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deposition images Detection of Aβ plaque deposition in MR images based on pixel feature selection and class information in image level PMID: Detection of Aβ plaque deposition in MR images based on pixel feature selection and class information in image level Yongming Li 1Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing, 400038 China 2College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044 China Xueru Zhu 2College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044 China Pin Wang 2College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044 China Jie Wang 2College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044 China Shujun Liu 2College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044 China Fan Li 2College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044 China Mingguo Qiu 1Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing, 400038 China 1Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing, 400038 China 2College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044 China Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.
The Creative Commons Public Domain Dedication waiver applies to the data made available in this article, unless otherwise stated.
As a noninvasive, nonradioactive and highly cost-effective clinical imaging method, magnetic resonance imaging MRI is the perfect imaging deposition images for the clinical diagnosis of AD, but it cannot display the plaque deposition directly.
This paper resolves this problem based on pixel feature selection algorithms at the image level.
Methods and results Firstly, the brain region was segmented from mouse model brain MR images.
Secondly, the pixels in the segmented brain region were extracted as a feature vector features.
Thirdly, feature selection was conducted on the extracted features, and the optimal feature subset was obtained.
Fourthly, the various optimal feature subsets were obtained by repeating the same processing above.
Fifthly, based on the optimal feature subsets, the final optimal feature subset was obtained by voting mechanism.
Finally, using the final optimal selected features, the corresponding pixels on the MR images could be found and marked to show the information about Aβ plaque deposition.
The MR images and brain histological image slices of twenty-two model mice were used in the experiments.
Four feature selection algorithms were used on the MR images and six kinds of classification experiments are conducted, thereby choosing a pixel feature opinion eurodollar certificates of deposit quizlet speaking algorithm for further study.
The experimental results showed that by using the pixel features selected by the algorithms in this paper, the best classification accuracy between early AD and control slides could be as high as 80 %.
The selected and marked MR pixels could show information of Aβ plaque deposition without missing most of the Aβ plaque deposition compared with brain histological slice images.
The hit rate is over than 90 %.
Conclusions According to the experimental results, the proposed detection algorithm of the Aβ plaque deposition based on MR pixel feature selection algorithm is effective.
The proposed algorithm can detect the information of the Aβ plaque deposition on MR images and the information can be useful for improving the classification accuracy as assistant MR biomarker.
Besides, these findings firstly show the feasibility of detection of the Aβ plaque deposition on MR images and provide reference method for interested relevant researchers in public.
Today, it represents a major public health problem and accounts for the majority of the whole population with dementia.
Therefore, early diagnosis of AD is very necessary.
With the emergence of symptomatic treatment and the promise of drugs that can delay disease progression, the development of diagnostic biomarkers for AD has become very important.
Relevant studies have shown that the β-amyloid Aβ protein is the main component of senile plaques.
A marked increase in Aβ in anatomical structures e.
Research has also shown that the main pathologic characteristic of AD is that Aβ deposition appears in the cerebral cortex and hippocampus, which gradually accumulate senile plaques SPs.
Previous studies have indicated that Aβ, which has strong neurotoxicity, is the core pathogenic substance of AD and is the most important prevention and treatment target for AD.
Research has shown that Aβ begins to deposit 15—20 years before AD symptoms occur.
Therefore, the noninvasive detection of the Aβ would be very helpful to the early diagnosis of AD.
In addition to detecting Aβ plaque deposition, it is possible to increase the noninvasive early diagnostic accuracy in AD.
However, due to technological deficiencies in the noninvasive detection of Aβ, it is difficult to attain the goal of clinical application of research for early diagnosis and timely intervention therapy.
Compared with the disadvantages of PET, MRI is inexpensive and noninvasive, it involves no radiation and no tracer, it has high resolution, and it has been widely applied in clinical applications.
Insoluble cellulose caused by Aβ leads to the rapid attenuation of proton magnetization.
Based on the analysis above, although MR cannot display information about Aβ plaque deposition information directly, it can reflect information.
Therefore, it is just click for source to find a method to extract the information of Aβ plaque deposition from MR images and display it.
The extraction of Aβ information is essentially a data mining problem, so it is feasible to solve the problem by introducing machine learning methods.
This paper intends to solve this problem using this idea.
First, segment the brain regions that possibly contain Aβ plaque deposition on brain MR images.
Second, extract the pixels from the brain regions to construct pixel feature vectors samples.
Third, conduct feature selection by maximizing the classification accuracy and obtaining the optimal pixel features.
Finally, map the selected pixel features to the corresponding pixels on MR images, thereby showing the Aβ plaque deposition.
The remainder of this paper is organized as follows.
In this process, four feature selection algorithms are involved.
Finally, highlights are presented in last section.
