Precise quantification and localization of tracer-labeled neurons are essential for unraveling brain connectivity patterns and constructing a mesoscopic brain connectome atlas in macaques. However, methodological challenges and limitations in dataset development have impeded this scientific progress. This work introduced the Macaque Fluorescently Labeled Neurons (MFN) dataset, derived from retrograde tracing on three rhesus macaques. The dataset, meticulously annotated by six specialists, includes 1,600 images and 33,411 high-quality neuron annotations. Leveraging this dataset, we developed a Dense Convolutional Attention U-Net (DAUNet) cell counting model. By integrating Dense Convolutional blocks and a multi-scale attention module, the model exhibits robust feature extraction and representation capabilities while maintaining low complexity. On the MFN dataset, DAUNet achieved a Mean Absolute Error of 0.97 for cell counting and an F1-score of 96.29% for cell localization, outperforming several benchmark models. Extensive validation across four additional public datasets demonstrated the robust generalization ability of the model. Furthermore, the trained model was applied to quantify labeled neurons of a macaque brain, mapping the input connectivity patterns of two adjacent subregions in the lateral prefrontal cortex. This work provides a training dataset and algorithmic resource that advances mesoscopic brain connectivity research in macaques. The MFN dataset and source code are available at https://github.com/Gendwar/DAUnet.

Brain atlas is an indispensable tool for studying the relationship between brain structure and cognitive function. We proposed to create a new brain atlas – the Brainnetome atlas, using brain connectivity profiles. The Brainnetome atlas lays the foundation for research in brain science and brain-inspired intelligence, and opens a new avenue not only for the study of brain science and brain diseases, but also for brain-inspired intelligence. In this lecture, we first introduce the research background and content of the Brainnetome, including the definition and the main research directions of the Brainnetome, the idea of creating the Brainnetome atlas, and the essential differences from existing brain atlas. Then, we will introduce the application of the Brainnetome atlas in elucidating brain cognitive mechanisms and precise diagnosis of brain diseases. We will also introduce the challenges and solutions of neuromodulation robots guided by the Brainnetome atlas for precise treatment of brain diseases. Finally, a summary and perspective on future research directions are provided.

Lysergic acid diethylamide (LSD) has shown remarkable potential in modulating brain functional organization and dynamics. However, the exact mechanisms underlying its effects remain unclear. In this study, we employed a data-driven approach to analyze recurrent functional connectivity patterns in resting-state fMRI data and developed a parameterized feedback inhibition model to characterize excitatory/inhibitory (E/I) balance. The findings demonstrate that LSD enhances global brain synchrony and dynamic complexity. This enhanced synchrony likely stems from LSD's preferential stabilization of a globally synchronized yet functionally non-modular brain state - a pattern showing higher occurrence probability and acts as an "attractor" that recruits transitions from cognitive control networks. Crucially, these phenomena appear underpinned by LSD-induced convergence of excitatory/inhibitory balance across cortical hierarchies, particularly through Sensorimotor (SOM) suppression coupled with transmodal potentiation, where the Sensorimotor cortices emerge as potential regulatory hubs driving this neurochemical rebalancing. These convergent effects are consistent with the emergence of a brain state characterized by weakened sensory anchoring and enhanced cognitive flexibility, where the typical separation between concrete perception and abstract cognition becomes blurred. This neurophysiological remodeling therefore suggests a potential mechanism that could contribute to LSD's hallucinatory effects and its therapeutic potential in mental disorders characterized by rigid thought patterns.

Accurate sleep staging is crucial for the diagnosis of diseases such as sleep disorders. Existing sleep staging models with excellent performance are usually large and require a lot of computational resources, limiting their application on wearable devices. Therefore, it is a key issue to distil the knowledge embedded in large models into small heterogeneous models for better deployment. In the process of knowledge distillation of heterogeneous models for sleep electroencephalography (EEG) signals, we mainly deal with three major challenges: 1) There are large structural differences between heterogeneous sleep staging models; 2) What kind of knowledge should be conveyed in sleep EEG signals in the knowledge distillation of heterogeneous models; 3) Significant scale differences exist between heterogeneous models. To address these challenges, we design a generic heterogeneous model knowledge distillation framework for sleep staging. Specifically, we first propose a knowledge distillation strategy for heterogeneous models that addresses the large structural differences between heterogeneous models. Then, a multi-level knowledge distillation module is designed to effectively transfer important multi-level feature knowledge. In addition, the teacher assistant module is introduced to ease the scale difference between the heterogeneous models which further enhances the knowledge distillation performance. Experimental results on both Sleep-EDF and ISRUC datasets show that our distillation framework achieves state-of-the-art performance.

