위상차 현미경 영상 내 푸리에 묘사자를 이용한 암세포 형태별 분류
Classification of Tumor cells in Phase-contrast Microscopy Image using Fourier Descriptor
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초록
Tumor cell morphology is closely related to its migratory behaviors. An active tumor cell has a highly irregular shape, whereas a spherical cell is inactive. Thus, quantitative analysis of cell features is crucial to determine tumor malignancy or to test the efficacy of anticancer treatment. We use 3D time-lapse phase-contrast microscopy to analyze single cell morphology because it enables to observe long-term activity of living cells without photobleaching and phototoxicity,which is common in other fluorescence-labeled microscopy. Despite this advantage, there are image-level drawbacks to phase-contrast microscopy, such as local light effect and contrast interference ring. Therefore, we first corrected for non-uniform illumination artifacts and then we use intensity distribution information to detect cell boundary. In phase contrast microscopy image, cell is normally appeared as dark region surrounded by bright halo ring. Due to halo artifact is minimal around the cell body and has non-symmetric diffusion pattern, we calculate cross sectional plane which intersects center of each cell and orthogonal to first principal axis. Then, we extract dark cell region by analyzing intensity profile curve considering local bright peak as halo area. Finally, we calculated the Fourier descriptor that morphological characteristics of cell to classify tumor cells into active and inactive groups. We validated classification accuracy by comparing our findings with manually obtained results.
Abstract
Tumor cell morphology is closely related to its migratory behaviors. An active tumor cell has a highly irregular shape, whereas a spherical cell is inactive. Thus, quantitative analysis of cell features is crucial to determine tumor malignancy or to test the efficacy of anticancer treatment. We use 3D time-lapse phase-contrast microscopy to analyze single cell morphology because it enables to observe long-term activity of living cells without photobleaching and phototoxicity,which is common in other fluorescence-labeled microscopy. Despite this advantage, there are image-level drawbacks to phase-contrast microscopy, such as local light effect and contrast interference ring. Therefore, we first corrected for non-uniform illumination artifacts and then we use intensity distribution information to detect cell boundary. In phase contrast microscopy image, cell is normally appeared as dark region surrounded by bright halo ring. Due to halo artifact is minimal around the cell body and has non-symmetric diffusion pattern, we calculate cross sectional plane which intersects center of each cell and orthogonal to first principal axis. Then, we extract dark cell region by analyzing intensity profile curve considering local bright peak as halo area. Finally, we calculated the Fourier descriptor that morphological characteristics of cell to classify tumor cells into active and inactive groups. We validated classification accuracy by comparing our findings with manually obtained results.
참고문헌 (24)
- S. Wadler, "Quantification of Ribonucleotide Reductase Expression in Wild-Type and Hydroxyurea-Resistant Cell Lines Employing in Situ Reverse Transcriptase Polymerase Chain Reaction and a Computerized Image Analysis System", Anal. Biochem, 267(1), pp.24~29 (1999)
- Y. Hamamoto, "On the behavior of artificial neural network classifiers in high-dimensional spaces", IEEE Trans. Pattern Anal. Mach. Intell, 18(5), pp.571~575 (1996)
- M.S. Kang, "Cell morphology classification in phase contrast microscopy image reducing halo artifact", in Proc. SPIE Photonics West (2012)
- M.S. Kang, "Quantitative tracking of tumor cells in phase contrast microscopy exploiting halo artifact pattern", in Proc. SPIE Medical Imaging (2012)
- F. Zernike, "Physica", pp.686~ (1942)
- K. Li, "Cell population tracking and lineage construction with spatiotemporal context", Med. Image Anal., 12(5), pp.546~566 (2008)
- I. Adanja, "Automated tracking of unmarked cells migrating in three-dimensional matrices applied to anti-cancer drug screening", Expl. Cell Res, 316, pp.181~193 (2010)
- J. Pan, "Learning to detect different types of cells under phase contrast microscopy", in Proc. Microscopic Image Analysis with Applications in Biology (MIAAB) (2009)
- M. Magg, "Tracking cells in phase contrast light microscopic images", Diploma Theses, ~ (2010)
- J. Orikawa, "Cell segmentation from phasecontrast images using hybrid watershed and region growing algorithm for genomic drug discovery", SICE Annual Conference (2010)
- S. Hadjidemetriou, "Detection and tracking of cell divisions in phase contrast video microscopy", in Proc. Microscopic Image Analysis with Applications in Biology (MIAAB) (2008)
- C. Decaestecker, "Can anti-migratory drugs be screened in vitro? A review of 2D and 3D assays for the quantitative analysis of cell migration", Med Res Rev, 27(2), pp.149~176 (2007)
- H. Lee, "Histogram based Correction of Non-uniform Illumination on Phase-Contrast Microscopy", in Proc. Workshop on Image Processing and Image Understanding (IPIU), pp.347~348 (2010)
- T. Arici, "A histoogram modificcation framework and its application for image contrast enhancement", IEEE Trans. Image Process, 18(9), pp.1921~1935 (2009)
- Z. Chen, "Gray-Level Grouping (GLG): An Automatic Method for Optimized Image Conrast Enhancement-Part I : The Basic Method", IEEE Tran. on Image Processing, 15(8), pp.2290~2302 (2006)
- L.I. Smith, "A tutorial on Principal Components Analysis" (2002)
- A tutorial on principal component analysis, "J. Shlens", ~ (2005)
- The Use of Discrete Data in PCA: Theory, Simulations, and Applications to Socioeconomic Indices, "S. Kolenikov", Carolina Population Center, University of North Carolina at Chapel Hill, ~ (2004)
- 최현주, "Grading of renal cell carcinoma by 3D morphological analysis of cell nuclei", COMPUTERS IN BIOLOGY AND MEDICINE, 37, pp.1334~1341 (2007), PERGAMON-ELSEVIER SCIENCE LTD
- Y. Watanabe, "A method for volume estimation by using vector areas and centroids of serial cross sections", IEEE Trans. on Bio-Medical Engineering, 29(3), pp.202~205 (1982)
- N.M Tahir, "Fourier Descriptor for Pedestrian Shape Recognition using Support Vector Machine", in Proc. 7th IEEE International Symposium on Signal Processing and Information (2007)
- M. Kazhdan, "An approximate and efficient method for optimal rotation alignment of 3D models", IEEE Trans. on Pattern Analysis and Machine Intelligence, 29(7), pp.1221~1229 (2007)
- V.N. Vapnik, "The Nature of Statistical Learning Theory" (1995), Springer
- "LIBSVM: a library for support vector machines"[URL]