Cell image segmentation for diagnostic pathology

The colors associated with a digitized specimen representing peripheral blood smear are typically characterized by only a few, non-Gaussian clusters, whose shapes have to be discerned solely from the image being processed. Nonparametric methods such as mode-based analysis [952], are particularly suitable for the segmentation of this type of data since they do not constrain the cluster shapes. This chapter reviews an efficient cell segmentation algorithm that detects clusters in the L*u*v color space and delineates their borders by employing the gradient ascent mean shift procedure [950], [951]. The color space is randomly tessellated with search windows that are moved till convergence to the nearest mode of the underlying probability distribution. After the pruning of the mode candidates, the colors are classified using the basins of attraction. The segmented image is derived by mapping the color vectors in the image domain and enforcing spatial constraints.

[1]  Dorin Comaniciu,et al.  Image-guided decision support system for pathology , 1999, Machine Vision and Applications.

[2]  King-Sun Fu,et al.  Shape Discrimination Using Fourier Descriptors , 1977, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Alex Pentland,et al.  Probabilistic Visual Learning for Object Representation , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Dorin Comaniciu,et al.  Decision support system for multiuser remote microscopy in telepathology , 1999, Proceedings 12th IEEE Symposium on Computer-Based Medical Systems (Cat. No.99CB36365).

[5]  Fang Liu,et al.  Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Emanuele Trucco,et al.  Introductory techniques for 3-D computer vision , 1998 .

[7]  Fang Liu,et al.  Real-time recognition with the entire Brodatz texture database , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Larry D. Hostetler,et al.  The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.

[9]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[10]  Vladimir Pavlovic,et al.  Toward multimodal human-computer interface , 1998, Proc. IEEE.

[11]  B. S. Manjunath,et al.  NeTra: A toolbox for navigating large image databases , 1997, Multimedia Systems.

[12]  Charles R. Giardina,et al.  Elliptic Fourier features of a closed contour , 1982, Comput. Graph. Image Process..

[13]  James S. Duncan,et al.  Synthesis of Research: Medical Image Databases: A Content-based Retrieval Approach , 1997, J. Am. Medical Informatics Assoc..

[14]  P. Meer,et al.  Retrieval performance improvement through low rank corrections , 1999, Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries (CBAIVL'99).

[15]  Robert Sedgewick,et al.  Algorithms in C , 1990 .

[16]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  S. Pileri,et al.  Mantle Cell Lymphoma , 2022, The EBMT/EHA CAR-T Cell Handbook.

[18]  G. Wyszecki,et al.  Color Science Concepts and Methods , 1982 .

[19]  Thomas S. Huang,et al.  A Modified Fourier Descriptor for Shape Matching in MARS , 1998, Image Databases and Multi-Media Search.

[20]  Dorin Comaniciu,et al.  Bimodal system for interactive indexing and retrieval of pathology images , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[21]  M. Mitschke,et al.  Eecient Query Reenement for Image Retrieval , 1998 .

[22]  Anil K. Jain,et al.  Texture classification and segmentation using multiresolution simultaneous autoregressive models , 1992, Pattern Recognit..

[23]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Dorin Comaniciu,et al.  Shape-based image indexing and retrieval for diagnostic pathology , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[25]  William H. Press,et al.  Numerical recipes in C , 2002 .

[26]  Thomas S. Huang,et al.  Supporting similarity queries in MARS , 1997, MULTIMEDIA '97.

[27]  H. Müller-Hermelink,et al.  Image analysis detects lineage-specific morphologic markers in leukemic blast cells. , 1996, American journal of clinical pathology.

[28]  Yanxi Liu,et al.  A classification based similarity metric for 3D image retrieval , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[29]  Bruce A. Draper,et al.  FOCUS: Searching for multi-colored objects in a diverse image database , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[30]  Dorin Comaniciu,et al.  Mean shift analysis and applications , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[31]  Matti Pietikäinen,et al.  An Experimental Comparison of Autoregressive and Fourier-Based Descriptors in 2D Shape Classification , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Kris Popat,et al.  Cluster-based probability model and its application to image and texture processing , 1997, IEEE Trans. Image Process..

[33]  Ramesh C. Jain,et al.  Pattern Recognition Methods in Image and Video Databases: Past, Present and Future , 1998, SSPR/SPR.

[34]  Peter Meer,et al.  Image Segmentation from Consensus Information , 1997, Comput. Vis. Image Underst..

[35]  Ingemar J. Cox,et al.  An optimized interaction strategy for Bayesian relevance feedback , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).