Two-Step Iterative Registration for 2D-Gel Electrophoresis Images
0
Citation
19
Reference
10
Related Paper
Abstract:
2D-gel electrophoresis (2D-GE) is a central technique for separation of protein samples in proteomic research. A 2D-GE image usually contains hundreds of protein spots that should be detected by an image processing technique. Identifying the missing proteins by comparison of two samples such as from healthy person and patient can be helpful in developing medicine or disease control. This matching process is a laborious work that needs to be automatically done by computers with less user intervention as much as possible. In this paper, we propose a two-step iterative registration algorithm for protein spots in 2D-GE images. The algorithm estimates the geometrical differences between the corresponding landmarks and iteratively moves the spot points in the matched gel in order to be closer to its correspondence in the reference gel. The amount of movement of each spot is affected by the Euclidian distances from the spot to all landmarks. The single-step matching is defined with its pros and cons; then the motivation to a two-step iterative registration algorithm and its results are discussed.Keywords:
Image registration
Feature (linguistics)
Iterative closest point
Operator (biology)
Distance measures
Decision maker
Cite
Citations (135)
Feature (linguistics)
Euclidean domain
Cite
Citations (2)
The p-median model is used to locate P facilities to serve a geographically distributed population. Conventionally, it is assumed that the population patronize the nearest facility and that the distance between the resident and the facility may be measured by the Euclidean distance. Carling, Han, and Hakansson (2012) compared two network distances with the Euclidean in a rural region with a sparse, heterogeneous network and a non-symmetric distribution of the population. For a coarse network and P small, they found, in contrast to the literature, the Euclidean distance to be problematic. In this paper we extend their work by use of a refined network and study systematically the case when P is of varying size (2-100 facilities). We find that the network distance give as good a solution as the travel-time network. The Euclidean distance gives solutions some 2-7 per cent worse than the network distances, and the solutions deteriorate with increasing P. Our conclusions extend to intra-urban location problems.
Geographical distance
Distance measures
Small-world network
Cite
Citations (2)
Combining different distance matrices or dissimilarity representations can often increase the performance of individual ones. In this work, we experimentally study on the performance of combining Euclidean distances and its relationship with the non-Euclideaness produced from combining Euclidean distances. The relationship between the degree of non-Euclideaness from combining Euclidean distances and the correlations between these Euclidean distances are also investigated in the experiments. From the results, we observe that combining dissimilarities computed with Euclidean distances usually performs better than combining dissimilarities computed with squared Euclidean distances. Also, the improvements are found to be highly related to the degree of non-Euclideaness. Moreover, the degree of non-Euclideaness is relatively large if two highly uncorrelated dissimilarity matrices are combined and the degree of non-Euclideaness remains lower if two dissimilarity matrices to be combined are more correlated.
Degree (music)
Uncorrelated
Distance matrix
Cite
Citations (7)
We give examples of Generalized Euclidean but not norm-Euclidean number fields of degree greater than 2. In the same way we give examples of 2-stage Euclidean but not norm-Euclidean number fields of degree greater than 2. In both cases, no such examples were known.
Euclidean domain
Euclidean group
Cite
Citations (0)
Abstract In this paper, we present the fuzzy‐induced Euclidean ordered weighted averaging distance (FIEOWAD) operator. It is an extension of the ordered weighted averaging (OWA) operator that uses the main characteristics of the induced OWA (IOWA), the Euclidean distance and uncertain information represented by fuzzy numbers. The main advantage of this operator is that it is able to consider complex attitudinal characters of the decision maker by using order‐inducing variables in the aggregation of the Euclidean distance. Moreover, it is able to deal with uncertain environments where the information is very imprecise and can be assessed with fuzzy numbers. We study some of its main properties and particular cases such as the fuzzy maximum distance, fuzzy minimum distance, fuzzy‐normalized Euclidean distance (FNED), fuzzy‐weighted Euclidean distance (FWED), and fuzzy Euclidean ordered weighted averaging distance (FEOWAD) operator. Finally, we present an application of the operator to a group decision‐making problem concerning the selection of strategies.
Operator (biology)
Group Decision Making
Distance measures
Cite
Citations (16)
Line (geometry)
Real line
Euclidean algorithm
Euclidean domain
Cite
Citations (34)
Curve fitting
Cite
Citations (21)
A sufficient condition of Euclidean rings is given by polynomial optimization. Then, through computation, we give all norm-Euclidean square number fields, four examples of norm-Euclidean cubic number fields and two examples of norm-Euclidean cyclotomic fields, with the absolute of a norm less than 1 over the corresponding box, respectively.
Euclidean domain
Square (algebra)
Euclidean shortest path
Euclidean algorithm
Cite
Citations (0)
Considering that the information is incomplete and unevenly distributed in comprehensive evaluation,and people often subjectively look backin reality,a comprehensive evaluation method of Euclidean norm based on the interval number ordered weighted averaging operator( IOWA operator) is proposed. Firstly,the paper introduces the related knowledge about IOWA operator. Then,based on the characteristics of the IOWA operator,it employs the normal distribution to determine the position weight vector,and combines the weight vector with Euclidean norm to form weighted Euclidean norm effectively. Finally,the paper gives an example to verify the effectiveness of the method proposed,which considers the distribution of evaluation information fully and makes the evaluation more objective and accurate.
Operator (biology)
Operator norm
Cite
Citations (0)