Automated detection in microscopic images using segmentation
DOI:
https://doi.org/10.38152/bjtv7n2-003Keywords:
segmentation, clustering, classification, image, automaticAbstract
In this paper, we present a segmentation clusteringbased approach for automated object detection. This paper deals with the segmentation and classification of blood cells for the purpose of detecting leukemia (abnormal blood cells). After the image acquisition and the preprocessing step, we proceeded to the application of the k-means method. In order to show the interest of the proposed approach, we present the different cancerous regions identified with their characteristics for biomedical diagnostic aid. The proposed method is tested on image dataset and achieves 98% segmentation accuracy. These results show that our approach offers encouraging performance and best automatic leukemia detection. The proposed system is successfully implemented in Matlab, experimental results demonstrate that our approach offers encouraging performance and better quality automatic leukemia detection.
References
Bouzid-Daho, A. Boughazi, M. and Tanougast, C. (2017) ‘Algorithmic Processing to Aid in Leukemia Detection’, In Medical Technologies Journal, Vol. 1, N°. 1, pp.10-11.
Donida Labati, R. Piuri, V. Scotti, F. (2011) ‘ALL-IDB: the acute lymphoblastic leukemia image database for image processing’, In Proc. of the 2011 IEEE Int. Conf. on Image Processing (ICIP 2011), Brussels, Belgium, pp.2045-2048.
https://leucemie.ooreka.fr/comprendre/pronostic- leucemie( 2018).
Wang, Q. Chang, L. Zhou, M. and L, Q. (2016) ‘A spectral and morphologic method for white blood cell classification’, ELSEVIER: Optics & Laser Technology, Vol. 84, pp.144-148.
Joshi, M.D, Karode, A.H. and Suralkar, S.R. (2013) ‘White Blood Cells Segmentation and Classification to Detect Acute Leukemia’, In International Journal of Emerging Trends and Technology in Computer Science, Vol. 2, N°. 3, pp.147-151.
Pham, D.L. Xu, C. and Prince, J.L. (2000) ‘Current Methods in Medical Image Segmentation’, In Annual Review of Biomedical Engineering," Vol. 2. pp.315-337.
Soltane.S (2012) ‘Adaptive approach to image Segmentation using Two Dimensional Cosine Transform 2D DCT’, IEEE 6th International Symposium on Signal, Image, Vidéo and Communications ISIVC 2012, july 4-6 Valenciennes France. P232-234.
N.A. Abu Osman, F. Ibrahim, W.A.B. Wan Abas, H.S. Abd Rahman, H.N. (2008), ‘Ting Blood Cell Image Segmentation: A Review’, Biomed 2008, Proceedings 21, pp. 141–144, 2008 www.springerlink.com © Springer-Verlag Berlin Heidelberg 2008.
Yasodha, K.R. Thiagarasu, V. and Phil, M. (2013) ‘Automatic Segmentation of Brain Tumor from MRI Images- A Review’, In International Journal of Computer Trends and Technology, Vol. 4, N°. 9, pp.3110-3116.
Mishra, S.J. and Deshmukh, A.P. (2015) ‘Detection of Leukemia Using Matlab’, In International Journal of Advanced Research in Electronics and Communication Engineering, Vol. 4, N°. 2, pp.394-398.
Aimi,S.A.Mohd,Y.M.andZeehaida,M. (2013) ‘ColourImageSegmentationApproachforDetectionofMalariaParasiter usingVariousColourModelsandk-MeansClustering’,InWSEASTransactiononBiologyandBiomedecine,Vol.10.
Bouzid-Daho, A. Boughazi, M. and Senouci, B. (2017) ‘Multispectral Images Segmentation for Biomedical Applications Diagnosis: K-Means Oriented Approach,’ In IEEE BioSMART’2017, 2nd International Conference on Bio-engineering for Smart Technologies, Paris, France. pp. 1-5.
Purohit, P. and Joshi, R. (2013) ‘A New Efficient Approach towards k-means Clustering Algorithm’, In International Journal of Computer Applications, Vol. 65, N°.11, pp.7-10.
Abdul Nazeer, K.A. and Sebastian, M.P. (2009) ‘Improving the Accuracy and Efficiency of the k-means Clustering Algorithm’, In Proceedings of the World Congress on Engineering, London, WCE, Vol. 1, pp.308-312.
Madhu, Y.Srinivasa, R. P. and Srinivasa, T.M. (2010) ‘Enhanced k-means Clustering Algorithm with Improved Initial Center’, In InternationalJournalofScienceandInformationTechnologies,Vol.1, N°.2,pp.121-125.
Pallavi, P. and Ritesh, J.A. (2013) ‘New Efficient Approach towards k-means Clustering Algorithm’, In International Journal of Computer Applications, Vol. 65, N°.11, pp.07-10.
Aleta, C.F., and all. (2017) ‘Enhanced Initial Centroids for K-means Algorithm’, In International Journal of Information Technology and Computer Science, Vol. 1, pp. 26-33.
Mohamed, M. and Far, B. (2012) ‘An Enhanced Threshold Based Technique for White Blood Cells Nuclei Automatic Segmentation’, In IEEE 14th International Conference on e-Health Networking, Applications and Services, DOI: 10.1109/HealthCom.2012.6379408.
Alan, J. A. Ravi, S. and Sambath, M. (2014) ‘Brain Tumor Segmentation using k-means Clustering and Fuzzy C-means Algorithm and its Area Calculation’, In International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, N°. 3, pp.3496-3501.
