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Tao Gong

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15 papers

2 author rows

AAAI Conference 2026 Conference Paper

Flora: Effortless Context Construction to Arbitrary Length and Scale

Tianxiang Chen
Zhentao Tan
Xiaofan Bo
Yue Wu
Tao Gong
Qi Chu
Jieping Ye

Effectively handling long contexts is challenging for Large Language Models (LLMs) due to the rarity of long texts, high computational demands, and substantial forgetting of short-context abilities. Recent approaches have attempted to construct long contexts for instruction tuning, but these methods often require LLMs or human interventions, which are both costly and limited in length and diversity. Also, the drop in short-context performances of present long-context LLMs remains significant. In this paper, we introduce Flora, an effortless (human/LLM-free) long-context construction strategy. Flora can markedly enhance the long-context performance of LLMs by arbitrarily assembling short instructions based on categories and instructing LLMs to generate responses based on long-context meta-instructions. This enables Flora to produce contexts of arbitrary length and scale with rich diversity, while only slightly compromising short-context performance. Experiments on Llama3-8B-Instruct and QwQ-32B show that LLMs enhanced by Flora excel in three long-context benchmarks while maintaining strong performances in short-context tasks.

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AAAI Conference 2026 Conference Paper

MagicPaint: Operate Anything for Image Inpainting with Diffusion Model

Qinhong Yang
DongDong Chen
Qi Chu
Tao Gong
Qiankun Liu
Zhentao Tan
Xulin Li
Huamin Feng

Recent diffusion-based models have significantly improved inpainting quality. However, existing methods struggle with multi-task inpainting due to conflicting optimization objectives, and current datasets are typically limited to task-specific scenarios, hindering joint training. To address these challenges, we propose MagicPaint, a unified diffusion-based inpainting model that supports object addition, removal, and unconditional inpainting across both text and image modalities. MagicPaint semantically decouples operation types and target content by learnable tokens in MMToken Module, effectively reconciling conflicting optimization objectives and enabling robust multi-task, multi-modal inpainting. Besides, a novel inpainting paradigm named MagicMask, encodes operating intent directly into the mask and applies a mask loss for spatially precise supervision. In addition, existing inpainting datasets are insufficient for multi-task and multi-modal scenarios, limiting the capability of inpainting models. Thus, we further introduce a new dataset comprising 2.1M image tuples. It is dedicatedly designed to support diverse inpainting scenarios and significantly improves upon existing datasets, particularly in object removal. Through efforts from both model and data perspectives, MagicPaint enables users to operate anything—add, remove or inpaint content which is specified through either text or image modalities in a seamless and unified manner. Extensive experiments demonstrate that MagicPaint achieves state-of-the-art performance across three key tasks (i.e., text-guided addition, image-guided addition, and object removal) and produces outputs with superior visual consistency and contextual fidelity compared to existing methods.

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JBHI Journal 2025 Journal Article

Cross-Correlation Rectification for Robust Deformable Registration of Brain Tumor MRI Between Preoperative and Postoperative Phases

Chongwei Wu
Tao Gong
Xiaoyu Zeng
Shuxian Niu
Guangbin Wang
Qiang Li
Zhiwei Wang

A key challenge in registering pre- and post-operative brain tumor images lies in the anatomical inconsistencies caused by pathological changes and surgical resections. Recent efforts have addressed this issue by masking affected regions during optimization, but such approaches discard contextual information and rely on CNN backbones that implicitly model deformation, often overfitting to distant normal tissues and failing to capture the severe nonlinear distortions near the tumor. Correlation-based alternatives enhance generalization to diverse deformation patterns by explicitly modeling geometric correspondences, yet they frequently yield unreliable matches in and around tumor regions, disrupting the deformation field. In this paper, we propose Cross-correlation Rectification-based Registration Network (CRRNet), the first framework that introduces an active rectification mechanism specifically for robustness and structurally coherent pre- to post-operative brain tumor image registration. Specifically, CRRNet achieves this through two complementary modules: 1) a Cross-correlation Analysis-Based Inconsistency module that identifies invalid correspondences via bidirectional loop-closure evaluation on cross-correlations, and 2) a Dual-level Correspondence Rectification module that adaptively integrates contextually reliable correlations from local and long-range perspectives to restore structurally coherent matches. This synergistic design retains the strengths of cross-correlation while effectively mitigating correspondence mismatches. Extensive experiments on multiple tumor benchmarks demonstrate the superiority of CRRNet. Specifically, on the BraTS-Reg dataset, it reduces the mean registration errors by 8. 18% in near-tumor regions and 3. 95% in far-from-tumor regions, surpassing state-of-the-art methods.

