DOI: https://doi.org/10.26641/1997-9665.2018.3.164-171

Система для напівавтоматичного класифікації тканин з використанням оптичного дифрактометра для аналізу біополімерних структур.

A. A. Skrynnik, V. A. Oganessian, A. G. Jablokov, O. V. Gradov

Анотація


У роботі описується інструментальний стенд для напівавтоматичної класифікації тканин, заснований на конструкції, описаній Б. Вайнштейном у статті по тривимірній електронної мікроскопії біополімерних структур. Фазографічне голографічне і кореляційно-спектральне вимірювання на даному стенді можуть бути розширеними джерелами комплементарних ідентифікаційних дескрипторів. Впровадження лазерного об'єктива-коліматора відрізняє вхідна ланка даної установки від класичної версії Вайнштейна. Метод проекційних трансформант також може бути реалізований (з деякими доповненнями і модифікаціями в тому числі) на даній установці.


Ключові слова


ідентифікація тканин; класифікація тканин; регулярність структури гістологічних зразків; періодичність гістологічних зразків; голографічний аналіз гістологічних зразків; проекційні трансформанти; лазерна біофізика

Повний текст:

PDF (Русский)

Посилання


Kothari S, Phan JH, Young AN, Wang MD. Histological image classification using biologically interpretable shape-based features. BMC Med Imaging. 2013;13:9. doi:10.1186/1471-2342-13-9.

Azar JC, Busch C, Carlbom IB. Histological stain evaluation for machine learning applications. J Pathol Inform. 2013;4:11. doi:10.4103/2153-3539.109869.

Qi X, Xing F, Foran DJ, Yang L. A fast, automatic segmentation algorithm for locating and delineating touching cell boundaries in imaged histopathology. Methods Inf Med. 2012;51(3):260-7

Díaz G, Romero E. Microstructural tissue analysis for automatic histopathological image annotation. Microsc Res Tech. 2012;75(3):343-58.

Lang M, Ermert H, Heuser L. In vivo study of online liver tissue classification based on envelope power spectrum analysis. Ultrason Imaging. 1994;16(2):77-86.

Riaz F, Silva FB, Ribeiro MD, Coimbra MT. Invariant Gabor texture descriptors for classification of gastroenterology images. IEEE Trans Biomed Eng. 2012;59(10):2893-904.

Bauer S, Nolte LP, Reyes M. Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. Med Image Comput Comput Assist Interv. 2011;14(3):354-61.

Tohka J, Dinov ID, Shattuck DW, Toga AW. Brain MRI tissue classification based on local Markov random fields. Magn Reson Imaging. 2010;28(4):557-73.

Huang PW, Lee CH. Automatic classification for pathological prostate images based on fractal analysis. IEEE Trans Med Imaging. 2009;28(7):1037-50.

Vaynshteyn BK. [Three-dimensional electron microscopy of biological macromolecules]. Uspekhi fizicheskikh nauk. 1973;109(3):455-97. Russian.

Grudinin BN, Kislenok EG, Plotnikov EV, Fischenko VK. [Analysis, filtration and decomposition of microscopic images based on orthogonal transformations]. Avtometriya. 2007;43(1):24-37. Russian.

Gradov OV, Nasirov FA, Goncharova AA, Fishchenko VK, Yablokov AG. [The technologies of the lensless holographic trichoscopy and trichometry on the chip are microinterference, 2D Fourier spectral (integral frequency and spatial) and correlational techniques in clinical trichology]. Morphologia. 2018;12(2):7-21. Russian.

Lendaris GG, Stanley GL. Diffraction pattern sampling for pattern recognition. Proceedings of IEEE. 1970;58(2):198-216.

Valdés Hernández Mdel C, Gallacher PJ, Bastin ME, Royle NA, Maniega SM, Deary IJ, Wardlaw JM. Automatic segmentation of brain white matter and white matter lesions in normal aging: comparison of five multispectral techniques. Magn Reson Imaging. 2012;30(2):222-9.