Sleep stage classification has important clinical significance for the diagnosis of sleep-related diseases. To pursue more accurate sleep stage classification, multi-channel sleep signals are widely used due to the rich spatial-temporal information contained. However, it leads to a great increment in the size and computational costs, which constrain the application of multi-channel sleep models on hardware devices. Knowledge distillation is an effective way to compress models, yet existing knowledge distillation methods cannot fully extract and transfer the spatial-temporal knowledge in the multi-channel sleep signals. To solve the problem, we propose a general knowledge distillation framework for multi-channel sleep stage classification called spatial-temporal mutual distillation. Based on the spatial relationship of human body and the temporal transition rules of sleep signals, the spatial and temporal modules are designed to extract the spatial-temporal knowledge, thus help the lightweight student model learn the rich spatial-temporal knowledge from large-scale teacher model. The mutual distillation framework transfers the spatial-temporal knowledge mutually. Teacher model and student model can learn from each other, further improving the student model. The results on the ISRUC-III and MASS-SS3 datasets show that our proposed framework compresses the sleep models effectively with minimal performance loss and achieves the state-of-the-art performance compared to the baseline methods.

Individual brains vary greatly in morphology, connectivity and organization. Individualized brain parcellation is capable of precisely localizing subject-specific functional regions. However, most individualization approaches have examined single modalities of data and have not generalized to nonhuman primates. The present study proposed a novel multimodal connectivity-based individual parcellation (MCIP) method, which optimizes within-region homogeneity, spatial continuity and similarity to a reference atlas with the fusion of personal functional and anatomical connectivity. Comprehensive evaluation demonstrated that MCIP outperformed state-of-the-art multimodal individualization methods in terms of functional and anatomical homogeneity, predictability of cognitive measures, heritability, reproducibility and generalizability across species. Comparative investigation showed a higher topographic variability in humans than that in macaques. Therefore, MCIP provides improved accurate and reliable mapping of brain functional regions over existing methods at an individual level across species, and could facilitate comparative and translational neuroscience research.

Brain atlas is an important tool in the diagnosis and treatment of neurological disorders. However, due to large variations in the organizational principles of individual brains, many challenges remain in clinical applications. Brain atlas individualization network (BAI-Net) is an algorithm that subdivides individual cerebral cortex into segregated areas using brain morphology and connectomes. The presented method integrates group priors derived from a population atlas, adjusts areal probabilities using the context of connectivity fingerprints derived from the fiber-tract embedding of tractography, and provides reliable and explainable individualized brain areas across multiple sessions and scanners. We demonstrate that BAI-Net outperforms the conventional iterative clustering approach by capturing significantly heritable topographic variations in individualized cartographies. The topographic variability of BAI-Net cartographies has shown strong associations with individual variability in brain morphology, connectivity as well as higher relationship on individual cognitive behaviors and genetics. This study provides an explainable framework for individualized brain cartography that may be useful in the precise localization of neuromodulation and treatments on individual brains.

As an important element of emotional brain-computer interfaces, electroencephalography (EEG) signals have made significant progress in emotion recognition due to their high temporal resolution and reliability. However, EEG signals vary widely among individuals and do not satisfy temporal non-stationarity. Furthermore, trained models cannot maintain good classification accuracy for new individuals or new sessions during the inference stage. Although domain adaptation has been employed to address these issues, most approaches that consider different subjects or sessions as a single source domain ignore the large discrepancies between source domains, while methods that consider multi-source domains need to construct a domain adaptation branch for each source domain. Here, we propose a novel emotion recognition method, i.e., multi-source attention-based dynamic residual transfer (MS-ADRT). We introduce a dynamic feature extractor, in which the model uses an attention module to induce parameters to vary with the sample, implicitly enabling multi-source domain adaptation by adapting to the sample, thus reducing multi-source domain adaptation to single-source domain adaptation. Maximum mean discrepancy (MMD) and maximum classifier discrepancy (MCD)-based adversarial training are also used to narrow distances between source and target domains and facilitate the feature extractor to mine domain-invariant and sentiment-distinguishable features. We compared our algorithm with representative methods using the SEED and SEED-IV datasets, and experimentally verified that our method outperforms other state-of-the-art approaches. The proposed method provides a more effective transfer learning pathway for EEG-based sentiment analysis under multi-source scenarios.