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IROS Conference 2025 Conference Paper

Region-Aware 6D Grasping for Industrial Bin-Picking: A Sim2Real Label Self-Generation and Hybrid Evaluation Framework

Xungao Zhong
Tao Gong
Xunyu Zhong
Qiang Liu 0003
Huosheng Hu

The integration of high-quality datasets, a generalized network model, and robust evaluation strategies sets a significant benchmark for advancing policy development in industrial bin-picking. This paper introduces the concept of region-aware grasping, a cutting-edge simulation to reality system designed to generate and evaluate 6D poses, empowering robots to grasp novel workpieces in stacked environments. The proposed system comprises two core components: the Sim2Real dataset, a large-scale synthetic point cloud dataset for grasp analysis, and Semantic-GraspNet, a policy framework that predicts full 6D grasp poses for stacked objects. By encoding and decoding point cloud data, Semantic-GraspNet innovatively transforms the pose prediction into a semantic categorization problem. Furthermore, we present a hybrid evaluation strategy that integrates pose assessment with mechanical grasp performance analysis, thereby enhancing both grasp success rates and sorting efficiency. To extend its capabilities, Semantic-GraspNet is combined with multi-modal large models, enabling accurate object-category-specific grasping in complex bin-picking scenarios. In real-world industrial applications, the system achieves a grasp completion rate of 91. 3% in cluttered scenes and 89. 2% in densely stacked environments, showcasing state-of-the-art performance in robotic picking and placing tasks.

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AAAI Conference 2025 Conference Paper

Rethinking Masked Data Reconstruction Pretraining for Strong 3D Action Representation Learning

Tao Gong
Qi Chu
Bin Liu
Nenghai Yu

In 3D human action recognition, limited supervised data makes it challenging to fully tap into the modeling potential of powerful networks such as transformers. As a result, researchers have been actively investigating effective self-supervised pre-training strategies. For example, MAMP shows that instead of following the prevalent masked joint reconstruction, explicit masked motion reconstruction is key to the success of learning effective feature representation for 3D action recognition. However, we find that if we make a simple and effective change to the reconstructed target of masked joint reconstruction, masked joint reconstruction can achieve the same results as masked motion reconstruction. The devil is in the special characteristic of 3D skeleton data and the normalization process of training targets. We need to dig for all effective information of targets during normalization. Besides, considering that mask data reconstruction focuses more on learning local relations in input data for fulfilling the reconstruction task, instead of modeling the relation among samples, we further employ contrastive learning to learn more discriminative 3D action representations. We show that contrastive learning can consistently boost the performance of model pre-trained by masked joint prediction under various settings, especially in the semi-supervised setting that has a very limited number of labeled samples. Extensive experiments on NTU-60, NTU-120, and PKU-MMD datasets show that the proposed pre-training strategy achieves state-of-the-art results without bells and whistles.