The mice were obtained from Beijing HFK Bio-technology Co.
The animals were housed in temperature- and humidity-controlled rooms with ad libitum access to food and water.
All of the research with mice followed the University Policies on the Use and Care of Animals and was approved by the Institutional Animal Experiment Committee of Fourth Military Medical University.
All of the experiments were performed blinded with regard to the genetic status of the mice.
The mouse model in this paper was the APP transgenic mouse, which causes Aβ plaque deposition.
The 9 months old App mice are corresponding to the early stage of AD.
The MR images and brain histological images all came from the Beijing animal imaging scan room.
The information about the image data is summarized as follows: the MR image sequence was a T2-weighted image sequence TE first echoTR: 4000 ms, ETL: 8, and ESP: 10; and the data size was 128 × 128.
Each mouse had 12-layer, two-dimensional images DICOM format.
The 4th to 9th slices of brain MR images of each mouse were chosen.
For subsequent pixel feature selection, the 132 samples are randomly divided into three parts.
They are training set, validation set and test set respectively.
The three parts do not overlap link other.
By repeating the division for eight times, the eight groups of the data sets are constructed.
The brains were fixed in 0.
After treatment with 0.
The slices were then incubated with horseradish-labeled secondary antibody for 1 h, and the color signal was developed with DAB.
The nuclei were stained with hematoxylin.
The slides were observed, and images were obtained under an Olympus BX53 microscope Olympus, Japan.
Brain histological images were divided into the immune group and control group, the former containing apparent Aβ plaque deposition.
The resolution of the brain histological images was 2560 × 1920, and they were magnified 40 times.
Each mouse had six tissue section images including the left and right sections of the hippocampus and the left and right sections of the cerebral cortex.
As shown in Fig.
But there is no obvious difference between the two images.
The middle images are the left hippocampi of two MR images, and we cannot distinguish whether they contain Aβ plaque deposition or not.
In the figure, the right images are the corresponding microscopy images of histology slides c and f.
By observing the brain histological images, we found that for CTL mice, there was no Aβ plaque deposition, whereas for early lesions in the AD mice, Aβ plaque deposition could be seen in the brain histological slice images see small brown area.
MR and tissue section images of Aβ protein deposition.
The result shows that there is strong correlation between the MR pixels and the information of the Aβ plaque.
By observation, the histology slices from APP mice AD contain the Aβ plaque deposition apparently, but the histology slices from healthy mice CTL contain the Aβ plaque barely.
Hence there is strong correlation between the information of the Aβ plaque deposition and the classification of CTL and AD.
As we known, Aβ plaque deposition is an important prevention and treatment target for AD, so there are strong correlations among the three things.
The classification accuracy of the Aβ plaque deposition can be improved by selecting the corresponding MR pixels.
The MR pixels can reflect the information of the Aβ plaque deposition.
The information of the Aβ plaque deposition can be helpful for improving the classification accuracy of CTL and AD.
Therefore, the AD and CTL groups could be considered a gold standard to detect the Aβ plaque deposition.
Based on this idea, we could optimize the MR pixels that reflect information about Aβ plaque deposition by maximizing the classification accuracy of AD and CTL samples.
Therefore, this idea could be called the detection of Aβ plaque deposition on MR images based on pixel feature selection and class information at the image level.
Flowchart of the propose algorithm The flow chart of the proposed method is shown in Fig.
First, the brain tissue is segmented manually, and then brain tissue images of the mice are obtained.
Second, the pixel values are extracted from the brain tissue https://spin-jackpot-money.site/deposit-84/pokerstars-wcoop-deposit-code-3243.html to form feature matrices as data samples.
Third, randomly split samples are obtained for training, validation and testing of the three parts, each for training, optimizing, and testing the feature selection and performance of the model respectively.
Fourth, the optimal pixel features are obtained by maximizing the classification accuracy of AD.
Fifth, the final optimal pixel features are obtained by voting mechanism.
Sixth, the test samples are classified as CTL or AD base on the final optimal pixels and the classification accuracy rates are calculated.
Finally, elastic mapping is performed of the final optimal pixel features onto the pixels on the MR images of AD, and they are marked to show the location of the Aβ plaque deposition.
Flowchart of this detection idea.
The process consists of four main steps, including segmentation of brain tissue with MRIcro, pixel feature extraction, feature selection with CAGA or PCA, and classifier testing; marking of brain MR images is based on the coordinate information of the pixel features Brain image segmentation In this paper, MRIcro software was used to examine and transform efficiently the brain MRI of mice and to input and output the brain imaging data.
Because Aβ plaque deposition is located in the brain tissue region, the brain tissue region of the mice was the region of interest ROI.
This paper created and preserved the segmented images using MRIcro.
