A novel approach for detection and delineation of cell nuclei using feature similarity index measure

• Autorzy: John J. , Nair M. S. , Kumar P. R. A. , Wilscy M.

Identyfikatory

DOI

10.1016/j.bbe.2015.11.002

• Języki publikacji: EN

Abstrakty

EN

Accurate image segmentation of cells and tissues is a challenging research area due to its vast applications in medical diagnosis. Seed detection is the basic and most essential step for the automated segmentation of microscopic images. This paper presents a robust, accurate and novel method for detecting cell nuclei which can be efficiently used for cell segmentation. We propose a template matching method using a feature similarity index measure (FSIM) for detecting nuclei positions in the image which can be further used as seeds for segmentation tasks. Initially, a Fuzzy C-Means clustering algorithm is applied on the image for separating the foreground region containing the individual and clustered nuclei regions. FSIM based template matching approach is then used for nuclei detection. FSIM makes use of low level texture features for comparisons and hence gives good results. The performance of the proposed method is evaluated on the gold standard dataset containing 36 images (_8000 nuclei) of tissue samples and also in vitro cultured cell images of Stromal Fibroblasts (5 images) and Human Macrophage cell line (4 images) using the statistical measures of Precision and Recall. The results are analyzed and compared with other state-of-the-art methods in the literature and software tools to prove its efficiency. Precision is found to be comparable and the Recall rate is found to exceed 92% for the gold standard dataset which shows considerable performance improvement over existing methods.

Słowa kluczowe

EN

microscopic image; feature similarity index measure; macrophages; cell nuclei; seed detection; cell segmentation

PL

obraz mikroskopowy; wskaźnik podobieństwa; makrofagi; jądro komórkowe; segmentacja komórki

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2016
• Tom: Vol. 36, no. 1
• Strony: 76--88
• Opis fizyczny: Bibliogr. 37 poz., rys., tab.

Twórcy

autor

John J.

• Department of Computer Science, University of Kerala, Kariavattom, Thiruvananthapuram 695581, Kerala, India

autor

Nair M. S.

• Department of Computer Science, University of Kerala, Kariavattom, Thiruvananthapuram 695581, Kerala, India

autor

Kumar P. R. A.

• Tissue Culture Laboratory, Bio Medical Technology Wing, SCTIMST, Poojappura, Kerala, India

autor

Wilscy M.

• Department of Computer Science, University of Kerala, Kariavattom, Thiruvananthapuram 695581, Kerala, India