Scotti F (2006) ‘Robust segmentation and measurements techniques of white cells in blood microscope images’. In: Daponte P, Linnenbrink T, editors. In: Proceedings of the IEEE instrumentation and measurement technology conference, 24–27 Apr. IEEE Publisher, Sorrento, Italy, pp 43–48
Putzu L, Di Ruberto C (2013) ‘White blood cells identification and classification from leukemic blood image’ In: Rojas I, Ortuo Guzman FM (eds) Proceedings of the IWBBIO international work-conference on bioinformatics and biomedical engineering, 18–20 Mar. Copicentro Editorial, Granada, Spain, pp. 99–106
Nazlibilek S, Karacor D, Ercan T, Sazli MH, Kalender O, Ege Y (2014) ‘Automatic segmentation, counting, size determination and classification of white blood cells’, Measurement 55:58–65
Yan Li,Rui Zhu,Lei Mi,1 Yihui Cao, and Di Yao (2016) ‘Segmentation of White Blood Cell from Acute Lymphoblastic Leukemia Images Using Dual-Threshold Method’ Hindawi publishing corporation, Computational and Mathematical Methods in Medicine 2016, Article ID 9514707, 12 pages http://dx.doi.org/10.1155/2016/9514707
Olaf Ronneberger, Philipp Fischer, Thomas Brox (2015) ‘U-Net: Convolutional Networks for Biomedical Image SegmentationMedical Image Computing and Computer-Assisted Intervention’ (MICCAI), Springer, LNCS, Vol.9351: pp234-241 2015, available at arXiv:1505.04597 [cs.CV]
Xin Zhenga , Yong Wangb , Guoyou Wangc , Jianguo Liuc(2018) ‘Fast and robust segmentation of white blood cell images by self-supervised learning’ Journal Micron 107 pp55–71 Elsevier.
Sara Colantonio Igor Gurevich , and Ovidio Salvetti P. Perner and O.Salvetti (2007) ‘Automatic Fuzzy-neural Based Segmentation of Microscopic Cell Images’ (Eds.): MDA 2006/2007, LNAI 4826, pp. 115–127, 2007. © Springer-Verlag Berlin Heidelberg 2007
Liqun Lin , Weixing Wang and Bolin Chen (2018) ‘Leukocyte recognition with convolutional neural network’ Journal of Algorithms & Computational Technology Volume 13: pp1–8 SAGA
https://homes.di.unimi.it/scotti/all/#datasets(Cons: 2018).
Li, Y, Aece GR, Bacca J (2006), ‘Weighted median filters for multichannel signals’, IEEE Trans Signal Process 54, pp 4271-4281.
G.Padmavathi et al. (2009) ‘Performance analysis of Non Linear Filtering Algorithms for underwater images’ International Journal of Computer Science and Information Security, Vol.6, No. 2, 2009.
Benjamin et al. (2017) ‘Efficient Novel Vector Median Filter Design for impulse noise suppression in Color images’ International Journal of Innovative Computing, Information and Control 2017 Volume 13, pp. 1965–1980.
Soltane, S. (1999) ‘Adaptive Approach to Image Segmentation based on Local Image Indices’, Thesis Book LAMIH Laboratory, University of Valenciennes UVHC, January 1999 Valenciennes France.
Talegaonkar, N.R. Shinde, P.N. Shelke, S.J. Vaidya, S.D. Barathe, A.G. Wankhade, N. and Kulkarni, S. (2014) ‘An approach to automatic brain tumor detection in magnetic resonance Images’, In proceeding of IRF International Conference, pp.17-19.
Bouzid-Daho, A. and Boughazi, M. (2018) ‘Segmentation of Abnormal Blood Cells for Biomedical Diagnostic Aid’, In International Journal of Image, Graphics and Signal Processing, Vol. 10, N°.1, pp.30-35.
Neelima et al. (2017) ‘An Integrated Framework to Image Retrieval Using L*a*b Color Space ans Local Binary Pattern’ Image Processing and Pattern Recognition RTIP2R 2016 Vol 709, Springer pp 270-276.
Selvaraj, S. and Kanakaraj, B.R. (2014) ‘K-Means Clustering Based Segmentation of Lymphocytic Nuclei for Acute Lymphocytic Leukemia Detection’, In International Journal of Applied Engineering Research, Vol. 9, N°.21, pp.11423-11432.
Bouzid-Daho, A. Boughazi, M. and Petit, E. (2019) ‘Detection of abnormal blood cells by segmentation and classification’, In International Journal of Medical Engineering and Informatics, Vol. 11, N°. 1, pp. 57-70.
Dhendra Marutho; Sunarna Hendra Handaka; Ekaprana Wijaya; Muljono, (2018) ‘The Determination of Cluster Number at k-Mean Using Elbow Method and Purity Evaluation on Headline News’,IEEE International Seminar on Application for Technology of Information and Communication, 21-22 Sept. 2018 Indonesia.
Araujo, V. G., & Accioly, P. (2020). Use of Remote Sensing in Estimating Density of Forest Settlements / Uso do Sensoriamento Remoto na Estimativa da Densidade de Povoamentos Florestais. Brazilian Journal of Technology, 2(3), 758–783. Retrieved from https://ojs.brazilianjournals.com.br/ojs/index.php/BJT/article/view/6701
Hack, G. L. B., & Carvalho, R. I. N. de. (2019). Life-Saving System in Grain Silos / Sistema Salva Vidas em Silos Graneleiros. Brazilian Journal of Technology, 2(1), 523–534. Retrieved from https://ojs.brazilianjournals.com.br/ojs/index.php/BJT/article/view/2177