To ensure the accuracy of the segmentation, the whole process was conducted under the guidance of a doctor.
First, the outline of the brain tissue was manually traced; second, the filling operation was performed; and finally, the ROI was output as an analytic image, as shown in the following Fig.
In this paper, the process of manual extraction of brain tissue images was performed under the supervision of doctors.
The segmentation results were accepted by the doctor, and the segmentation accuracy met the requirements.
Effect of brain tissue segmentation.
According to different images, the number of pixels in the brain tissue was possibly different, and the lengths of the feature vectors were different.
Therefore, based on the shortest feature vector, elastic mapping was conducted of the feature vectors with different lengths onto those with the same length.
The length was determined by the shortest feature vector.
Feature selection Usually, the feature selection method included three major parts: the feature selection mode, search algorithm, and evaluation criteria.
They are described as follows.
Feature selection mode In this paper, two types of feature selection modes were used: wrapper mode and filter mode.
The former evaluates subsets of variables, unlike filter approaches, which allow the detection of the possible interactions between variables.
Its evaluation criterion is the accuracy rate from the classifier.
The latter is an unsupervised learning algorithm that analyzes the internal information of the feature subset to measure its quality.
Search algorithm optimization algorithm In the paper, two popular search algorithms were involved here, PCA principal component analysis and chain agent genetic algorithm CAGA algorithmwhich the authors proposed previously.
The CAGA algorithm, as an improved agent genetic algorithm, has advantages of high and stable search accuracy and strong robustness.
The population individuals are designed to be intelligent agents within this algorithm.
For details, please see Ref.
The agent ring can be described as that in Fig.
Each circle represents an agent, the data in a circle represent its position in the ring, and the agent can interact with the left neighboring position and the right neighboring position.
Chain-like agent structure inside the sub-population.
The whole large circle indicates one population.
Each small circle represents an agent, and L size indicates the size of the population.
The figure indicates the population structure of the genetic algorithm Energy is defined as follows: an agent, a, represents a candidate solution to the optimization problem in process.
The value of its energy is defined as follows: 2 where fitness indicates the fitness value of some individual in the population.
For feature selection, it corresponds to some evaluation criterion.
As seen, each agent represents an individual.
To realize the local perceptivity of agents, the environment is constructed as a chain-like structure as mentioned above.
L i, n indicates the nth gene of the ith individual L 1, i that is, the chromosomeand m 1, n indicates the nth gene of Max 1, i.
If L 1, i satisfies formulathen it persists in the agent chain.
Otherwise, it dies, and its chain-point is occupied by New 1, i.
The competition process is performed in ascending order, and after the competition of the 1st agent, the 1st agent is updated.
Assuming the ith agents before competition and after competition are L 1i p r e and L 1i p o s t, respectively, so Max 1, i is determined by Eq.
Updating of L 1i t is as follows in Eq.
Adaptive crossover operator In the course of the crossover operation, the crossover probability is adaptive.
The corresponding formula is given by Eq.
The specific cross-operation is as follows: generate a random number U 0, 1 between 0 and 1 and then compare it with p c to determine whether L 1i t can cross over with max 1, i.
The intersection parents randomly exchange at the same locus, thereby generating new individuals.
Stopping criterion To obtain more stable selection results, the paper introduces an adaptive stopping criterion.
N iter indicates the preset maximum iteration number.
Evaluation criteria The evaluation criteria are decided based on the feature selection mode.
Under the wrapper mode, the evaluation criterion is classification accuracy.
The evaluation criterion under the filter mode is characterized by internal information of the feature measurements guidelines.
In this paper, we adopt the separability distance criterion.
In this paper, the evaluation criterion under the filter mode is the separability distance criterion, which is one of the classification abilities that characterize the evaluation criteria.
Like the mainstream standard to evaluate the separability, the separability criterion could replace classification accuracy for feature selection, and its value is positively proportional to the classification capability.
In this paper, the fitness deposition images by the separability distance criterion is designed based on geometric distance.
The fitness function is the ratio between the inter-class variance and intra-class variance.
Its main parameters to be trained are described in Eq.
Here, we used linear kernel as the kernel function, which can be written as follows in Eq.
Step 1: Bootstrap methods are used for re-sampling, randomly generating T training sets: S 1, S 2, …, S T.
Step 2: Based on each training set, the corresponding decision trees are generated: C 1, C 2, …, C T; m attributes from M attributes are randomly selected as the splitting attribute set of the current node, and the node is split.
Step 3: Each tree grows integrally, without being pruned.
Step 4: Based on each decision tree, the sample in the test set X is classified, thereby obtaining the corresponding categories, C 1 XC 2 X…, C T X.
