Guide Image Processing and Data Analysis : The Multiscale Approach

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Meanwhile, registration was adopted to test our SDFS algorithm using pattern matching in the cyclic structures of skeletons from multiscale segmentation results. We tried linear, affine, and quadratic transformation and found that the affine model was enough and robust to describe the transformation. For each species, 50 scallops of Yesso scallop, Weathervane scallop, and Zhikong scallop were randomly selected for capturing images.

Next, we divided them into two halves, with one half receiving 40 images used for training and the remaining half used for testing. Meanwhile, we randomly selected an image as a template image for the training images for every individual scallop. Indeed, in order to avoid the curse of dimensionality, it is desirable for the feature set to be as small as possible.

We used a neural network NN with three layers to obtain individual recognition. The number of inputs was equal to the dimension of the feature set. The hidden layer nodes were To maintain a certain level of confidence in the results, NN classification experiments were repeated 10 times. Training was conducted until the average error fell below 0. The average error denoted the error limit to stop NN training. The average error was the average of NN target output subtracted from the desired target output from all the training patterns.

The desired target outputs were set to [1 0] for the class representing the same individual, while for the rest of the feature classes, it was set to [0 1]. Obviously, the more delicate cyclic structures were identified; the higher accuracy was obtained using our multiscale segmentation method.


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The process of feature generation was basically the calculation of the similarity measures of cyclic structures in the segmented images and overlap ratio after registration. Because the dimension of the feature was fixed, the classification of feature vectors was fast. We further tested the effectiveness of the above individual recognition method in identifying the three species Yesso scallop, Weathervane scallop, and Zhikong scallop, which are generally indistinguishable from each other by using the naked eye.

Fifty scallops of each species were analyzed. Although the shape of Zhikong scallop was a little different compared to the Yesso scallop and Weathervane scallop, it was still a challenge to identify it in a big population of the three mixed species by manual sorting. An important advantage of this approach is that shape information from homologous anatomical structures landmarks , and points along curves and points on anatomical surfaces could be included in the same analysis and were termed semilandmarks.

Next, we digitized 15 semilandmarks along the ventral edge of the shell. Finally, we quantified the general shell surface by digitizing cyclic structures as semilandmarks on the surface texture of each image. To accomplish this, we selected cyclic structures from relatively evenly spaced surface on a single shell and treated this as a template. The cyclic structures on the image nearest to the cyclic structures on the template were then taken as the surface semilandmarks for that individual.

Astronomer (Honorary)

This was repeated on all individuals to obtain surface semilandmarks. We utilized this procedure to capture the general shape of the shell surface because the number of ridges per shell is not consistent among species or among individuals within a species. Once all individuals were digitized, we aligned them using a generalized Procrustes superimposition Rohlf, From the aligned shells, a set of Procrustes shape coordinates were obtained.

For the pair of species, we calculated the difference in average shell shape as the Euclidean distance between species means using Procrustes shape coordinates.

We also performed a principal components analysis PCA to visualize the patterns of variation within and among the species. The two species between the Yesso scallop and Zhikong scallop or between the Weathervane scallop and Zhikong scallop could be sorted on the basis of shell shape by PC1 alone; however, there was also significant sorting between species when viewed by both PC1 and PC2. The patterns of periodic structures are composed of bifurcation points, crossover points of the growth rings and ribs, and the connected lines.

The characteristic vector of each bifurcation structure consists of normalized branching angle and length, which is invariant against translation, rotation, scaling, and even modest distortion. This can greatly reduce mismatching during the matching process.

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This individual recognition method can be easily applied to species identification. Species identification is necessary because Yesso scallop, Weathervane scallop, and Zhikong scallop, as major economic aquaculture species, form mixed species in many areas around the China. One of the most compelling patterns observed in evolutionary biology is that of morphological and behavioral convergence among species inhabiting similar environments.

Background

Using our method, the Yesso scallop and Weathervane scallop, which inhabit similar environments and were more similar in their shell shape, could be easily sorted, which will be useful for scallop machine sorting in the future. Our method can also save time and cost less in comparison with molecular methods for the individual identification of scallops, especially in a large population. Therefore, we would propose that the periodic structures of scallop could be adapted to photographic mark—recapture PMR to study a large population of scallops.


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Genetic breeding for a higher growth rate is one of the main focuses of the scallop farming industry Gjedrem, Our method of scallop individual recognition could be used to calculate the growth rate easily by characterizing shell images during a time of scallop growth and development, thus allowing the subsequent study of these growth rates as they are influenced by environmental factors or aquaculture techniques. Indeed, the establishment of a method with a possible function in predicting the growth rate in relation to scallop developmental traits could provide useful information for targeting genetic improvement of this species.

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All authors, Q. X and T. W were involved in preparation of shells for image processing and conducted the major part of MATLAB package for data analysis. All authors read and approved the final manuscript. Volume 7 , Issue 5. The full text of this article hosted at iucr. If you do not receive an email within 10 minutes, your email address may not be registered, and you may need to create a new Wiley Online Library account. If the address matches an existing account you will receive an email with instructions to retrieve your username.

Ecology and Evolution Volume 7, Issue 5. Search for more papers by this author.

Image Processing and Data Analysis: The Multiscale Approach

Yangfan Wang Corresponding Author E-mail address: yfwang ouc. Tools Request permission Export citation Add to favorites Track citation. Share Give access Share full text access. Share full text access. Please review our Terms and Conditions of Use and check box below to share full-text version of article. Abstract The fine periodic growth patterns on shell surfaces have been widely used for studies in the ecology and evolution of scallops. Figure 1 Open in figure viewer PowerPoint. Figure 2 Open in figure viewer PowerPoint.

Figure 3 Open in figure viewer PowerPoint. Figure 4 Open in figure viewer PowerPoint. Figure 5 Open in figure viewer PowerPoint. Journal of Electronic Imaging 27 , 1. Journal of Mathematical Analysis and Applications. Optik , Applied Mathematical Modelling 61 , Mathematical Problems in Engineering , Journal of Mathematical Analysis and Applications :2, Journal of Computational and Applied Mathematics , Multidimensional Systems and Signal Processing IET Image Processing 12 :6, Applied Sciences 8 :6, Annals of PDE 4 Journal of Scientific Computing 75 :2, Cluster Computing Medical Imaging Image Processing , Journal of Scientific Computing 74 :3, Signal Processing: Image Communication 62 , Applied Optics 57 :2, Journal of Scientific Computing 74 :1, Multidimensional Systems and Signal Processing 29 :1, Bellido and Carlos Mora-Corral.

Ronny Bergmann and Daniel Tenbrinck. Variational Methods in Image Processing. Qiang Du and Xiaochuan Tian.


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Multiscale Modeling & Simulation

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