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Infrared non-uniformity correction algorithm based on classification and regression tree segmentation

OPTICAL ENGINEERING 2025年第1期64卷

作者： Qu, Wen-qing Ma, Hao-ran Wei, Jiang-yuan Ning, Yu Li, Jia-ming Ge, Kun Zhang, Hong-fei Wang, Jian Univ Sci & Technol China Dept Modern Phys State Key Lab Particle Detect & Elect Hefei Peoples R China Deep Space Explorat Lab Hefei Peoples R China Univ Sci & Technol China Sch Microelect Hefei Peoples R China Univ Sci & Technol China Inst Adv Technol Hefei Peoples R China

In infrared imaging, pixel non-uniformity due to manufacturing and technological limitations significantly degrades image quality, posing a critical challenge for high-performance applications. This issue is especially pronounced in the field of infrared astronomical observation, where the scientific integrity of the data must be ensured when correcting infrared images. In addition, the coupling capacitance between pixels leads to nonlinear effects in pixel sensitivity. To address these challenges, a non-uniformity correction (NUC) algorithm was introduced that utilizes classification and regression tree segmentation. This approach enables precise corrections by adapting to varying illuminance levels, a capability not fully explored in existing solutions. In our method, we innovatively segment the pixel response curves into distinct low and high illuminance ranges and apply customized corrections for each segment to enhance the accuracy of correction. Evaluations using real image data demonstrate that our method enhances image quality and consistency. (c) 2025 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： infrared detector non-uniformity correction classification and regression tree

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Exploring Forest Soils Variability Based on Physicochemical Properties Using Correspondence Analysis and classification and regression tree for Forensic Provenance Analysis

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JOURNAL OF ANALYTICAL CHEMISTRY 2025年第1期80卷 182-194页

作者： Abd Hamid, Nadirah Baba, Muhamad Hilmi Sino, Hukil Halim, Azhar Abdul Lee, Loong Chuen Univ Kebangsaan Malaysia Fac Hlth Sci Forens Sci Program CODTIS Bangi 43600 Selangor Darul Malaysia Univ Kebangsaan Malaysia Fac Sci & Technol Dept Earth Sci & Environm Bangi 43600 Selangor Darul Malaysia

Soils on forest lands are likely characterized by high organic matter content and a diversity of nutrients. Thus, differentiating forest soils from different sites of origin is a challenging task. This study aims to assess the variability of 163 forest soils based on two easily measured physiochemical properties: soil color and pH. The soil samples were collected from 11 different sites across six states in Malaysia. Soil color was recorded under both air-dry and moistened conditions based on the Munsell Soil Color Chart. Meanwhile, soil pH was determined using a soil-to-water ratio of 1 : 20 (w/v). The association between the source origins and the physiochemical properties of the soils was assessed using simple correspondence analysis. classification of the soils by the type of anthropogenic activities, according to pH, was further modeled using the classification and regression tree algorithm. The results showed that soils from ecotourism sites tended to be less acidic than those from reserved forest sites. Meanwhile, soils from forest lands used for hiking showed acidity levels between the two types of sites. On the other hand, variability in soil color by the source origin was found to be insignificant. In conclusion, forest soils could be further classified according to the type of anthropogenic activities, which are partly governed by the type and magnitude of human activities.

关键词： forensic geoscience physiochemical properties Malaysian soil simple correspondence analysis classification and regression tree anthropogenic activities

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classification and regression tree for estimating predictive markers to detect T790M mutations after acquired resistance to first line EGFR-TKI: HOPE-002

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INVESTIGATIONAL NEW DRUGS 2022年第2期40卷 361-369页