Step 5: Applying the voting method, the categories of the sample are output by the T decision trees.
The category with maximum votes is the final category of the sample.
Pixel feature selection algorithms Based on the main parts described above, the four pixel feature selection algorithms are developed for selecting the optimal pixel features, thereby showing the information of the Aβ plaque deposition.
The four algorithms are described as follow.
By genetic iteration, the GA search the optimal pixel features by maximizing the fitness value.
The separability distance criterion is a useful criterion for indirectly evaluating the classification accuracy.
It has advantage of low time cost and high generalization capability.
Here, both the training set and validation set are used for calculating the fitness values based on the feature subset candidates chromosomes.
The feature subset candidate with the highest fitness value is the optimal feature subset or the optimal pixels features feature vector.
This transformation is defined in such a manner that the first principal component has the largest possible variance that is, accounts for as much of the variability in the data as possibleand each succeeding component, in turn, has the highest variance possible under the constraint that it is orthogonal to the preceding components.
The resulting vectors are an uncorrelated orthogonal basis set.
The principal components are orthogonal because they are the eigenvectors of the covariance matrix, which is symmetric.
By the procedures above, the best components can be obtained.
By inverse covariance matrix, the best components can be transformed as the corresponding optimal pixel features.
The whole process of PCA is conducted on the training and validation set.
GA is used for search algorithm.
The classification accuracy from SVM is used for constructing the fitness function.
By genetic iteration, the GA search the optimal pixel features by maximizing the fitness value.
The classification accuracy is a useful criterion for directly evaluating the classification accuracy.
It has advantage of high classification accuracy.
Here, the training set is used for training the SVM with feature subset candidates chromosomes ; the validation set is used for calculating the fitness values based on the trained SVM and the feature subset candidates.
The feature subset candidate with the highest fitness value is the optimal feature subset or the optimal features feature vector.
GA is used for search algorithm.
The classifier is RF rather than SVM.
The classification accuracy from RF is used for constructing the fitness function.
By genetic iteration, the GA search the optimal pixel features by maximizing the fitness value.
Here, the training set is used for training the RF with feature subset candidates chromosomes ; the validation set is used for calculating the fitness values based on the trained RF and the feature subset candidates.
The feature subset candidate with the highest fitness value is the optimal feature subset or the optimal features feature vector.
Voting mechanism By the feature selection algorithm, the optimal pixel features can be obtained.
By repeating the same feature selection algorithm for m times, the m optimal feature subsets feature vectors can be obtained.
For each feature, calculate the times k select that the feature which is selected.
Elastic mapping from selected pixel features to the marked pixels on MR images The main procedure is described as follows: Step 1: m optimal feature subsets are obtained from the pixel feature selection algorithm by repeating it m times.
Step 2: The voting mechanism is used to obtain the final optimal feature vector.
Step 3: According to the final optimal feature vector, elastic mapping to the original pixels feature vectors that formed in feature extraction section, thereby obtaining the corresponding pixels on the brain MR image.
Step 4: The mapped pixels are marked on the MR image of AD to show the position of the Aβ plaque deposition Step 5: Steps 1—4 are repeated until all of the MR images of AD are marked.
The process can be seen in Fig.
Flowchart of elastic mapping based on the optimal feature subset.
Feature subsets were obtained by binary coding; m is the total number of samples.
By all of the feature subsets, the optimal feature subset is obtained.
Pixel vectors with different dimensions were obtained using elastic mapping from the optimal feature vectors.
By the pixel vectors, the corresponding pixels in the brain images can be found and marked, thus providing information about Aβ plaque deposition Experimental conditions The mouse model in the paper consisted of APP transgenic mice, which experience Aβ plaque deposition.
The MR images and all of the brain histological images came from the Beijing animal imaging scan room.
The information about the imaging data is summarized as follows: The data were divided into two categories CTL and AD.
The image sequence was a T2-weighted image sequence TE first echoTR: 4000 ms, ETL: 8, and ESP: 10; the data size was 128 × 128.
Each mouse had 12-layer two-dimensional images DICOM format.
The area of brain tissue on some of the images was small, so these images were removed.
The 4th to 9th slices of the brain MR images of each mouse were chosen.
The shortest feature vector was used as a feature vector template.
Every feature vector was aligned mapped with the feature vector template.
The length of the template vector was 2911, so the images of the 132 samples were converted to gray feature matrices of 2911 × 132, where 132 is the number of data samples, and 2911 is the number of the features.
In this paper, experimental operating system platform was the Windows, version 7, 64-bit operating system, and the memory size was 4 GB.
To verify the accuracy of the algorithms to detect Aβ plaque deposition information on MR images, four feature selection algorithms are involved.