Traditional individual identification methods, such as face and fingerprint recognition, carry the risk of personal information leakage. The uniqueness and privacy of electroencephalograms (EEG) and the popularization of EEG acquisition devices have intensified research on EEG-based individual identification in recent years. However, most existing work uses EEG signals from a single session or emotion, ignoring large differences between domains. As EEG signals do not satisfy the traditional deep learning assumption that training and test sets are independently and identically distributed, it is difficult for trained models to maintain good classification performance for new sessions or new emotions. In this article, an individual identification method, called Multi-Loss Domain Adaptor (MLDA), is proposed to deal with the differences between marginal and conditional distributions elicited by different domains. The proposed method consists of four parts: a) Feature extractor, which uses deep neural networks to extract deep features from EEG data; b) Label predictor, which uses full-layer networks to predict subject labels; c) Marginal distribution adaptation, which uses maximum mean discrepancy (MMD) to reduce marginal distribution differences; d) Associative domain adaptation, which adapts to conditional distribution differences. Using the MLDA method, the cross-session and cross-emotion EEG-based individual identification problem is addressed by reducing the influence of time and emotion. Experimental results confirmed that the method outperforms other state-of-the-art approaches.

Induced electric fields (E-field) of a coil within target areas are substantial for precise trans cranial therapy. The fast and precise estimation of a stimulation is essential for a navigation system. However, high accuracy and low time consumption are rarely satisfied at the same time in previous models. In this paper, we present an anatomical-awareness model to integrate binary, explicit anatomical structures as mediate variables. Multi-scale attention blocks are also introduced to capture the anatomical variations. The presented model mitigates the anatomy-related errors. The presented architecture not only reduces the mean relative errors of E-field to about 7%, but also has the characteristics of low time consumption, which makes it suitable for a real-time navigation system.

BACKGROUND: Data from genome-wide association studies (GWASs) have been used to estimate the heritability of human complex traits in recent years. Existing methods are based on the linear mixed model, with the assumption that the genetic effects are random variables, which is opposite to the fixed effect assumption embedded in the framework of quantitative genetics theory. Moreover, heritability estimators provided by existing methods may have large standard errors, which calls for the development of reliable and accurate methods to estimate heritability. RESULTS: In this paper, we first investigate the influences of the fixed and random effect assumption on heritability estimation, and prove that these two assumptions are equivalent under mild conditions in the theoretical aspect. Second, we propose a two-stage strategy by first performing sparse regularization via cross-validated elastic net, and then applying variance estimation methods to construct reliable heritability estimations. Results on both simulated data and real data show that our strategy achieves a considerable reduction in the standard error while reserving the accuracy. CONCLUSIONS: The proposed strategy allows for a reliable and accurate heritability estimation using GWAS data. It shows the promising future that reliable estimations can still be obtained with even a relatively restricted sample size, and should be especially useful for large-scale heritability analyses in the genomics era.

Abstract As we know, the rhesus macaque as a nonhuman primate is quite similar to a human being in genetics. Moreover, it has become essential in animal model anatomy and physiology in many modern medicine research such as the cardiovascular and cerebrovascular diseases in the recent years. This paper describes a pipeline from the raw rhesus macaque brain magnetic resonance imaging data to intuitive 3D inspection of its layers. Brain extraction is an initial step for subsequent analyses, but most of the existing methods so far are designed for human brain, which does not work well with the rhesus macaque. Firstly, we propose a reliable and efficient method to extract the brain from magnetic resonance imaging data sets based on dividing the brain into blocks. Then, we design a trapezoid opacity transfer function based on Compute Unified Device Architecture (CUDA)‐based real‐time volume ray casting, which is dedicated to volume rendering to make the inspection more intuitive. Besides, for an immersive exploration in virtual reality benefits understanding the volumetric data sets, so we also design a layer filter for the segmented rhesus macaque brain data sets, which facilitates the inspection of the interior in Region of Interest (ROI) by an intuitive bimanual interaction via a Leap Motion sensor. Our experiments prove usability and efficiency.