作者： Tamiya, Motohiro Fujikawa, Kei Suzuki, Hidekazu Yokoyama, Toshihide Uenami, Takeshi Tamiya, Akihiro Sato, Yuki Saito, Go Uchida, Junji Morita, Mitsunori Hirashima, Tomonori Fukuda, Yasushi Kanazu, Masaki Hosoya, Kazutaka Suzuki, Takuji Ueno, Kiyonobu Fujimoto, Daichi Kumagai, Toru Teramukai, Satoshi Osaka Int Ctr Inst Dept Thorac Oncol Osaka Japan Kyoto Prefectural Univ Med Grad Sch Med Sci Dept Biostat Kyoto Japan Osaka Habikino Med Ctr Dept Thorac Malignancy Osaka Japan Kurashiki Cent Hosp Dept Resp Med Okiyama Japan Natl Hosp Org Dept Thorac Oncol Osaka Toneyama Med Ctr Osaka Japan Natl Hosp Org Dept Internal Med Kinki Chuo Chest Med Ctr Osaka Japan Kobe City Med Ctr Gen Hosp Dept Resp Med Kobe Hyogo Japan Chiba Univ Grad Sch Med Dept Respirol Chiba Japan Osaka Gen Med Ctr Dept Resp Med Osaka Japan

Background and objective. Osimertinib as first-line treatment for patients with non-small cell lung cancer (NSCLC) harboring epidermal growth factor (EGFR) mutations remains controversial. Sequential EGFR-tyrosine kinase inhibitor (TKI) might be superior to the first line osimertinib in patients at risk of developing acquired T790M mutations. Methods. We enrolled consecutive patients with EGFR-mutated (deletion 19 or L858R) advanced NSCLC treated with first-line drugs and evaluated predictive markers using classification and regression tree (CART) for the detection of T790M mutations based on patient backgrounds prior to initial treatment. Results. Patients without acquired T790M mutations had worse outcomes than those with T790M mutations (median OS: 798 days vs. not reached;HR: 2.70;P < 0.001). CART identified three distinct groups based on variables associated with acquired T790M mutations (age, CYF, WBC, liver metastasis, and LDH;AUROC: 0.77). Based on certain variables, CART identified three distinct groups in deletion 19 (albumin, LDH, bone metastasis, pleural effusion, and WBC;AUROC: 0.81) and two distinct groups in L858R (age, CEA, and ALP;AUROC: 0.80). The T790M detection frequencies after TKI resistance of afatinib and first-generation EGFR-TKIs were similar (35.3% vs. 37.4%, P = 0.933). Afatinib demonstrated longer PFS (398 vs. 279 days;HR: 0.67;P = 0.004) and OS (1053 vs. 956 days;HR: 0.68;P = 0.051) than first-generation EGFR-TKIs. Conclusion. Identification of patients at risk of acquiring T790M mutations after EGFR-TKI failure may aid in choice of first-line EGFR-TKI. Furthermore, afatinib may be the more effective 1st-line EGFR-TKI treatment for patients at risk of developing T790M as initial EGFR-TKI resistance.

关键词： Non-small cell lung cancer EGFR Tyrosine kinase inhibitors T790M Predict marker classification and regression tree

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classification and regression tree (CART) model to assist clinical prediction for tracheostomy in patients with traumatic cervical spinal cord injury: a 7-year study of 340 patients

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EUROPEAN SPINE JOURNAL 2022年第5期31卷 1283-1290页

作者： Sun, Dawei Zhao, Hanqing Zhang, Zhengfeng Army Mil Med Univ Xinqiao Hosp Dept Orthoped 183 Xinqiao St Chongqing 400037 Peoples R China Xuzhou Med Univ Affiliated Huaihai Hosp Xuzhou Jiangsu Peoples R China

Objective To develop a classification and regression tree (CART) model to predict the need of tracheostomy in patients with traumatic cervical spinal cord injury (TCSCI) and to quantify scores of risk factors to make individualized clinical assessments. Methods The clinical characteristics of patients with TCSCI admitted to our hospital from January 2014 to December 2020 were retrospectively analyzed. The demographic characteristics (gender, age, smoking history), mechanism of injury, injury characteristics (ASIA impairment grades, neurological level of impairment, injury severity score), preexisting lung disease and preexisting medical conditions were statistically analyzed. The risk factors of tracheostomy were analyzed by univariate logistic regression analysis (ULRA) and multiple logistic regression analysis (MLRA). The CART model was established to predict tracheostomy. Results Three hundred and forty patients with TCSCI met the inclusion criteria, in which 41 patients underwent the tracheostomy. ULRA and MLRA showed that age > 50, ISS > 16, NLI > C5 and AIS A were significantly associated with tracheostomy. The CART model showed that AIS A and NLI > C5 were at the first and second decision node, which had a significant influence on the decision of tracheostomy. The final scores for tracheostomy from CART algorithm, composed of age, ISS, NLI and AIS A with a sensitivity of 0.78 and a specificity of 0.96, could also predict tracheostomy. Conclusion The establishment of CART model provided a certain clinical guidance for the prediction of tracheostomy in TCSCI. Quantifications of risk factors enable accurate prediction of individual patient risk of need for tracheostomy.

