Tensor classification of N-point correlation function features for histology tissue segmentation

Mosaliganti, Kishore; Janoos, Firdaus; İrfanoğlu, M. Okan; Ridgway, Randall; Machiraju, Raghu; Huang, Kun; Saltz, Joel; Leone, Gustavo; Ostrowski, Michael C.

doi:10.1016/j.media.2008.06.020

Cited by 24 publications

(22 citation statements)

References 30 publications

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“…They are used to form threedimensional models of microstructure [13] and to characterize that microstructure and relate it to macroscopic properties such as the diffusion coefficient, fluid permeability, and elastic modulus [31,30]. The n-point correlations have also been used to create feature sets for medical image segmentation and classification [22,19,2].…”

Section: Introductionmentioning

confidence: 99%

Fast algorithms for comprehensive n-point correlation estimates

March

Connolly

Gray

2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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The n-point correlation functions (npcf) are powerful spatial statistics capable of fully characterizing any set of multidimensional points. These functions are critical in key data analyses in astronomy and materials science, among other fields, for example to test whether two point sets come from the same distribution and to validate physical models and theories. For example, the npcf has been used to study the phenomenon of dark energy, considered one of the major breakthroughs in recent scientific discoveries. Unfortunately, directly estimating the continuous npcf at a single value requires O(N n ) time for N points, and n may be 2, 3, 4 or even higher, depending on the sensitivity required. In order to draw useful conclusions about real scientific problems, we must repeat this expensive computation both for many different scales in order to derive a smooth estimate and over many different subsamples of our data in order to bound the variance.We present the first comprehensive approach to the entire n-point correlation function estimation problem, including fast algorithms for the computation at multiple scales and for many subsamples. We extend the current state-of-the-art tree-based approach with these two algorithms. We show an order-of-magnitude speedup over the current best approach with each of our new algorithms and show that they can be used together to obtain over 500x speedups over the stateof-the-art in order to enable much larger datasets and more accurate scientific analyses than were possible previously.

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Section: Introductionmentioning

confidence: 99%

Fast algorithms for comprehensive n-point correlation estimates

March

Connolly

Gray

2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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show abstract

“…The npoint correlation functions are also used in materials science to form three-dimensional models of microstructure [13] and to characterize that microstructure and relate it to macroscopic properties such as the diffusion coefficient, fluid permeability, and elastic modulus [36,37]. The n-point correlations have also been used for medical image processing, clustering, and classification [3,20,27].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the computation of n-point correlations on large-scale astronomical data

March

Czechowski

Dukhan

et al. 2012

2012 International Conference for High Performance Computing, Networking, Storage and Analysis

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Abstract-The n-point correlation functions (npcf) are powerful statistics that are widely used for data analyses in astronomy and other fields. These statistics have played a crucial role in fundamental physical breakthroughs, including the discovery of dark energy. Unfortunately, directly computing the npcf at a single value requires O (N n ) time for N points and values of n of 2, 3, 4, or even larger. Astronomical data sets can contain billions of points, and the next generation of surveys will generate terabytes of data per night. To meet these computational demands, we present a highly-tuned npcf computation code that show an order-of-magnitude speedup over current state-of-theart. This enables a much larger 3-point correlation computation on the galaxy distribution than was previously possible. We show a detailed performance evaluation on many different architectures.

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“…Processing of color imagery for feature and edge extraction [3] underlies several image processing algorithms for pattern recognition in industrial and scientific applications, computer vision systems and image coding methods. Color medical images may be enhanced or segmented using such techniques as vector filtering and tensor analysis [4], [5]. Colors are perceived as combinations of the three primary colors, red (R), green (G) and blue (B).…”

Section: Introductionmentioning

confidence: 99%

A Window-Based Self-Organizing Feature Map (SOFM) for Vector Filtering Segmentation of Color Medical Imagery

Stephanakis

Anastassopoulos

Iliadis

2011

Engineering Applications of Neural Networks

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Abstract. Color image processing systems are used for a variety of purposes including medical imaging. Basic image processing algorithms for enhancement, restoration, segmentation and classification are modified since color is represented as a vector instead of a scalar gray level variable. Color images are regarded as two-dimensional (2-D) vector fields defined on some color space (like for example the RGB space). In bibliography, operators utilizing several distance and similarity measures are adopted in order to quantify the common content of multidimensional color vectors. SelfOrganizing Feature Maps (SOFMs) are extensively used for dimensionality reduction and rendering of inherent data structures. The proposed windowbased SOFM uses as multidimensional inputs color vectors defined upon spatial windows in order to capture the correlation between color vectors in adjacent pixels. A 3x3 window is used for capturing color components in uniform color space (L * u * v * ). The neuron featuring the smallest distance is activated during training. Neighboring nodes of the SOFM are clustered according to their statistical similarity (using the Mahalanobis distance). Segmentation results suggest that clustered nodes represent populations of pixels in rather compact segments of the images featuring similar texture.

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Tensor classification of N-point correlation function features for histology tissue segmentation

Cited by 24 publications

References 30 publications

Fast algorithms for comprehensive n-point correlation estimates

Fast algorithms for comprehensive n-point correlation estimates

Optimizing the computation of n-point correlations on large-scale astronomical data

A Window-Based Self-Organizing Feature Map (SOFM) for Vector Filtering Segmentation of Color Medical Imagery

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