By exploiting cross-information among multiple imaging data, multimodal fusion has often been used to better understand brain diseases. However, most current fusion approaches are blind, without adopting any prior information. There is increasing interest to uncover the neurocognitive mapping of specific clinical measurements on enriched brain imaging data; hence, a supervised, goal-directed model that employs prior information as a reference to guide multimodal data fusion is much needed and becomes a natural option. Here, we proposed a fusion with reference model called "multi-site canonical correlation analysis with reference + joint-independent component analysis" (MCCAR+jICA), which can precisely identify co-varying multimodal imaging patterns closely related to the reference, such as cognitive scores. In a three-way fusion simulation, the proposed method was compared with its alternatives on multiple facets; MCCAR+jICA outperforms others with higher estimation precision and high accuracy on identifying a target component with the right correspondence. In human imaging data, working memory performance was utilized as a reference to investigate the co-varying working memory-associated brain patterns among three modalities and how they are impaired in schizophrenia. Two independent cohorts (294 and 83 subjects respectively) were used. Similar brain maps were identified between the two cohorts along with substantial overlaps in the central executive network in fMRI, salience network in sMRI, and major white matter tracts in dMRI. These regions have been linked with working memory deficits in schizophrenia in multiple reports and MCCAR+jICA further verified them in a repeatable, joint manner, demonstrating the ability of the proposed method to identify potential neuromarkers for mental disorders.

Recently, many researchers have used graph theory to study the aberrant brain structures in Alzheimer's disease (AD) and have made great progress. However, the characteristics of the cortical network in Mild Cognitive Impairment (MCI) are still largely unexplored. In this study, the gray matter volumes obtained from magnetic resonance imaging (MRI) for all brain regions except the cerebellum were parcellated into 90 areas using the automated anatomical labeling (AAL) template to construct cortical networks for 98 normal controls (NCs), 113 MCIs and 91 ADs. The measurements of the network properties were calculated for each of the three groups respectively. We found that all three cortical networks exhibited small-world properties and those strong interhemispheric correlations existed between bilaterally homologous regions. Among the three cortical networks, we found the greatest clustering coefficient and the longest absolute path length in AD, which might indicate that the organization of the cortical network was the least optimal in AD. The small-world measures of the MCI network exhibited intermediate values. This finding is logical given that MCI is considered to be the transitional stage between normal aging and AD. Out of all the between-group differences in the clustering coefficient and absolute path length, only the differences between the AD and normal control groups were statistically significant. Compared with the normal controls, the MCI and AD groups retained their hub regions in the frontal lobe but showed a loss of hub regions in the temporal lobe. In addition, altered interregional correlations were detected in the parahippocampus gyrus, medial temporal lobe, cingulum, fusiform, medial frontal lobe, and orbital frontal gyrus in groups with MCI and AD. Similar to previous studies of functional connectivity, we also revealed increased interregional correlations within the local brain lobes and disrupted long distance interregional correlations in groups with MCI and AD.

Intuitively, higher intelligence might be assumed to correspond to more efficient information transfer in the brain, but no direct evidence has been reported from the perspective of brain networks. In this study, we performed extensive analyses to test the hypothesis that individual differences in intelligence are associated with brain structural organization, and in particular that higher scores on intelligence tests are related to greater global efficiency of the brain anatomical network. We constructed binary and weighted brain anatomical networks in each of 79 healthy young adults utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. Based on their IQ test scores, all subjects were divided into general and high intelligence groups and significantly higher global efficiencies were found in the networks of the latter group. Moreover, we showed significant correlations between IQ scores and network properties across all subjects while controlling for age and gender. Specifically, higher intelligence scores corresponded to a shorter characteristic path length and a higher global efficiency of the networks, indicating a more efficient parallel information transfer in the brain. The results were consistently observed not only in the binary but also in the weighted networks, which together provide convergent evidence for our hypothesis. Our findings suggest that the efficiency of brain structural organization may be an important biological basis for intelligence.

We investigated how to establish a realistic model for the evolutionary emergence of gene network that is scale-free. After theoretical analysis and compute simulation, we have demonstrated the evolutionary principles of gene network modeling: Preference attachment and random connection loss.