关键词： Cervical spinal cord injury Tracheostomy Trauma Risk factors classification and regression tree

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classification and regression tree analysis vs. multivariable linear and logistic regression methods as statistical tools for studying haemophilia

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HAEMOPHILIA 2015年第6期21卷 715-722页

作者： Henrard, S. Speybroeck, N. Hermans, C. Inst Catalisis & Petroleoquim Inst Hlth & Soc IRSS B-1200 Brussels Belgium Clin Univ St Luc Div Haematol Haemostasis Thrombosis Unit B-1200 Brussels Belgium

IntroductionHaemophilia is a rare genetic haemorrhagic disease characterized by partial or complete deficiency of coagulation factor VIII, for haemophilia A, or IX, for haemophilia B. As in any other medical research domain, the field of haemophilia research is increasingly concerned with finding factors associated with binary or continuous outcomes through multivariable models. Traditional models include multiple logistic regressions, for binary outcomes, and multiple linear regressions for continuous outcomes. Yet these regression models are at times difficult to implement, especially for non-statisticians, and can be difficult to interpret. AimsThe present paper sought to didactically explain how, why, and when to use classification and regression tree (CART) analysis for haemophilia research. Materials & methodsThe CART method is non-parametric and non-linear, based on the repeated partitioning of a sample into subgroups based on a certain criterion. Breiman developed this method in 1984. classification trees (CTs) are used to analyse categorical outcomes and regression trees (RTs) to analyse continuous ones. ResultsThe CART methodology has become increasingly popular in the medical field, yet only a few examples of studies using this methodology specifically in haemophilia have to date been published. Two examples using CART analysis and previously published in this field are didactically explained in details. ConclusionThere is increasing interest in using CART analysis in the health domain, primarily due to its ease of implementation, use, and interpretation, thus facilitating medical decision-making. This method should be promoted for analysing continuous or categorical outcomes in haemophilia, when applicable.

关键词： classification and regression tree haemophilia multivariable analysis non-parametric statistics random forest statistics

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classification and regression tree analysis of acute-on-chronic hepatitis B liver failure: Seeing the forest for the trees

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JOURNAL OF VIRAL HEPATITIS 2017年第2期24卷 132-140页

作者： Shi, K. -Q. Zhou, Y. -Y. Yan, H. -D. Li, H. Wu, F. -L. Xie, Y. -Y. Braddock, M. Lin, X. -Y. Zheng, M. -H. Wenzhou Med Univ Affiliated Hosp 1 Liver Res Ctr Dept Hepatol Wenzhou Peoples R China Wenzhou Med Univ Inst Hepatol Wenzhou Peoples R China Jinhua Municipal Hosp Dept Cardiol Jinhua Peoples R China Ningbo 2 Hosp Dept Infect Dis Ningbo Zhejiang Peoples R China Tianjin Infect Dis Hosp Dept Intens Care Unit Tianjin Peoples R China Wenzhou Med Univ Affiliated Hosp 1 Dept Clin Lab Wenzhou Peoples R China AstraZeneca R&D Global Med Dev Loughborough Leics England