Valentinitsch A, Karampinos DC, Alizai H, Subburaj K, Kumar D, Link TM, Majumdar S. Au-tomated unsupervised multiparametric classification of adipose tissue depots in skeletal muscle. J Magn Reson Imaging. 2013;37(4):917-27.

Vomweg TW, Teifke A, Kauczor HU, Achenbach T, Rieker O, Schreiber WG, Heitmann KR, Beier T, Thelen M. [Self-organizing neural networks for automatic detection and classification of contrast (media) enhancement of lesions in dynamic MR-mammography]. Rofo. 2005;177(5):703-13. German.

Awate SP, Tasdizen T, Foster N, Whitaker RT. Adaptive Markov modeling for mutual-information-based, unsupervised MRI brain-tissue classification. Med Image Anal. 2006;10(5):726-39.

Pitiot A, Totman J, Gowland P. Null point imaging: a joint acquisition/analysis paradigm for MR classification. Med Image Comput Comput Assist Interv. 2007;10(1):759-66.

Folkesson J, Samset E, Kwong RY, Westin CF. Unifying statistical classification and geodesic active regions for segmentation of cardiac MRI. IEEE Trans Inf Technol Biomed. 2008;12(3):328-34.

Li C, Xu C, Anderson AW, Gore JC. MRI tissue classification and bias field estimation based on coherent local intensity clustering: a uni-fied energy minimization framework. Inf Process Med Imaging. 2009;21:288-99.

Tidwell VK, Kim JH, Song SK, Nehorai A. Automatic segmentation of rodent spinal cord diffusion MR images. Magn Reson Med. 2010;64(3):893-901.

Weizman L, Ben-Sira L, Joskowicz L, Pre-cel R, Constantini S, Ben-Bashat D. Automatic segmentation and components classification of optic pathway gliomas in MRI. Med Image Comput Comput Assist Interv. 2010;13(1):103-10.

Weizman L, Ben Sira L, Joskowicz L, Constantini S, Precel R, Shofty B, Ben Bashat D. Automatic segmentation, internal classification, and follow-up of optic pathway gliomas in MRI. Med Image Anal. 2012;16(1):177-88.

Li C, Xu C, Anderson AW, Gore JC. MRI tissue classification and bias field estimation based on coherent local intensity clustering: a unified energy minimization framework. Inf Process Med Imaging. 2009;21:288-99.

Liberman G, Louzoun Y, Aizenstein O, Blumenthal DT, Bokstein F, Palmon M, Corn BW, Ben Bashat D. Automatic multimodal MR tissue classification for the assessment of response to bevacizumab in patients with glioblastoma. Eur J Radiol. 2013;82(2):87-94.

Folkesson J, Samset E, Kwong RY, Westin CF. Unifying statistical classification and geodesic active regions for segmentation of cardiac MRI. IEEE Trans Inf Technol Biomed. 2008;12(3):328-34.

Anbeek P, Išgum I, van Kooij BJ, Mol CP, Kersbergen KJ, Groenendaal F, Viergever MA, de Vries LS, Benders MJ. Automatic segmentation of eight tissue classes in neonatal brain MRI. PLoS One. 2013;8(12):81895. doi:10.1371/journal.pone.0081895.

Nachimuthu DS, Baladhandapani A. Multi-dimensional texture characterization: on analysis for brain tumor tissues using MRS and MRI. J Digit Imaging. 2014;27(4):496-506.

Chaudhry A, Hassan M, Khan A, Kim JY. Automatic active contour-based segmentation and classification of carotid artery ultrasound images. J Digit Imaging. 2013;26(6):1071-81.

Moon WK, Chang SC, Chang JM, Cho N, Huang CS, Kuo JW, Chang RF. Classification of breast tumors using elastographic and B-mode features: comparison of automatic selection of representative slice and physician-selected slice of images. Ultrasound Med Biol. 2013;39(7):1147-57.

Yang MC, Huang CS, Chen JH, Chang RF. Whole breast lesion detection using naive bayes classifier for portable ultrasound. Ultrasound Med Biol. 2012;38(11):1870-80.