Fingerprint registration is a critical step in fingerprint matching. Although a variety of registration alignment algorithms have been proposed, accurate fingerprint registration remains an unresolved problem. We propose a new algorithm for fingerprint registration using orientation field. This algorithm finds the correct alignment by maximization of mutual information between features extracted from orientation fields of template and input fingerprint images. Orientation field, representing the flow of ridges, is a relatively stable global feature of fingerprint images. This method uses the statistics and distribution of global feature of fingerprint images so that it is robust to image quality and local changes in images. The primary characteristic of this method is that it uses this stable global feature to align fingerprints, and that its behavior may resemble the way humans compare fingerprints. Experimental results show that the occurrence of misalignment is dramatically reduced and that registration accuracy is greatly improved at the same time, leading to enhanced matching performance.

MOTIVATION: One popular method for analyzing functional connectivity between genes is to cluster genes with similar expression profiles. The most popular metrics measuring the similarity (or dissimilarity) among genes include Pearson's correlation, linear regression coefficient and Euclidean distance. As these metrics only give some constant values, they can only depict a stationary connectivity between genes. However, the functional connectivity between genes usually changes with time. Here, we introduce a novel insight for characterizing the relationship between genes and find out a proper mathematical model, variable parameter regression and Kalman filtering to model it. RESULTS: We applied our algorithm to some simulated data and two pairs of real gene expression data. The changes of connectivity in simulated data are closely identical with the truth and the results of two pairs of gene expression data show that our method has successfully demonstrated the dynamic connectivity between genes. CONTACT: [email protected].

Most methods for fMRI data analysis assume that the hemodynamic responses (HRs) across similar experimental events are same. This assumption is not appropriate when HRs vary unpredictably from trial to trial. Here, we introduce a new method for fMRI data analysis. The main features of the proposed method are as follows: 1) The trial-to-trial variability is modeled as meaningful signal rather than assuming that the same HR is evoked in each trial; 2) Since the proposed method is a constrained optimization based general framework, it could be extended by utilizing prior knowledge of HR; 3) The traditional deconvolution method can be included into our method as a special case. A comparison of performance on simulated fMRI datasets is made using the general linear model, the deconvolution method and the proposed method with receiver operating characteristic (ROC) methodology. In addition, we examined the effectiveness and usefulness of our method on real experimental data.

BACKGROUND: Subcellular localization of a new protein sequence is very important and fruitful for understanding its function. As the number of new genomes has dramatically increased over recent years, a reliable and efficient system to predict protein subcellular location is urgently needed. RESULTS: Esub8 was developed to predict protein subcellular localizations for eukaryotic proteins based on amino acid composition. In this research, the proteins are classified into the following eight groups: chloroplast, cytoplasm, extracellular, Golgi apparatus, lysosome, mitochondria, nucleus and peroxisome. We know subcellular localization is a typical classification problem; consequently, a one-against-one (1-v-1) multi-class support vector machine was introduced to construct the classifier. Unlike previous methods, ours considers the order information of protein sequences by a different method. Our method is tested in three subcellular localization predictions for prokaryotic proteins and four subcellular localization predictions for eukaryotic proteins on Reinhardt's dataset. The results are then compared to several other methods. The total prediction accuracies of two tests are both 100% by a self-consistency test, and are 92.9% and 84.14% by the jackknife test, respectively. Esub8 also provides excellent results: the total prediction accuracies are 100% by a self-consistency test and 87% by the jackknife test. CONCLUSIONS: Our method represents a different approach for predicting protein subcellular localization and achieved a satisfactory result; furthermore, we believe Esub8 will be a useful tool for predicting protein subcellular localizations in eukaryotic organisms.

BACKGROUND: Microarray experiments are becoming a powerful tool for clinical diagnosis, as they have the potential to discover gene expression patterns that are characteristic for a particular disease. To date, this problem has received most attention in the context of cancer research, especially in tumor classification. Various feature selection methods and classifier design strategies also have been generally used and compared. However, most published articles on tumor classification have applied a certain technique to a certain dataset, and recently several researchers compared these techniques based on several public datasets. But, it has been verified that differently selected features reflect different aspects of the dataset and some selected features can obtain better solutions on some certain problems. At the same time, faced with a large amount of microarray data with little knowledge, it is difficult to find the intrinsic characteristics using traditional methods. In this paper, we attempt to introduce a combinational feature selection method in conjunction with ensemble neural networks to generally improve the accuracy and robustness of sample classification. RESULTS: We validate our new method on several recent publicly available datasets both with predictive accuracy of testing samples and through cross validation. Compared with the best performance of other current methods, remarkably improved results can be obtained using our new strategy on a wide range of different datasets. CONCLUSIONS: Thus, we conclude that our methods can obtain more information in microarray data to get more accurate classification and also can help to extract the latent marker genes of the diseases for better diagnosis and treatment.