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IJCAI Conference 2025 Conference Paper

Towards Anytime Retrieval: A Benchmark for Anytime Person Re-Identification

Xulin Li
Yan Lu
Bin Liu
Jiaze Li
Qinhong Yang
Tao Gong
Qi Chu
Mang Ye

In real applications, person re-identification (ReID) expects to retrieve the target person at any time, including both daytime and nighttime, ranging from short-term to long-term. However, existing ReID tasks and datasets cannot meet this requirement, as they are constrained by available time and only provide training and evaluation for specific scenarios. Therefore, we investigate a new task called Anytime Person Re-identification (AT-ReID), which aims to achieve effective retrieval in multiple scenarios based on variations in time. To address the AT-ReID problem, we collect the first large-scale dataset, AT-USTC, which contains 135k images of individuals wearing multiple clothes captured by RGB and IR cameras. Our data collection spans over an entire year and 270 volunteers were photographed on average 29. 1 times across different dates or scenes, 4-15 times more than current datasets, providing conditions for follow-up investigations in AT-ReID. Further, to tackle the new challenge of multi-scenario retrieval, we propose a unified model named Uni-AT, which comprises a multi-scenario ReID (MS-ReID) framework for scenario-specific features learning, a Mixture-of-Attribute-Experts (MoAE) module to alleviate inter-scenario interference, and a Hierarchical Dynamic Weighting (HDW) strategy to ensure balanced training across all scenarios. Extensive experiments show that our model leads to satisfactory results and exhibits excellent generalization to all scenarios.

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AAAI Conference 2025 Conference Paper

Training-free Open-Vocabulary Semantic Segmentation via Diverse Prototype Construction and Sub-region Matching

Xuanpu Zhao
Dianmo Sheng
Zhentao Tan
Zhiwei Zhao
Tao Gong
Qi Chu
Bin Liu
Nenghai Yu

Open-vocabulary semantic segmentation (OVSS) aims to segment images of arbitrary categories specified by class labels. While previous approaches relied on extensive image-text pairs or dense semantic annotations, recent training-free methods attempted to overcome these limitations by constructing semantic prototypes in the construction stage and image-to-image matching (i.e., prototype matching) during testing. However, these methods often struggle to effectively capture the visual characteristics of categories and fail to utilize local features during prototype matching. To deal with these problems, we propose a novel training-free framework for OVSS that constructs diverse prototypes and performs fine-grained sub-region matching. Specifically, our method leverages Large Language Models (LLMs) to guide support image generation by descriptions of different attributes of categories and employs coarse-fine clustering to obtain diverse and robust part-level prototypes in the construction stage. During testing, we propose a sub-region matching method, which assigns part-level prototypes to sub-regions utilizing optimal transport, to fully utilize local image features among part-level prototypes. Extensive experiments demonstrate the effectiveness of our method and show that our method achieves state-of-the-art performance, outperforming previous methods across five datasets.

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YNICL Journal 2024 Journal Article

Unveiling MRI markers for Parkinson’s Disease: GABAergic dysfunction and cortical changes

Yuan Tian
Sijia Geng
Tianyi Liu
Qi Wang
Jianxiu Lian
Liangjie Lin
Jiayu Li
Tao Gong

OBJECTIVE: The study aimed to investigate changes in basal levels of the inhibitory γ-aminobutyric acid (GABA) neurotransmitter in the sensorimotor cortex (SMC) and cortical gyrification in patients with Parkinson's disease (PD), which could further identify potential imaging biomarkers for PD, particularly in patients with early-onset Parkinson's disease (EOPD). METHOD: Fifty patients with PD (EOPD: 10, late-onset Parkinson's disease [LOPD]: 40) and fifty-two age- and gender-matched healthy controls (HC) underwent GABA-edited 1H MRS of the SMC and high-resolution 3D T1-weighted brain imaging. GABA levels and local gyrification index (LGI) were calculated to assess GABAergic and cortical gyrification deficits in PD. RESULT: The Pearson correlation coefficients revealed significant negative associations between eight indicators, including GABA/Cr level and local gyrification index (LGI) of specific cortical regions (precentral, postcentral, entorhinal, superiortemporal, posteriorcingulate, cuneus, and transversetemporal cortex), and the likelihood of Parkinson's disease (r < -0.4, p < 0.001). Additionally, GABA levels were significantly lower in the SMC region of both EOPD and LOPD patients compared to healthy controls (mean ± SD [u.i.]: EOPD=0.081 ± 0.022 vs. Young-HC=0.112 ± 0.021, p = 0.003; LOPD=0.054 ± 0.024 vs. Old-HC=0.099 ± 0.021, p < 0.001). The logistic regression model was established by using multivariate analysis, identifying two statistically significant indicators: GABA/Cr and LGI of the transversetemporal. The combined model exhibited the highest AUC values in both younger and older populations. CONCLUSION: GABAergic dysfunction may play an important role in the pathogenesis of PD patients. Changes in neurotransmitter and morphological may serve as potential markers for the preclinical diagnosis and progression of PD, including EOPD.

