Abstract
Acute lymphoblastic leukemia (ALL) is the most frequently leukemia and categorized into three morphological subtypes named L1, L2 and L3. Early diagnosis of ALL plays a key role in treatment procedure especially in the case of children. Several similarities between morphology of three subtypes ALL (L1, L2, L3) and lymphocyte subtypes (normal, reactive and atypical) as noncancerous cells have remained a high challenge. Diagnosis of ALL and lymphocyte subtypes are done by microscopic viewing examination of cells in the peripheral blood samples by hematologists. Since this exam is time-consuming, boring and dependent on the skill of the hematologists, automatic systems are desired to overcome these limitations. In this study, 312 microscopic images including 958 cells are obtained from blood samples of 7 normal subjects and 14 patients. The first step of proposed system is image enhancement to decreases the effects of various luminosity situations with transformation from RGB to HSV color space and then applying histogram equalization on V channel for equalizing the grey level of image lightness. Nuclei segmentation from the blood cell images is the second step and performed using fuzzy c-means (FCM) clustering. After identify cluster of nuclei, we performed opening and closing process in morphological operation binary in order to remove extra noises and fill some minor holes in the nuclei. Moreover, to discrete the link between nuclei, watershed transform was applied. Then, a set of quantitative features (five geometric features about the size and figure of a cell and 36 statistical features about the spatial arrangement of intensities of nuclei image) are extracted to characterize the properties of these nuclei. In the next step, due to high number of features, the best features are selected by exhaustive search of all of the subsets of features and 13 features are selected. The final step is the classification of L1, L2, L3, normal, reactive and atypical cells by applying Random Forest (RF) classifier and result in 98% accuracy. We compared RF classifier with two other commonly classifiers named: MultiLayer Perceptron (MLP), and multi-SVM classifier with more success especially for recognition of L1, normal and reactive cells. So, this system can be used as an assistant diagnostic tool for hematologists to recognize subtypes of ALL and lymphocyte.
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References
Parkin DM, Pisani P, Ferlay J (1999) Global cancer statistics. CA Cancer J Clin 49(1):33–64
Bain BJ (2017) A beginner’s guide to blood cells. Wiley, Hoboken
Bain BJ (2017) Leukaemia diagnosis. Wiley, Hoboken
Ries LA et al (2006) SEER cancer statistics review, 1975–2003
Sawyers CL, Denny CT, Witte ON (1991) Leukemia and the disruption of normal hematopoiesis. Cell 64(2):337–350
Tkachuk CD, Hirschmann JV (2007) Wintrobe’s Atlas of clinical hematology, 1st edn. Lippincott Williams and Wilkins, Philadelphia
Haworth C et al (1981) Routine bone marrow examination in the management of acute lymphoblastic leukaemia of childhood. J Clin Pathol 34(5):483–485
Bain BJ (2005) Diagnosis from the blood smear. N Engl J Med 353:498–507
Theml H, Diem H, Haferlach T (2004) Color Atlas of Hematology. Thieme
Madhloom H et al (2010) An automated white blood cell nucleus localization and segmentation using image arithmetic and automatic threshold. J Appl Sci 10(11):959–966
Nee LH, Mashor MY, Hassan R (2012) White blood cell segmentation for acute leukemia bone marrow images. J Med Imaging Health Informatics 2(3):278–284
Theera-Umpon N (2005) White blood cell segmentation and classification in microscopic bone marrow images. In: International conference on fuzzy systems and knowledge discovery, Springer
Scotti F (2005) Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images. In: Proceedings of IEEE international conference on computational intelligence for measurement systems and applications, pp 96–101
Amin MM et al (2015) Recognition of acute lymphoblastic leukemia cells in microscopic images using K-means clustering and support vector machine classifier. J Med Sign Sens 5(1):49
Moradi M et al (2015) Enhanced recognition of acute lymphoblastic leukemia cells in microscopic images based on feature reduction using principle component analysis. Front Biomed Technol 2(3):128–136
Mohapatra S, Patra D, Satpathy S (2014) An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images. Neural Comput Appl 24(7):1887–1904
Acharya V, Kumar P (2019) Detection of acute lymphoblastic leukemia using image segmentation and data mining algorithms. Med Biol Eng Comput 57(8):1783–1811
Laosai J, Chamnongthai K (2018) Classification of acute leukemia using medical-knowledge-based morphology and CD marker. Biomed Signal Process Control 44:127–137
Rawat J, Singh A, Virmani J, Devgun JS (2017) Computer assisted classification framework for prediction of acute lymphoblastic and acute myeloblastic leukemia. Biocybern Biomed Eng 37(4):637–654
Jothi G, Hannah Inbarani H, Azar AT, Devi KR (2019) Rough set theory with Jaya optimization for acute lymphoblastic leukemia classification. Neural Comput Appl 31(9):5175–5194
Moshavash Z, Danyali H, Helfroush MS (2018) An automatic and robust decision support system for accurate acute leukemia diagnosis from blood microscopic images. J Dig Imaging 3(5):702–717
Rawat J, Sigh A, Bhadauria HS, Virmani J, Devgun JS (2018) Leukocyte classification using adaptive neuro-fuzzy inference system in microscopic blood images. Arab J Sci Eng 43(12):7041–7058
Bezdek JC et al (1999) Fuzzy models and algorithms for pattern recognition and image processing. Springer Science & Business Media, Berlin, p 4
Khajehpour H et al (2013) Detection and segmentation of erythrocytes in blood smear images using a line operator and watershed algorithm. J Med Sign Sens 3(3):164
Bertsekas DP (1995) Nonlinear programming. Athenas Scientific, Belmont
Shalbaf A, Shalbaf R, Saffar M, Sleigh J (2020) Monitoring the level of hypnosis using a hierarchical SVM system. J Clin Monit Comput 34(2):331–338
Azarmi F, Ashtiani SNM, Shalbaf A, Behnam H, Daliri MR (2019) Granger causality analysis in combination with directed network measures for classification of MS patients and healthy controls using task-related fMRI. Comput Biol Med 115:103495
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Criminisi A, Shotton J, Konukoglu E (2012) Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Found Trends Comput Graph Vis 7(2):81–227
Wu B et al (2003) Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data. Bioinformatics 19(13):1636–1643
Mirmohammadi P, Rasooli A, Ashtiyani M, Amin MM (2018) MR Deevband: automatic recognition of acute lymphoblastic leukemia using multi-SVM classifier. Curr Sci 115(8):1512–1518
Mirmohammadi P, Taghavi A, Ameri A (2017) Automatic recognition of acute lymphoblastic leukemia cells from microscopic images. Int J Innovat Res Sci Eng 5(7):8–11
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This study was conducted according to the declaration of Helsinki and received ethics committee approval at Isfahan University of Medical Sciences.
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Mirmohammadi, P., Ameri, M. & Shalbaf, A. Recognition of acute lymphoblastic leukemia and lymphocytes cell subtypes in microscopic images using random forest classifier. Phys Eng Sci Med 44, 433–441 (2021). https://doi.org/10.1007/s13246-021-00993-5
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DOI: https://doi.org/10.1007/s13246-021-00993-5