Bibliografia

• [1] Selvan, Shirley. Automatic seed point selection in ultrasound echography images of breast using texture features. Biocybern Biomed Eng 2014.
• [2] GeethaRamani R, Balasubramanian L. Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis. Retinal blood vessel segmentation in fundus images. Biocybern Biomed Eng 2015.
• [3] Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. Int J Comput Vis 1988;1(4):321–31.
• [4] Mumford D, Shah J. Optimal approximations by piecewise smooth functions and associated variational problems. Commun Pure Appl Math 1989;42(5):577–685.
• [5] Sethian JA. Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry Fluid Mechanics Computer Vision and Materials Science, vol. 3. Cambridge University Press; 1999.
• [6] Chan TF, Vese LA. Active contours without edges. IEEE Trans Image Process 2001;10(2):266–77.
• [7] Lin G, Adiga U, Olson K, Guzowski JF, Barnes CA, Roysam B. A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks. Cytometry A 2003;56(1): 23–36.
• [8] Korzynska A, Strojny W, Hoppe A, Wertheim D, Hoser P. Hybrid texture and contour based method in segmentation of microscope images of living cells. Pattern Anal Appl 2007;10(October (4)):301–19.
• [9] Korzynska A, Hoppe A, Strojny W, Wertheim D. Investigation of a combined texture and contour method for segmentation of light microscopy cell images. Presented at the 2nd International Conference of the International Association of Science and Technology for Development (IASTED) on Biomedical Engineering, BioMED; 2004.
• [10] Korzynska A, Roszkowiak L, Lopez C, Bosch R, Witkowski L, Lejeune M. Nuclei segmentation in with adaptive threshold for pathological examination of tissue section: validation of various adaptive threshold methods of segmentation applied to follicular lymphoma digital images stained with 3,30-Diaminobenzidine&Haematoxylin. Diagn Pathol 2013;8 (March (25)).
• [11] Yu W, Lee HK, Hariharan S, Bu W, Ahmed S. Level set segmentation of cellular images based on topological dependence. Advances in visual computing. Springer; 2008. p. 540–51.
• [12] Korzynska A, Iwanowski M. Texture and watershed method in multistage morphological segmentation of bright-field and fluorescent microscopy images. Opto-Electr Rev 2012;20(January (2)):174–86.
• [13] Iwanowski M, Korzynska A. Segmentation of moving cells in bright field and epi-fluorescent microscopic image sequences. Computer vision and graphics. Berlin, Heidelberg: Springer; 2010. p. 401–10.
• [14] Wählby C, SINTORN I-M, Erlandsson F, Borgefors G, Bengtsson E. Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections. J Microsc 2004;215(1):67–76.
• [15] Soille P. Morphological image analysis: principles and applications. Springer-Verlag New York, Inc.; 2003.
• [16] Ortiz de Solorzano C, Garcia Rodriguez E, Jones A, Pinkel D, Gray JW, Sudar D, et al. Segmentation of confocal microscope images of cell nuclei in thick tissue sections. J Microsc 1999;193(3):212–26.
• [17] Ortiz de Solorzano C, Malladi R, Lelievre SA, Lockett SJ. Segmentation of nuclei and cells using membrane related protein markers. J Microsc 2001;201(3):404–15.
• [18] Ballard DH. Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognit 1981;13(2):111–22.
• [19] Zimmer C, Olivo-Marin J-C. Coupled parametric active contours. IEEE Trans Pattern Anal Mach Intell 2005;27 (11):1838–42.
• [20] Plissiti ME, Nikou C. Overlapping cell nuclei segmentation using a spatially adaptive active physical model. IEEE Trans Image Process 2012;21(11):4568–80.
• [21] Plissiti ME, Nikou C, Charchanti A. Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images. Pattern Recognit Lett 2011;32(6): 838–53.
• [22] Qi X, Xing F, Foran DJ, Yang L. Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set. IEEE Trans Biomed Eng 2012;59(3):754–65.
• [23] Shitong W, Min W. A new detection algorithm (NDA) based on fuzzy cellular neural networks for white blood cell detection. IEEE Trans Inf Technol Biomed 2006;10(1): 5–10.
• [24] Naik S, Doyle S, Agner S, Madabhushi A, Feldman M, Tomaszewski J. Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology. 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. ISBI 2008. 2008. pp. 284–7.
• [25] Begelrnan G, Gur E, Rivlin E, Rudzsky M, Zalevsky Z. Cell nuclei segmentation using fuzzy logic engine. Image Processing, International Conference on ICIP'04, vol. 5. 2004. pp. 2937–40.
• [26] Ancin H, Roysam B, Dufresne TE, Chestnut MM, Ridder GM, Szarowski DH, et al. Advances in automated 3-D image analysis of cell populations imaged by confocal microscopy. Cytometry 1996;25(3):221–34.
• [27] Malpica N, Ortiz de Solorzano C, Vaquero JJ, Santos A, Vallcorba I, Garcia-Sagredo JM, et al. Applying watershed algorithms to the segmentation of clustered nuclei; 1997.
• [28] Zhang L, Zhang D, Mou X. FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 2011;20(8):2378–86.
• [29] Wienert S, Heim D, Saeger K, Stenzinger A, Beil M, Hufnagl P, et al. Detection and segmentation of cell nuclei in virtual microscopy images: a minimum-model approach. Sci Rep 2012;2.
• [30] Al-Kofahi Y, Lassoued W, Lee W, Roysam B. Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE Trans Biomed Eng 2010;57 (4):841–52.
• [31] Dunn JC. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybern 1973;3:32–57.
• [32] Glasbey CA. An analysis of histogram-based thresholding algorithms. CVGIP Graph Models Image Process 1993;55 (6):532–7.
• [33] Sauvola J, Pietikäinen M. Adaptive document image binarization. Pattern Recognit 2000;33(2):225–36.
• [34] Kachouie NN, Fieguth P, Ramunas J, Jervis E. Probabilistic model-based cell tracking. Int J Biomed Imaging 2006;2006.
• [35] Otsu N. A threshold selection method from gray-level histograms. Automatica 1975;11(285–296):23–7.
• [36] Al Kofahi Y, et al. Cell-based quantification of molecular biomarkers in histopathology specimens. Histopathology 2011;59(1):40–54.
• [37] Schneider CA, et al. 671 NIH image to imageJ: 25 years of image analysis. Nat Methods 2012;9(7).

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.