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YNIMG Journal 2022 Journal Article

Neurometabolic timecourse of healthy aging

Tao Gong
Steve C.N. Hui
Helge J. Zöllner
Mark Britton
Yulu Song
Yufan Chen
Aaron T. Gudmundson
Kathleen E. Hupfeld

PURPOSE: The neurometabolic timecourse of healthy aging is not well-established, in part due to diversity of quantification methodology. In this study, a large structured cross-sectional cohort of male and female subjects throughout adulthood was recruited to investigate neurometabolic changes as a function of age, using consensus-recommended magnetic resonance spectroscopy quantification methods. METHODS: 102 healthy volunteers, with approximately equal numbers of male and female participants in each decade of age from the 20s, 30s, 40s, 50s, and 60s, were recruited with IRB approval. MR spectroscopic data were acquired on a 3T MRI scanner. Metabolite spectra were acquired using PRESS localization (TE=30 ms; 96 transients) in the centrum semiovale (CSO) and posterior cingulate cortex (PCC). Water-suppressed spectra were modeled using the Osprey algorithm, employing a basis set of 18 simulated metabolite basis functions and a cohort-mean measured macromolecular spectrum. Pearson correlations were conducted to assess relationships between metabolite concentrations and age for each voxel; Spearman correlations were conducted where metabolite distributions were non-normal. Paired t-tests were run to determine whether metabolite concentrations differed between the PCC and CSO. Finally, robust linear regressions were conducted to assess both age and sex as predictors of metabolite concentrations in the PCC and CSO and separately, to assess age, signal-noise ratio, and full width half maximum (FWHM) linewidth as predictors of metabolite concentrations. RESULTS: Data from four voxels were excluded (2 ethanol; 2 unacceptably large lipid signal). Statistically-significant age*metabolite Pearson correlations were observed for tCho (r(98)=0.33, p 0.20). Age associations for tCho, tCr, mI and sI in the CSO and for NAAG, tCho, and tCr in the PCC remained when controlling for sex in robust regressions. CSO NAAG and Asp, as well as PCC tNAA, sI, and Lac were higher in women; PCC Gln was higher in men. When including an age*sex interaction term in robust regression models, a significant age*sex interaction was seen for tCho (F(1,96)=11.53, p=0.001) and GSH (F(1,96)=7.15, p=0.009) in the CSO and tCho (F(1,96)=9.17, p=0.003), tCr (F(1,96)=9.59, p=0.003), mI (F(1,96)=6.48, p=0.012), and Lac (F(1,78)=6.50, p=0.016) in the PCC. In all significant interactions, metabolite levels increased with age in females, but not males. There was a significant positive correlation between linewidth and age. Age relationships with tCho, tCr, and mI in the CSO and tCho, tCr, mI, and sI in the PCC were significant after controlling for linewidth and FWHM in robust regressions. CONCLUSION: The primary (correlation) results indicated age relationships for tCho, tCr, mI, and sI in the CSO and for NAAG, tCho, tCr, and Gln in the PCC, while no age correlations were found for tNAA, NAA, Glx, Glu, GSH, PE, Lac, or Asp in either region. Our results provide a normative foundation for future work investigating the neurometabolic time course of healthy aging using MRS.

