Image Segmentation Methods for Identifying Submerged Particles of Low Contrast Images

AUTHOR(S):

Mayar A. A. Shafaey, Mohammed A.-M. Salem, Doaa A.-K. Hegazy, Mohamed I. Roushdy

TITLE

Image Segmentation Methods for Identifying Submerged Particles of Low Contrast Images

ABSTRACT

The lack of automated concepts is considered as a major reason for barriers for investments into biotechnological processes that serve bulk chemicals and can provide a substantial part towards a more sustainable economy. Hence, this paper presents a method for enhancing the automation of submerged microalgae particles recognition, counting, and classifying using a microscopic device for in-situ imaging. The proposed method includes image de-noising using Anisotropic Diffusion technique; image normalization by Contrast Limited Histogram Equalization (CLAHE) method, image enhancement by morphological operations, a region of interest (ROI) extraction, and image segmentation. Furthermore, the ROI are classified into biological algae cells and others (i.e. grain stones) based on ROI’s size and texture. This method is applied on different datasets as synthetic and real microscopic images of microalgae. The experimental results proved that the microalgae particles can be quantified and classified correctly with accuracy reaches up to 99% and 100% for the segmentation and classification processes, respectively, compared to reference values.

KEYWORDS

Active Contour, Anisotropic Diffusion, CLAHE, Fuzzy C-mean, Watershed Transform, Microalgae Particles, Biomedical Imaging

REFERENCES

[1] JP. Wynn, P. Behrens, A. Sundararajan, J. Hansen, and K. Apt, Production of Single Cell Oils by Dinoflagellates. In: Single Cell Oils, AOCS, 2005.

[2] ES. Martin, Docosahexaenoic acid production by the marine alga crypthecodinium cohnii. Applied microbiology and biotechnology, Netherlands: Delft University Press, 2003.

[3] U. Michal, E. Malte, and S. Eberhard, A Crypthecodinium cohnii-like dinoflagellate feeding myzocytotically on the unicellular red alga porphyridium sp, European Journal of Phycology, Vol. 32, No. 2, 1997, pp. 133– 140.

[4] D. Prabowo, O. Hiraishi, and S. Suda, Diversity of crypthecodinium spp (dinophyceae) from okinawa prefecture, Marine Science and Technology, Vol. 21, 2013, pp. 181– 191. Mayar A. A. Shafaey et al.

[5] Bazila, and Dr. AH. Mir, Segmentation of Microscopic Images of Living Cells: A Study, International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 7, No. 2, 2014, pp. 327–338.

[6] C. Bittner, G. Wehnert, T. Scheper, In situ microscopy for on-line determination, of biomass, Biotechnology and Bioengineering, Vol. 60, No. 1, 1998, pp. 24–25.

[7] B. Plasier, D. Lloyd, GC. Paul, CR. Thomas, M. Al-Rubeai, Automatic image analysis for quantification of apoptosis in animal cell culture by annexin-V affinity assay, Journal of Immunological Methods, Vol. 229, No. 1, 1999, pp. 81–95.

[8] F. Wartelle, P. Dhulster, JS. Guez, JPh. Cassar, and H. Suhr, Real time in situ microscopy for animal cell-concentration monitoring during high density culture in bioreactor. Journal of Biotechnology, Vol. 111, No. 3, 2004, pp. 335–343.

[9] S. Inta, K. Ralf, and M. Gerald, Automated image analysis as a control tool for multiple emulsions, Food Hydrocolloids, Vol. 19, No. 3, 2005, pp. 617 – 624.

[10] G. Enrique, CL. Patricia, B. Teresa, MC. Soledad, T. Blanca, V. Leticia, and C. Gabriel, Development of advanced image analysis techniques for the in situ characterization of multiphase dispersions occurring in bioreactors, Journal of Biotechnology, Vol. 116, No. 3, 2005, pp. 261– 270.