Based on the optimal pixels from the feature selection algorithms, the test samples were classified by the corresponding classifiers SVM, RF.
By cross combination, the six kinds of classification experiments were conducted.
For CAGA, to balance time cost and accuracy better, according to many experimental statistics, we determined the size of the initial population at 50, the initial crossover probability was 0.
At the end of each iteration, according to the fitness value, we reserved the 50 best individuals, and the maximum number of iterations was set at 30, which helped to find the global optimum deposit cruise and to select the optimal feature subset.
The different decision trees in the random forest had impacts on their generalization performance.
To reduce the impact of randomness, the paper established 100 forest models randomly and then regarded the average value of the average accuracy go here as the classification accuracy of the current tree.
Finally, by considering the random forest containing trees and the modeling speed, we chose the number of trees as the object of optimization and combine it with CAGA to select the optimal feature subset.
According to the statistical experiments, 500 was the best number of decision trees for CAGA + random forest.
For PCA + random forest, the best number of trees was 650.
Performance evaluation index The evaluation metrics used to measure the accuracy of whether Deposition images contained Aβ plaque deposition were the accuracy, sensitivity, and specificity of the test sample.
Classification accuracy of AD based on detected Aβ plaque deposition information indirectly The six kinds of classification experiments discussed above were conducted on the classification of the test samples.
Every sample is classified and labeled with AD or CTL class tag.
By comparing the labeled class tag of the samples and the real class tag of them, the classification accuracy rates can be calculated.
Each experiment was repeated eight times, and the statistical results of the classification are shown in Table.
It would be helpful to improve the accuracy of the current classification methods of AD on MR images.
The case is similar with the sensitivity and specificity.
Since the App mice slides are corresponding to the early stage of the AD and there is no obvious difference for the MR images of the two categories, the classification accuracy based on one biomarker is applicable.
Diagram of classification accuracy of the six types of classification algorithms.
The picture describes the average classification accuracy by the six types of classification algorithms for eight repetitions.
The x axis indicates the running time; the y axis indicates the classification accuracy Figure shows classification accuracy curve of the six experiments repeated eight times.
Figure shows the box plots of accuracy for the six experiments.
As seen in the figure, we could intuitively find outliers within the data.
Observing the length of the box, the top-and-bottom spaced entries, and the length of the whiskers, we could judge deposit in bank discrete degree and bias of accuracy from the six types of methods.
Box plots of six types of algorithms.
F filter, W wrapper.
The six box plots represent the skewness and tail weight of the data, intuitively and clearly identifying outliers in the data batch.
The p value above the box indicates a significant difference between the algorithms in this paper and a random algorithm Classification performance based on CAGA filter feature selection algorithms Because CAGA + SVM and CAGA + RF in filter mode have best classification accuracy, they are analyzed in this section.
Comparisons of the classification results in both cases are shown in Fig.
The classification results of the optimal classification algorithms.
The graph shows the classification accuracy with same feature selection algorithm and with different classifiers.
The green line indicates the best classification accuracy Compared with RF, the classification accuracy of SVM is far better.
The average accuracy is 73 %, and the accuracy rate is 76.
Analysis of significance level of the feature selection algorithms To demonstrate the significance level of the classification accuracy of the algorithms proposed in the paper the proposed algorithms were significantly different from the random pixel selection algorithmthe t test of the hypothesis was conducted.
The results are shown in Table and Fig.
The results indicated that the high classification accuracy was based on the proposed algorithms themselves, rather than chance.
In other words, the results indicated that the effectiveness of detection of the Aβ plaque deposition was based on the proposed algorithms themselves, rather than chance.
Repeat the feature selection for ten times, the ten optimal feature vectors are obtained.
By voting mechanism, the final optimal feature vector is obtained.
Conduct classification of test samples by SVM and the final optimal feature vector MR pixel features.
If the current test sample is classified as AD sample, the sample is labeled and the corresponding pixels in the sample are marked by the elastic mapping and the final optimal feature vector, thereby showing the Aβ plaque depositions.
By repeating the experiments for eight times, the statistical classification accuracies are obtained.
Please see the Table.
The mean classification accuracy is 75 % or so.
The results mean that the detected Aβ plaque deposition information can be helpful to improve the classification accuracy.
Besides, by voting mechanism, the number of the selected pixels decreases greatly.
The selected pixels become more stable and can reflect the Aβ plaque deposition information better.
In order to show that the classification accuracies are obtained based on the proposed algorithm rather than chance, the significance analysis of the mapped pixels marked pixels was conducted.
Seen from the p values in the Tableall the p values are lower than 0.
Apparently, the proposed algorithm is different from random algorithm which randomly selects the MR pixels greatly.
The classification accuracy indirectly reflects the Aβ plaque information is detected by the final optimal selected pixels.