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YNICL Journal 2021 Journal Article

Focal corticarl dysplasia in epilepsy is associated with GABA increase

Tao Gong
Yubo Liu
Yufan Chen
Liangjie Lin
Youting Lin
Guangbin Wang

PURPOSE: Focal cortical dysplasia (FCD) is a major cause of drug-resistant epilepsy; however the underlying epileptogenic mechanisms of FCD metabolism in epilepsy patients remain unclear. The aim of this study is to detect alterations of γ-aminobutyric acid (GABA), glutathione (GSH), and the composite of glutamate and glutamine (Glx) in MRI-typical and neuropathologically confirmed FCD-associated epilepsy using Hadamard Encoding and Reconstruction of Mega-Edited Spectroscopy (HERMES). MATERIALS AND METHODS: Fourteen epileptic patients suspected to be caused by FCD and 14 healthy controls were enrolled prospectively in this study; all subjects underwent a 3 T MRI scan, including 3D T1 weighted imaging and HERMES. The GABA signal detected by HERMES also contains signals from macromolecules and homocarnosine, so it is referred as GABA+. Signals of GABA+, GSH and Glx detected by HERMES from tumor foci, contralateral cerebral regions, and healthy controls were quantified using Gannet. Fitting errors and signal to noise ratios (SNRs) of GABA + signals were also recorded. Differences of GABA+, GSH, Glx, fitting error and SNR of GABA + among three groups were analyzed using linear mixed effects models. RESULTS: Twelve FCD-associated epilepsy patients (7 females, aged 21.9 ± 9.3 years) and 12 matched healthy controls (7 females, aged 22.8 ± 9.8 years) were finally enrolled in this study. ANOVA results indicated that GABA levels were significantly increased in FCD foci compared with contralateral regions (p = 0.008) and with healthy controls (p = 0.003), while no difference was found in GSH and Glx levels. No difference of fitting errors or SNR of GABA + was found among FCD foci, contralateral regions and healthy controls. CONCLUSIONS: Increased GABA levels were found in FCD foci that indicated GABA may play a central role in the pathophysiology of FCD patients with epilepsy.

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YNIMG Journal 2021 Journal Article

Frequency drift in MR spectroscopy at 3T

Steve C.N. Hui
Mark Mikkelsen
Helge J. Zöllner
Vishwadeep Ahluwalia
Sarael Alcauter
Laima Baltusis
Deborah A. Barany
Laura R. Barlow

PURPOSE: field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites. METHOD: A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC). RESULTS: Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p < 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI. DISCUSSION: This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed.

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AAAI Conference 2021 Conference Paper

Temporal ROI Align for Video Object Recognition

Tao Gong
Kai Chen
Xinjiang Wang
Qi Chu
Feng Zhu
Dahua Lin
Nenghai Yu
Huamin Feng

Video object detection is challenging in the presence of appearance deterioration in certain video frames. Therefore, it is a natural choice to aggregate temporal information from other frames of the same video into the current frame. However, ROI Align, as one of the most core procedures of video detectors, still remains extracting features from a single-frame feature map for proposals, making the extracted ROI features lack temporal information from videos. In this work, considering the features of the same object instance are highly similar among frames in a video, a novel Temporal ROI Align operator is proposed to extract features from other frames feature maps for current frame proposals by utilizing feature similarity. The proposed Temporal ROI Align operator can extract temporal information from the entire video for proposals. We integrate it into single-frame video detectors and other state-of-the-art video detectors, and conduct quantitative experiments to demonstrate that the proposed Temporal ROI Align operator can consistently and significantly boost the performance. Besides, the proposed Temporal ROI Align can also be applied into video instance segmentation.