[11] GRC. Garofano, CG. Venancio, CAT. Suazo, and PIF. Almeida, Application of the wavelet image analysis technique to monitor cell concentration in bioprocesses, Brazilian Journal of Chemical Engineering, Vol. 22, No. 4, 2005, pp. 573 – 583.

[12] JC. Eun, YO. Jung, YC. Hyun, WY. Jong, Production of exopolysaccharides by submerged mycelial culture of a mushroom Tremella fuciformis, Journal of Biotechnology, Vol. 127, No. 1, 2006, pp. 129–140.

[13] B. Presles, J. Debayle, A. Rivoire, J. Pinoli, G. Fevotte, In situ particle size measurements during crystallization processes using image analysis, In: XII Congrès de la Société Française de Génie des Procédés Pour relever les défis industriels du XXI siècle A la croisée des Sciences et des Cultures, France: Societe Fancaise de Genie des Procedes, 2009.

[14] Mohammed A-Megeed Salem, Medical Image Segmentation: Multiresolution- based Algorithms, VDM Verlag Dr. Müller, ISBN 978-3-639-38067-5 2011.

[15] R. Motiur, R. Shohel, I. Aminul, R. Masudur, HT. Mehedi, A New Filtering Technique for denoising Speckle Noise from Medical Images Based on Adaptive and Anisotropic Diffusion Filter, International Journal of Research in Computer and Communication Technology, Vol. 2, No. 9, 2013, pp. 689-693.

[16] HD. Cheng, and XJ. Shi, A simple and effective histogram equalization approach to image enhancement, Digital Signal Processing, Vol. 14, No. 2, 2004, pp. 158-170.

[17] E. Shoaib, FC. Adrian, R. Naveed, and DM. Klaus, Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems, Sensors, Vol. 15, No. 7, 2015, pp. 16804-16830.

[18] Mohammed A.-M. Salem, Alaa Atef, Alaa Salah, Marwa Shams, Recent Survey in Medical Image Segmentation, Handbook of Research on Machine Learning Innovations and Trends, IGI Global, Chapter 19, Volum I, pp 424-464, 2017, ISBN: 9781522522300.

[19] GK. Hassana, and B. Zou, Region-Based Segmentation versus Edge Detection, In: Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009, pp. 1217-1221.

[20] PL. Cheolha, Robust Image Segmentation using Active Contours: Level Set Approaches, North Carolina State University, 2005.

[21] HAJ. Farah, I. Waidah, A. Rosalina, H. Rosline, Image Segmentation Using A Hybrid Clustering Technique and Mean Shift for Automated Detection Acute Leukaemia Blood Cells Images, Journal of Theoretical and Applied Information Technology, Vol.76, No.1, 2015, pp. 89-96.

[22] M. Andrzej, and S. Michal, Texture Analysis Methods - A Review, Technical University of Lodz, Institute of Electronics, COST B11 report, Brussels, 1998, pp. 1-33.

[23] AA. Mogeeb, M. Hayat, M. Sorraya, M. Pozi, S. Aishah, A preliminary study on automated freshwater algae recognition and classification system, BMC Bioinformatics, Vol. 13, No. 17, 2012, pp. 1-13.

[24] S. Natchimuthu, P. Chinnaraj, S. Parthasarathy, K. Senthil, Automatic Identification of Algal Community from Microscopic Images, Bioinformatics and Biology Insights, Vol. 7, 2013, pp. 327-334.

[25] P. Sansoen, W. Pakaket, S. Nuttha, Automated Microalgae Image Classification, Procedia Computer Science, V. 29, 2014, pp.1981-1992.

Cite this paper

Mayar A. A. Shafaey, Mohammed A.-M. Salem, Doaa A.-K. Hegazy, Mohamed I. Roushdy. (2017) Image Segmentation Methods for Identifying Submerged Particles of Low Contrast Images. International Journal of Signal Processing, 2, 115-120