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020 _a9783319476292
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024 7 _a10.1007/978-3-319-47629-2
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050 4 _aTK5102.9
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082 0 4 _a621.382
_223
100 1 _aXu, Xiang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_959534
245 1 0 _aCellular Image Classification
_h[electronic resource] /
_cby Xiang Xu, Xingkun Wu, Feng Lin.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aIX, 137 p. 60 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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505 0 _aIntroduction -- Fundamentals -- Optical Systems for Cellular Imaging -- Image Representation with Bag-of-Words -- Image Coding -- Encoding Image Features -- Defining Feature Space for Image Classification -- Conclusions and Perspectives.
520 _aThis book introduces new techniques for cellular image feature extraction, pattern recognition and classification. The authors use the antinuclear antibodies (ANAs) in patient serum as the subjects and the Indirect Immunofluorescence (IIF) technique as the imaging protocol to illustrate the applications of the described methods. Throughout the book, the authors provide evaluations for the proposed methods on two publicly available human epithelial (HEp-2) cell datasets: ICPR2012 dataset from the ICPR'12 HEp-2 cell classification contest and ICIP2013 training dataset from the ICIP'13 Competition on cells classification by fluorescent image analysis. First, the reading of imaging results is significantly influenced by one’s qualification and reading systems, causing high intra- and inter-laboratory variance. The authors present a low-order LP21 fiber mode for optical single cell manipulation and imaging staining patterns of HEp-2 cells. A focused four-lobed mode distribution is stable and effective in optical tweezer applications, including selective cell pick-up, pairing, grouping or separation, as well as rotation of cell dimers and clusters. Both translational dragging force and rotational torque in the experiments are in good accordance with the theoretical model. With a simple all-fiber configuration, and low peak irradiation to targeted cells, instrumentation of this optical chuck technology will provide a powerful tool in the ANA-IIF laboratories. Chapters focus on the optical, mechanical and computing systems for the clinical trials. Computer programs for GUI and control of the optical tweezers are also discussed. to more discriminative local distance vector by searching for local neighbors of the local feature in the class-specific manifolds. Encoding and pooling the local distance vectors leads to salient image representation. Combined with the traditional coding methods, this method achieves higher classification accuracy. Then, a rotation invariant textural feature of Pairwise Local Ternary Patterns with Spatial Rotation Invariant (PLTP-SRI) is examined. It is invariant to image rotations, meanwhile it is robust to noise and weak illumination. By adding spatial pyramid structure, this method captures spatial layout information. While the proposed PLTP-SRI feature extracts local feature, the BoW framework builds a global image representation. It is reasonable to combine them together to achieve impressive classification performance, as the combined feature takes the advantages of the two kinds of features in different aspects. Finally, the authors design a Co-occurrence Differential Texton (CoDT) feature to represent the local image patches of HEp-2 cells. The CoDT feature reduces the information loss by ignoring the quantization while it utilizes the spatial relations among the differential micro-texton feature. Thus it can increase the discriminative power. A generative model adaptively characterizes the CoDT feature space of the training data. Furthermore, exploiting a discriminant representation allows for HEp-2 cell images based on the adaptive partitioned feature space. Therefore, the resulting representation is adapted to the classification task. By cooperating with linear Support Vector Machine (SVM) classifier, this framework can exploit the advantages of both generative and discriminative approaches for cellular image classification. The book is written for those researchers who would like to develop their own programs, and the working MatLab codes are included for all the important algorithms presented. It can also be used as a reference book for graduate students and senior undergraduates in the area of biomedical imaging, image feature extraction, pattern recognition and classification. Academics, researchers, and professional will find this to be an exceptional resource.
650 0 _aSignal processing.
_94052
650 0 _aPattern recognition systems.
_93953
650 0 _aBiomathematics.
_95084
650 1 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aAutomated Pattern Recognition.
_931568
650 2 4 _aMathematical and Computational Biology.
_932033
700 1 _aWu, Xingkun.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_959535
700 1 _aLin, Feng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_959536
710 2 _aSpringerLink (Online service)
_959537
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319476285
776 0 8 _iPrinted edition:
_z9783319476308
776 0 8 _iPrinted edition:
_z9783319837864
856 4 0 _uhttps://doi.org/10.1007/978-3-319-47629-2
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
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