At present, there is no ideal model for predicting the short-term outcome of patients with acute-on-chronic hepatitis B liver failure (ACHBLF). This study aimed to establish and validate a prognostic model by using the classification and regression tree (CART) analysis. A total of 1047 patients from two separate medical centres with suspected ACHBLF were screened in the study, which were recognized as derivation cohort and validation cohort, respectively. CART analysis was applied to predict the 3-month mortality of patients with ACHBLF. The accuracy of the CART model was tested using the area under the receiver operating characteristic curve, which was compared with the model for end-stage liver disease (MELD) score and a new logistic regression model. CART analysis identified four variables as prognostic factors of ACHBLF: total bilirubin, age, serum sodium and INR, and three distinct risk groups: low risk (4.2%), intermediate risk (30.2%-53.2%) and high risk (81.4%-96.9%). The new logistic regression model was constructed with four independent factors, including age, total bilirubin, serum sodium and prothrombin activity by multivariate logistic regression analysis. The performances of the CART model (0.896), similar to the logistic regression model (0.914, P=.382), exceeded that of MELD score (0.667, P<.001). The results were confirmed in the validation cohort. We have developed and validated a novel CART model superior to MELD for predicting three-month mortality of patients with ACHBLF. Thus, the CART model could facilitate medical decision-making and provide clinicians with a validated practical bedside tool for ACHBLF risk stratification.

关键词： acute-on-chronic hepatitis B liver failure classification and regression tree cohort study model for end-stage liver disease prognostic prediction

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Estimation of forest attributes in the Hyrcanian forests, comparison of advanced space-borne thermal emission and reflection radiometer and satellite poure I'observation de la terre-high resolution grounding data by multiple linear, and classification and regression tree regression models

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JOURNAL OF APPLIED REMOTE SENSING 2014年第1期8卷 083632-1-083632-17页

作者： Kalbi, Siavash Fallah, Asghar Shataee, Shaban Sari Agr & Nat Resource Univ Dept Forestry Sari 578 Iran Gorgan Agr & Nat Resource Gorgan 386 Iran

Estimation of structural forest attributes, such as volume, basal area, and tree density using a combination of remote sensing and field data, is currently considered a favored option compared to only using field survey data. In a comparative study, multiple linear regression (MLR) and classification and regression tree (CART) models were used to estimate volume, basal area, and tree density using advanced space-borne thermal emission and reflection radiometer (ASTER) and satellite poure I'observation de la terre (SPOT)-high resolution grounding (HRG) imagery in the Darabkola forests, located at the Hyrcanian region of Iran. Results showed that the CART model using SPOT-HRG data achieved the best overall performance for all three forest structural attributes, with adjusted R-2 - 0.746 and RMSE - 67.9 m(3) ha(-1) for volume, adjusted R-2 = 0.771 and RMSE = 3.94 m(2) ha(-1) for basal area, and adjusted R-2 = 0.871 and RMSE = 34.71 nha(-1) for tree density. In general, the CART model, using both ASTER and SPOT-HRG data, produced better estimates of forest attributes compared to the MLR model. In addition, results showed that forest attribute estimations using SPOT-HRG were better than those obtained from ASTER data. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： Hyrcanian forests forest attributes advanced space-borne thermal emission and reflection radiometer satellite poure I'observation de la terre-high resolution grounding classification and regression tree linear regression

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Diagnosis of gastric cancer using near-infrared Raman spectroscopy and classification and regression tree techniques

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JOURNAL OF BIOMEDICAL OPTICS 2008年第3期13卷 34013-1-34013-8页

作者： Teh, Seng Khoon Zheng, Wei Ho, Khelk Yu Teh, Ming Yeoh, Khay Guan Huang, Zhiwei Natl Univ Singapore Fac Engn Dept Bioengn Bioimaging Lab Singapore 117576 Singapore Natl Univ Singapore Hosp Dept Med Yoo Loo Lin Sch Med Singapore 119260 Singapore

