®Google Scholar 16Jha K.K., and Dutta H.S.: 'Mutual information based hybrid model and deep learning for acute lymphocytic leukemia detection in single cell blood smear images', Comput. Methods Programs Biomed., 2019, 179, p. 104987CrossrefPubMedWeb of Science®Google Scholar 17Ferrauto G., Di Gregorio E., and Dastrù W. et al: 'Gd-loaded-RBCs for the assessment of tumor vascular volume by contrast-enhanced-MRI', Biomaterials, 2015, 58, pp. 82– 92CrossrefCASPubMedWeb of Science®Google Scholar 18Ghassemi P., Ren X., and Foster B.M. et al: 'Post-enrichment circulating tumor cell detection and enumeration via deformability impedance cytometry', Biosens. Bioelectron., 2020, 150, p. 111868, doi: http://doi.org/10.1016/j.bios.2019.111868CrossrefCASPubMedWeb of Science®Google Scholar 19Emam S.E., Selim Abu Lila A., and Elsadek N.E. et al: 'Cancer cell-type tropism is one of crucial determinants for the efficient systemic delivery of cancer cell-derived exosomes to tumor tissues', Eur. J. Pharm. Biopharm., 2019, 145, pp. 27– 34CrossrefCASPubMedWeb of Science®Google Scholar 20Tulsani H., Saxena S., and Yadav N.: 'Segmentation using morphological watershed transformation for counting blood cells', Int. J. Comput. Appl. Inf. Technol., 2013, 2, (3), pp. 28– 36Google Scholar 21Chen Y.B., and Chen O.T.-C.: 'Image segmentation method using thresholds automatically determined from picture contents', EURASIP J. Image Video Process., 2009, 2009, pp. 1– 15, Article ID140492, doi: http://doi.org/10.1155/2009/140492CrossrefGoogle Scholar 22Kaganami H., and Beiji Z.: ' Region-based segmentation versus edge detection'. 2009 Fifth Int. Conf. on Intelligent Information Hiding and Multimedia Signal Processing, Kyoto, Japan, 2009, pp. 1217– 1221CrossrefGoogle Scholar 23Zhang Y., and Wu L.: 'Optimal multi-level thresholding based on maximum tsallis entropy via an artificial bee colony approach', Entropy, 2011, 13, (4), pp. 841– 859CrossrefWeb of Science®Google Scholar 24Seetha M., Muralikrishna I.V., and Deekshatulu B.L. et al: 'Artificial neural networks and other methods of image classification', J. Theor. Appl. Inf. Technol., 2008, 4, (11), pp. 1039– 1053Google Scholar 25Wu J.: 'Efficient HIK SVM learning for image classification', IEEE Trans. Image Process., 2012, 21, (10), pp. 4442– 4453CrossrefPubMedWeb of Science®Google Scholar 26Pal M., and Mather P.M.: ' Decision tree based classification of remotely sensed data', November 2001Google Scholar 27Chen S., Cao L., and Liu J. et al: ' Iterative MAP and ML estimations for image segmentation'. 2007 IEEE Conf. on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, 2007, pp. 1– 6CrossrefGoogle Scholar 28Kanugo T., Han M.J., and Li H. et al: 'Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA', Int. J. Remote Sens., 2013, 34, (1), pp. 45– 59CrossrefWeb of Science®Google Scholar 29Guan P.P., and Hong Y.: ' Blood cell image segmentation based on the Hough transform and fuzzy curve tracing'. 2011 Int. Conf. on Machine Learning and Cybernetics, Guilin, People's Republic of China, 2011, vol. 4, pp. 1696– 1701CrossrefGoogle Scholar 30Freixenet J., Muñoz X., and David R. et al: ' Yet another survey on image segmentation: region and boundary information integration'. European Conf. on Computer Vision, Copenhagen, Denmark, 2002, pp. 408– 422Google Scholar 31Cireşan D., Meier U., and Schmidhuber J.: ' Multi-column deep neural networks for image classification', arXiv preprint arXiv:1202.2745, 2012Google Scholar 32Liu B., Wang W., and Fan J. et al: 'RBC membrane camouflaged Prussian blue nanoparticles for gamabutolin loading and combined chemo/photothermal therapy of cancer', Biomaterials, 2019, 217, p. 119301CrossrefCASPubMedWeb of Science®Google Scholar 33Ramesh N., Salama M.E., and Tolga T.: ' Segmentation of haematopoeitic cells in bone marrow using circle detection and splitting techniques'. 2012 9th IEEE Int. Symp. on Biomedical Imaging (ISBI), Barcelona, Spain, 2012, pp. 206– 209CrossrefGoogle Scholar 34Gao R., Cheng L., and Wang S. et al: 'Efficient separation of tumor cells from untreated whole blood using a novel multistage hydrodynamic focusing microfluidics', Talanta, 2020, 207, p. 120261CrossrefCASPubMedWeb of Science®Google Scholar 35Vicente G, Mewes A.U.J., and Alcaniz M. et al: 'Improved watershed transform for medical image segmentation using prior information', IEEE Trans. Med. Imaging, 2004, 23, (4), pp. 447– 458CrossrefPubMedWeb of Science®Google Scholar 36Mahmood H.N., Che Poon L., and Mazalan S.M. et al: 'Blood cells extraction using color based segmentation technique', Int. J. Life Sci. Biotechnol. Pharma Res., 2013, 2, (2), pp. 233– 240Google Scholar 37Nguyen H.T., and Boomgaard Den V.R.: 'Watersnakes: energy-driven watershed segmentation', IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25, (3), pp. 330– 342CrossrefWeb of Science®Google Scholar 38https://blog.athelas.com/classifying-white-blood-cells-with-convolutional-neural-networksGoogle Scholar 39Mohideen Fatima Alias Niraimathi M., and Seenivasagam V.: 'Radial tracing method of cytoplasm segmentation in overlapped cervical cell images', IETE J. Res., 2015, 61, (4), pp. 402– 410CrossrefWeb of Science®Google Scholar 40Laddi S.K., Sharma S., and Kumar A.: 'Non-invasive jaundice detection using machine vision', IETE J. Res., 2013, 59, (4), p. 591CrossrefGoogle Scholar 41Elhelf I.A.S., Pillenahalli Maheshwarappa R., and Hodgson J. et al: 'Giant vertebral hemangioma masquerading as aggressive tumor: Tc-99m tagged RBC scan can help to solve the diagnostic conundrum!', Radiol. Case Rep., 2019, 14, (11), pp. 1360– 1363CrossrefPubMedGoogle Scholar Citing Literature Volume14, Issue9July 2020Pages 1726-1732 FiguresReferencesRelatedInformation