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YNICL Journal 2020 Journal Article

Brain GABA+ changes in primary hypothyroidism patients before and after levothyroxine treatment: A longitudinal magnetic resonance spectroscopy study

Bo Liu
Zhensong Wang
Liangjie Lin
Huan Yang
Fei Gao
Tao Gong
Richard A.E. Edden
Guangbin Wang

OBJECTIVE: Increasing evidence indicates the involvement of the GABAergic system in the pathophysiology of hypothyroidism. We aimed to investigate longitudinal changes of brain GABA in primary hypothyroidism before and after levothyroxine (L-T4) treatment. MATERIAL AND METHODS: In 18 patients with hypothyroidism, we used the MEGA-PRESS (Mescher-Garwood point-resolved spectroscopy) editing sequence to measure brain GABA levels from medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC) at baseline and after 6-months of L-T4 treatment. Sex- and age-matched healthy controls (n = 18) were scanned at baseline. Thyroid function and neuropsychological tests were also performed. RESULTS: GABA signals were successfully quantified from all participants with fitting errors lower than 15%. GABA signal was labeled as GABA+ due to contamination from co-edited macromoleculars and homocarnosine. In hypothyroid patients, mean GABA+ was significantly lower in the mPFC region compared with controls (p = 0.031), and the mPFC GABA+ measurements were significantly correlated with depressive symptoms and memory function (r = -0.558, p = 0.016; r = 0.522, p = 0.026, respectively). After adequate L-T4 treatment, the mPFC GABA+ in hypothyroid patients increased to normal level, along with relieved neuropsychological impairments. CONCLUSION: The study suggested the decrease of GABA+ may be an important neurobiological factor in the pathophysiology of hypothyroidism. Treatment of L-T4 may reverse the abnormal GABA+ and hypothyroidism-induced neuropsychiatric impairments, indicating the action mode of L-T4 in adjunctive treatment of affective disorders.

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EAAI Journal 2017 Journal Article

GPU-based parallel optimization of immune convolutional neural network and embedded system

Tao Gong
Tiantian Fan
Jizheng Guo
Zixing Cai

Up to now, the image recognition system has been utilized more and more widely in the security monitoring, the industrial intelligent monitoring, the unmanned vehicle, and even the space exploration. In designing the image recognition system, the traditional convolutional neural network has some defects such as long training time, easy over-fitting and high misclassification rate. In order to overcome these defects, we firstly used the immune mechanism to improve the convolutional neural network and put forward a novel immune convolutional neural network algorithm, after we analyzed the network structure and parameters of the convolutional neural network. Our algorithm not only integrated the location data of the network nodes and the adjustable parameters, but also dynamically adjusted the smoothing factor of the basis function. In addition, we utilized the NVIDIA GPU (Graphics Processing Unit) to accelerate the new immune convolutional neural network (ICNN) in parallel computing and built a real-time embedded image recognition system for this ICNN. The immune convolutional neural network algorithm was improved with CUDA programming and was tested with the sample data in the GPU-based environment. The GPU-based implementation of the novel immune convolutional neural network algorithm was made with the cuDNN, which was designed by NVIDIA for GPU-based accelerating of DNNs in machine learning. Experimental results show that our new immune convolutional neural network has higher recognition rate, more stable performance and faster computing speed than the traditional convolutional neural network.

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EAAI Journal 2017 Journal Article

Magnetic resonance imaging-clonal selection algorithm: An intelligent adaptive enhancement of brain image with an improved immune algorithm

Tao Gong
Tiantian Fan
Lei Pei
Zixing Cai

Artificial Immune System is used nowadays to solve complex problems, including medical problems. To overcome some flaws of traditional clonal selection algorithm in medical imaging applications, a novel clonal selection algorithm in intelligent adaptive enhancement design of magnetic resonance imaging (MRI) brain images is proposed. It is called the MRI-Clonal Selection Algorithm (MRI-CSA). The MRI-CSA uses three improvements for the Clonal Selection Algorithm. Firstly, instead of the simple binary coding, the real coding approach of the MRI brain image is designed. Secondly, the mutation distance is added into the mutation operator to better control the mutation progress and avoid any narrow local optimization. Finally, both the clone selection and the mutation are adjusted together in the Gauss distribution, the uniform distribution, and the chaotic distribution, rather than in only the Gauss distribution. In addition, the real MRI brain images are used in the image enhancement testing with our improved clonal selection algorithm. The experimental results show that the proposed approach outperforms the median filtering (MF) and the adaptive template filtering (ATF) in enhancing the MRI brain images.

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