Ungru K, Tenbrinck D, Jiang X, Stypmann J. Automatic classification of left ventricular wall segments in small animal ultrasound imaging. Comput Methods Programs Biomed. 2014;117(1):2-12. doi:10.1016/j.cmpb.2014.06.015.

Massoptier L, Casciaro S. A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans. Eur Radiol. 2008;18(8):1658-65. doi:10.1007/s00330-008-0924-y.

Sofka M, Wu D, Sühling M, Liu D, Tietjen C, Soza G, Zhou SK. Automatic contrast phase es-timation in CT volumes. Med Image Comput Comput Assist Interv. 2011;14(3):166-74.

Weßling J, Puesken M, Koch R, Kohlhase N, Persigehl T, Mesters R, Heindel W, Buerke B. MSCT follow-up in malignant lymphoma: comparison of manual linear measurements with semi-automated lymph node analysis for therapy response classification. Rofo. 2012;184(9):795-804.

Kakar M, Mencattini A, Salmeri M. Extracting fuzzy classification rules from texture segmented HRCT lung images. J Digit Imaging. 2013;26(2):227-38.

Freiman M, Cooper O, Lischinski D, Joskowicz L. Liver tumors segmentation from CTA images using voxels classification and affinity constraint propagation. Int J Comput Assist Radiol Surg. 2011;6(2):247-55.

Duy NT, Lamecker H, Kainmueller D, Zachow S. Automatic detection and classification of teeth in CT data. Med Image Comput Comput Assist Interv. 2012;15(1):609-16.

Bueno G, Vállez N, Déniz O, Esteve P, Rienda MA, Arias M, Pastor C. Automatic breast parenchymal density classification integrated into a CADe system. Int J Comput Assist Radiol Surg. 2011;6(3):309-18

Bielecki C, Bocklitz TW, Schmitt M, Krafft C, Marquardt C, Gharbi A, Knösel T, Stallmach A, Popp J. Classification of inflammatory bowel diseases by means of Raman spectroscopic imaging of epithelium cells. J Biomed Opt. 2012;17(7):076030.

Gradov OV, Yablokov AG, Skrynnik AA. [Periodimeter-regulator for kinetic analysis of symbolic registers of correlation stroboscopic electron microscopy and microprobe mapping data]. Fundamental'nyye problemy radioelektronnogo priborostroyeniya. 2017;17(3):769-72. Russian.

Gradov OV, Notchenko AV, Oganessian VA. The neurogoniometry: applied optical analysis for neural structure directogramm / radiation pattern measurements. Optics. 2015;4(6):37-42.

Handels H. Automatic 3D segmentation and characterization of brain tissues in multiparametric MR image sequences. Medinfo. 1995;8(1):696-700.

Luck BL, Carlson KD, Bovik AC, Richards-Kortum RR. An image model and segmentation algorithm for reflectance confocal images of in vivo cervical tissue. IEEE Trans Image Process. 2005;14(9):1265-76.

Thiran JP, Macq B. Morphological feature extraction for the classification of digital images of cancerous tissues. IEEE Trans Biomed Eng. 1996;43(10):1011-20.

Coimbra M, Riaz F, Areia M, Baldaque Silva F, Dinis-Ribeiro M. Segmentation for classification of gastroenterology images. Conf Proc IEEE Eng Med Biol Soc. 2010;2010:4744-7.

Bibicu D, Moraru L, Biswas A. Thyroid nodule recognition based on feature selection and pixel classification methods. J Digit Imaging. 2013;26(1):119-28.

Kendall EJ, Barnett MG, Chytyk-Praznik K. Automatic detection of anomalies in screening mammograms. BMC Med Imaging. 2013;13:43.

Cheng CC, Hsieh TY, Taur JS, Chen YF. An automatic segmentation and classification framework for anti-nuclear antibody images. Biomed Eng Online. 2013;12(1):5. doi:10.1186/1475-925X-12-S1-S5.




Morphologia