Image segmentation is an essential processing step for many image analysis applications. We propose a novel pixon-based adaptive scale method for image segmentation. The key idea of our approach is that a pixon-based image model is combined with a Markov random field (MRF) model under a Bayesian framework. We introduce a new pixon scheme that is more suitable for image segmentation than the "fuzzy" pixon scheme. The anisotropic diffusion equation is successfully used to form the pixons in our new pixon scheme. Experimental results demonstrate that our algorithm performs fairly well and computational costs decrease dramatically compared with the pixel-based MRF algorithm.

This article introduces a parallel genetic algorithm for 3D medical image analysis, which was applied in model based segmentation and multimodality image registration. The experimental results show that the proposed method is very encouraging and promising.

Active model-based segmentation has frequently been used in medical image processing with considerable success. Although the active model-based method was initially viewed as an optimization problem, most researchers implement it as a partial differential equation solution. The advantages and disadvantages of the active model-based method are distinct: speed and stability. To improve its performance, a parallel genetic algorithm-based active model method is proposed and applied to segment the lateral ventricles from magnetic resonance brain images. First, an objective function is defined. Then one instance surface was extracted using the finite-difference method-based active model and used to initialize the first generation of a parallel genetic algorithm. Finally, the parallel genetic algorithm is employed to refine the result. We demonstrate that the method successfully overcomes numerical instability and is capable of generating an accurate and robust anatomic descriptor for complex objects in the human brain, such as the lateral ventricles.

Many engineering problems can be formulated as optimization problems. It has become more and more important to develop an efficient global optimization technique for solving these problems. In this paper, we propose an evolutionary tabu search (ETS) for cell image segmentation. The advantages of genetic algorithms (GA) and TS algorithms are incorporated into the proposed method. More precisely, we incorporate "the survival of the fittest" from evolutionary algorithms into TS. The method has been applied to the segmentation of several kinds of cell images. The experimental results show that the new algorithm is a practical and effective one for global optimization; it can yield good, near-optimal solutions and has better convergence and robustness than other global optimization approaches.

The authors compare the performance of three typical and widely used optimization techniques for a specific MEG source localization problem. Firstly, they introduce a hybrid algorithm by combining genetic and local search strategies to overcome disadvantages of conventional genetic algorithms. Secondly, they apply the tabu search: a widely used optimization methods in combinational optimization and discrete mathematics, to source localization. To the best of the authors' knowledge, this is the first attempt in the literature to apply tabu search to MEG/EEG source localization. Thirdly, in order to further comparison of the performance of above algorithms, simulated annealing is also applied to MEG source localization problem. The computer simulation results show that the authors' local genetic algorithm is the most effective approach to dipole location.

In this paper, we propose a novel efficient method by incorporating a local genetic algorithm and a new pre-conditioning technique into Markov random field model for image restoration. The role of genetic algorithm is to improve the quality of restoration and the pre-conditioning technique aims at accelerating the convergence. The remarkable advantage of our approach is that restoring corrupted images and preserving the shape transitions in the restored results have been orchestrated very well. The experiments on 3D MR image show that our method work very well.

In this paper, we propose a novel pixon-based multiresolution method for image denoising. The key idea to our approach is that a pixon map is embedded into the MRF models under a Bayesian framework. The remarkable advantage of our approach over the existing works in this field is that restoring corrupted images and preserving the shape transitions in the restored results have been orchestrated very well. A simulated annealing algorithm is implemented to find the MAP solution. A lot of experiments illustrate that our method is much more effective and powerful in the noise reduction than the Wiener and median filtering techniques, two typical and widely used techniques.

In this paper, we propose a novel method for extracting the geometric primitives from geometric data. Specifically, we use tabu search to solve geometric primitive extraction problem. In the best of our knowledge, it is the first attempt that tabu search is used in computer vision. Our tabu search (TS) has a number of advantages: (1) TS avoids entrapment in local minima and continues the search to give a near-optimal final solution; (2) TS is very general and conceptually much simpler than either SA or GA; (3) TS is very easy to implement and the entire procedure only occupies a few lines of code; (4) TS is a flexible framework of a variety of strategies originating from artificial intelligence and is therefore open to further improvement.