In other words, the proposed detection algorithm is effective.
Figure shows the marked pixels detected Aβ plaque deposition information in the MR images and the corresponding brain histological image slices.
A, D: Hippocampi on brain MR images; B, E: Marked hippocampi of MR images; C, F: Hippocampi in corresponding brain histological image slices.
The regions marked with different colors in B and E are related to the distributions of the apparent Aβ plaque deposition in C and F.
Effect of detection of the Aβ protein deposition.
A, D Hippocampi on brain MR images; B, E Marked hippocampi of MR images; C, F Corresponding tissue section images.
The regions marked with different colors in B and E are related to the distributions deposition images the Aβ plaque deposition in C and F Seen from the figure, no information about Aβ plaque deposition in hippocampi could be directly seen in image A and D.
The brain histology image C and F could show information about Aβ plaque deposition.
By the proposed algorithm, the information about Aβ plaque deposition in hippocampi could be shown in MR image B and E.
The different colors of the ellipses show the Aβ plaque depositions which are matched with those marked pixels on the MR image slices.
The black line between the same color of the ellipses means the Aβ plaque deposition is matched.
Seen from the different colors of the ellipses, the positions of the main Aβ plaque depositions could be shown check chase a deposit at image B and E.
In other words, the proposed algorithm could detect Aβ plaque deposition on MR images.
By counting the matched and unmatched plaque depositions, the match rate and miss rate can be calculated.
The proposed detection algorithm is tested on the eight groups of test samples.
The information about the matching of the Aβ plaque deposition can be found in the Table and can show the performance of the proposed algorithm.
Since the test sets are constructed by random division of whole data set, there are different slices from different mice in every test set.
With the AD MR samples, the corresponding brain histological image slices can be found.
By observing the brain histological image slices, the apparent Aβ plaque depositions can be found and counted with the help from clinician of neurology.
By summing up the Aβ plaque depositions in the 20 article source, the total number of the Aβ plaque depositions in hippocampus in one group of test set can be calculated.
The one possible reason is that all the AD mice have the same age 9 month old.
By comparing the Aβ plaque depositions in hippocampus in the brain histological image slices and the marked pixels in the corresponding brain MR image slices, the https://spin-jackpot-money.site/deposit-84/lake-deposits-crossword-clue-3234.html and unmatched Aβ plaque depositions can be found.
By accumulating them, the number of matched and unmatched Aβ plaque depositions in hippocampus can be obtained.
The match rate more info miss rate can be obtained.
Seen from the rates, most of them are over 90 %.
The results show that the proposed detection algorithm can detect most of the Aβ plaque depositions while no other method can do the thing before.
According to the relevant theory about the Aβ plaque deposition of AD, the Aβ plaque deposition is positively proportional of the progress of AD, but the volume and the distribution of the Aβ plaque deposition do not correspond to the different states of AD one-to-one source />Hence, the 80—90 % of match rate to show the apparent Aβ plaque deposition can be acceptable.
First, based on the selected MR pixels, the intensity values of the selected pixels in every sample are found and accumulated.
The relevant information can be found in Table.
The results show that the intensity value based on the selected MR pixels can distinguish the image slices of AD and CTL.
The p value is lower than 0.
The classification based on the selected MR pixels is stable and reliable.
By considering the AD with Aβ plaque deposition and the CTL without Aβ plaque deposition, the results indirectly support the conclusion that the selected MR pixels reflect the information of the Aβ plaque deposition.
Discussion In this work, we proposed a detection algorithm for showing Aβ plaque deposition on MR images.
First, the brain tissue is segmented manually, and then brain tissue images of the mice are obtained.
Second, the pixel values are extracted from the brain tissue images to form feature matrices as data samples.
Third, randomly split samples are obtained for training, validation and testing of the three parts, each for training, optimizing, and testing the feature selection and classification of the model.
Fourth, the optimal pixel features are obtained by maximizing the classification accuracy.
Fifth, the final optimal pixel features are obtained by voting mechanism.
Sixth, the test samples are classified as CTL or AD base on the final optimal pixels and the classification accuracy rates are calculated.
Finally, elastic mapping is performed of the optimal pixel features onto the pixels on the MR images of AD, and they are marked to show the location of the Aβ plaque deposition.
There are two types of mouse models—CTL and AD: AD model contains Aβ plaque deposition, and CTL model does not.
The result shows that there is strong correlation between the information of the Aβ plaque deposition and the classification of CTL and AD.
By cross combination, the six kinds of classification experiments were conducted.
All the average classification accuracies of the six kinds of classification experiments are above 50 % greatly.
The best classification accuracy can achieve the 80 %.
By voting mechanism, the final optimal MR pixel feature vector is obtained.
Based on the final deposition images MR pixel feature vector, SVM is used to classify the test samples, label them, and output the classification accuracies.
The classification accuracy is improved further.
By comparing the marked MR pixels and the Aβ plaque depositions in the corresponding brain histological image slices by clinician, most of the apparent Aβ plaque depositions are matched by the marked MR pixels.
The results can directly support the effectiveness of the proposed feature selection algorithm.
They are satisfying and meet the elementary requirement from the department of neurology in hospital.
The experimental results of the significance level of the propose detection algorithm show that the positive just click for source are stable and reliable rather than by chance.
The experimental results show that the selected MR pixels can distinguish the CTL and AD samples significantly.
To the best knowledge of the authors, the detection of Aβ plaque deposition from brain MR images alone has not been discussed before in public.
Furthermore, PET is radioactive and expensive, preventing it from clinical application and from being accepted by patients requiring detection.
In comparison, MRI is inexpensive, noninvasive and non-radioactive, and it can provide information about anatomical structures and small lesions.
If it can detect Aβ plaque deposition, it will be a better imaging technology for clinical application.
This paper proved that it is feasible to detect information on Aβ plaque deposition with MR images alone, providing a solution for related research.
Because the detection of Aβ plaque deposition using only brain MR images has not been discussed before, no existing methods were compared with the method proposed in this paper.
To show that the idea of detecting Aβ plaque deposition on brain MR images alone is feasible, six pixel selection and classification experiments were realized.
According to the experimental results, most of the data show that the idea is feasible.
In addition, the classification rate was as high as 80 %, with high significance level, indicating that the idea of pixel selection based on the classification of images was feasible and reliable.
The best feature selection algorithm is chosen deposition images the four feature selection algorithms.
Based on the selected pixels and voting mechanism by it, the final optimal selected pixels are obtained.
By elastic mapping, the corresponding pixels on brain MR images of AD were found and marked to show Aβ plaque deposition.
By comparing the marked pixels on brain MR images of AD and the Aβ plaque deposition on corresponding histology images of the brain, it was found that the major Aβ plaque deposition was not missed.
Compared with the random algorithm, this proposed idea and the proposed algorithm were effective with a high significance level.
The hit rate and miss rate were calculated and support the effectiveness of the proposed detection algorithm of the Aβ plaque deposition on MR images.
Conclusions Aβ plaque deposition is an important target for early AD diagnosis and the evaluation deposition images treatment, so the noninvasive and nonradioactive detection of Aβ plaque deposition is necessary, especially for real applications.
MRI is a safe and cost-effective imaging method, and can contain information about Aβ plaque deposition.
However, it cannot detect the Aβ plaque deposition directly and there is currently no existing method to extract Aβ plaque deposition information and to show it on MR images.
In order to solve this problem, this paper proposed MR pixel feature selection algorithm to search for the Aβ plaque deposition information on MR images by maximizing the classification accuracy of AD and CTL MR samples.
The experimental results showed that the algorithms in the paper could obtain the best classification accuracy of CTL and early AD more than 80 % with high significance.
In addition, the selected pixels could link the position of Aβ plaque deposition with high match rate.
Most of the main Aβ plaque deposition was not missed almost.
The selected pixels can help to distinguish the CTL and early AD samples significantly.
The proposed detection method is based on the class information of the image slices, and they can detect Aβ plaque deposition based on class information at the image level.
Although it is effective to detect the Aβ plaque from brain MR images, there are many works to do to further evaluate and refine the proposed detection algorithm in the future.
For example, more mice models possible are needed; human body experiments are needed for clinical research; it is useful to construct a diagnosis algorithm of AD by combing the detected Aβ plaque information with the other MR biomarkers.
Highlights This paper proposed pixel feature selection algorithm as detection algorithm to extract information of Aβ plaque deposition from MR images by maximizing classification accuracy.
By considering the advantages of the MRI, the function can be helpful just click for source putting the Aβ into clinical applications.
The function is helpful for better understanding the mechanism of the occurrence and development of the Aβ plaque deposition of mouse model in vivo.
YL and MQ conceived of the whole study, and participated in its design and coordination and helped to draft the manuscript.
XZ and FL participated in the measurements of all subjects and drafted the complete.
PW and SL managed the trials and assisted with writing discussions in the manuscript.
All authors read and approved the final manuscript.
Acknowledgements Authors thank the professor Yanjiang Wang for his valuable suggests and several brain models.
Availability of data and supporting materials section The row data used in the manuscript was obtained from Beijing HFK Bio-technology Co.
If you need umass deposit row data to conduct related research,please do not hesitate to contact yongmingli cqu.
No further supporting materials section will be provided.
Ahmed OB, Mizotin M, Benois-Pineau J, Allard M, Catheline G, Amar CB.
Comput Med Imaging Graph.
Alafuzoff I, Thal DT, Bogdanovic N, Al-Sarraj S, Bodi I, Boluda S, Bugiani O, Duyckaerts C, Gelpi E, Gentleman S.
Assessment of β-amyloid deposits in human brain: a study of the BrainNet Europe Consortium.
Andreasen N, Blennow K.
Antonios G, Borgers H, Richard BC, Brauß A, Meißner J, Weggen S, Pena V, Pillot T, Davies SL, Bakrania P, Matthews D, Brownlees Deposition images, Bouter Y, Bayer TA.
Alzheimer therapy with an antibody against N-terminal Abeta 4-X and pyroglutamate Abeta 3-X.
Jankowsky JL, Slunt HH, Gonzales V, Savonenko AV, Wen JC, Jenkins NA, Copeland NG, Younkin LH, Lester HA, Younkin SG, Borchelt DR.
Persistent amyloidosis following suppression of Abeta production in a transgenic model of Alzheimer disease.
Kohannim O, Hua X, Hibar DP, Lee S, Chou YY, Toga AW, Jack CR Jr, Weiner MW, Thompson PM.
Boosting power for clinical trials using classifiers based on multiple biomarkers.
In: International conference on transportation engineering 2009, vol.
Linda JC, van Waalwijk van Doorn LJ, Koel-Simmelink MJ, Haußmann U, Klafki H, Struyfs H, Linning P, Knölker HJ, Twaalfhoven H, Kuiperij HB, Engelborghs D, Scheltens P, Verbeek MM, Vanmechelen E, Wiltfang J, Teunissen CE.
Validation of soluble amyloid-β precursor protein assays as diagnostic CSF biomarkers for neurodegenerative diseases.
Klunk WE, Engler H, Nordberg A, Wang Y, Blomqvist G, Holt DP, Bergström M, Savitcheva I, Feng HG, Sergio E.
Johnson KA, Sperling RA, Gidicsin CM, Carmasin JS, Maye JE, Coleman RE, Reiman EM, Sabbagh MN, Sadowsky CH, Fleisher AS, Doraiswamy PM, Carpenter AP, Clark CM, Joshi AD, Lu M, Grundman M, Mintun MA, Pontecorvo MJ, Skovronsky DM.
Small GW, Vladimir K, Ercoli LM, Siddarth P, Bookheimer SY, Miller KJ, Lavretsky H, Burggren AC, Cole GM, Vinters HV.
PET of brain amyloid and tau in mild cognitive impairment.
N Engl J Med.
Choi SR, Schneider JA, Bennett DA, Beach TG, Bedell BJ, Zehntner SP, Krautkramer MJ, Kung HF, Skovronsky DM, Hefti F, Clark CM.
Correlation of amyloid PET ligand florbetapir F 18 binding with Aβ aggregation and neuritic plaque deposition in postmortem brain tissue.
Alzheimer Dis Assoc Disord.
Brix G, Lechel U, Glatting G, Ziegler SI, Münzing W, Müller SP, Beyer T.
Wang J, Zhang Y, Zheng Y.
Chin J Med Sci.
Foster B, Bagci U, Mansoor A, Xu Z, Mollura DJ.
A review on segmentation of positron emission tomography images.
Brun F, Sensi F, Quartulli R, Rei L, Grucka A, Mancarella V, Chincarini A, Ukmar M, Accardo A, Longo R.
In: Proceedings of the annual international conference of the IEEE engineering in medicine and biology society.
Li G, Yang XF, Yang ES.
John H, Selkoe DJ.
Chamberlain R, Reyes D, Geoffrey LC, Marjanska M, Wengenack TM, Poduslo JF, Garwood M, Jr, Chamberlain CR, Reyes D, Curran GL, et al.
Meadowcroft MD, Connor JR, Smith MB, Yang QX.
J Magn Reson Imaging.
Zhang W, Hao J, Liu R, Zhang Z, Lei G, Su C, Miao J, Li Z.
López M, Ramírez J, Górriz JM, Álvarez I, Salas-Gonzalez D, Segovia F, Chaves R, Padilla P, Gómez-Rio M.
Li Y, Zeng X, Han L, Wang P.
Two coding based adaptive parallel co-genetic algorithm with double agents structure.
Eng Appl Artif Intell.
Sun Z, Fan Y, Lelieveldt BPF, Giessen MVD.
In: Proceeding of SPIE medical imaging, vol.
Piyush R, Ramakrishnan S.
Diffusion tensor based Alzheimer image analysis using region specific volume features and random forest classifier.
In: International conference in biomedical engineering, vol.


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