The purpose of this study is to apply near-infrared (NIR) Raman spectroscopy and classification and regression tree (CART) techniques for identifying molecular changes of tissue associated with cancer transformation. A rapid-acquisition NIR Raman system is utilized for tissue Raman spectroscopic measurements at 785-nm excitation. 73 gastric tissue samples (55 normal, 18 cancer) from 53 patients are measured. The CART technique is introduced to develop effective diagnostic algorithms for classification of Raman spectra of different gastric tissues. 80% of the Raman dataset are randomly selected for spectral learning, while 20% of the dataset are reserved for validation. High-quality Raman spectra in the range of 800 to 1800 cm(-1) are acquired from gastric tissue within 5 s. The diagnostic sensitivity and specificity of the learning dataset are 90.2 and 95.7%;and the predictive sensitivity and specificity of the independent validation dataset are 88.9 and 92.9%, respectively, for separating cancer from normal. The tissue Raman peaks at 875 and 1745 cm-1 are found to be two of the most significant features to discriminate gastric cancer from normal tissue. NIR Raman spectroscopy in conjunction with the CART technique has the potential to provide an effective and accurate diagnostic means for cancer detection in the gastric system. (C) 2008 society of Photo-Optical Instrumentation Engineers.

关键词： cancer diagnosis near-infrared Raman spectroscopy stomach classification and regression tree

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A comparative study of land subsidence susceptibility mapping of Tasuj plane, Iran, using boosted regression tree, random forest and classification and regression tree methods

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ENVIRONMENTAL EARTH SCIENCES 2020年第10期79卷 1-12页

作者： Ebrahimy, Hamid Feizizadeh, Bakhtiar Salmani, Saeed Azadi, Hossein Shahid Beheshti Univ Fac Earth Sci Remote Sensing & GIS Res Ctr Tehran 653641255 Iran Univ Tabriz Dept Remote Sensing & GIS Tabriz Iran Univ Tabriz Inst Environm Tabriz Iran Univ Ghent Dept Geog Ghent Belgium Univ Hamburg Inst Geog Res Grp Climate Change & Secur Hamburg Germany Czech Univ Life Sci Prague Fac Environm Sci Prague Czech Republic

Land subsidence occurrence in the Tasuj plane is becoming more frequent and hazardous in the near future due to the water crisis. To mitigate damage caused by land subsidence events, it is necessary to determine the susceptible or prone areas. This study focuses on producing and comparing land subsidence susceptibility map (LSSM) using boosted regression tree (BRT), random forest (RF), and classification and regression tree (CART) approaches with twelve influencing variables, namely altitude, slope angle, aspect, groundwater level, groundwater level change, land cover, lithology, distance to fault, distance to stream, stream power index, topographic wetness index, and plan curvature. Moreover, by implementing the Relief-F feature selection method, the most important variables in LSSM procedure were identified. The performance of the adopted methods was assessed using the area under the receiver operating characteristics curve (AUROC) and statistical evaluation indexes. The results showed that all the employed methods performed well;in particular, the BRT model (AUROC=0.819) yielded higher prediction accuracy than RF (AUROC=0.798) and CART (AUROC=0.764). Findings of this study can assist in characterizing and mitigating the related hazard of land subsidence events.

关键词： Land subsidence susceptibility Boosted regression trees Random forest classification and regression tree Iran

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Mineral recognition mapping using measured spectra based on classification and regression tree

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JOURNAL OF APPLIED REMOTE SENSING 2016年第4期10卷

作者： Zhan, Yunjun Su, Yubin Huang, Jiejun Ye, Fawang Zhang, Chuan Wuhan Univ Technol Sch Resources & Environm Engn 122 Luoshi Rd Wuhan 430070 Hunan Peoples R China Beijing Res Inst Uranium Geol Natl Key Lab Remote Sensing Informat & Image Anal 10 Anwai Xiaoguandongli Beijing 100029 Peoples R China

The alteration of surrounding rock is an important prospecting indicator in mineral exploration, but some important minerals are unclassified or misclassified when using hyper-spectral remote sensing mineral recognition. A method for mineral recognition mapping was proposed. In this method, a decision tree discrimination rule was established based on the classification and regression tree data-mining algorithm and the absorption characteristics of field-measured spectra. Compared with spectral angle mapping and mixture-tuned matched filtering (MTMF), this method is shown to be efficient for mineral recognition mapping using hyper-spectral images;its accuracy is 85.06%, which is greater than that of the MTMF method (83.91%). The advantages of the proposed method comprise the reduction of errors caused by the setting of the artificial threshold for mineral mapping and the lesser degree of difficulty in its training. Furthermore, the hierarchy structure of the decision tree in this method reflects the recognition process clearly, and the rule nodes are closely related to the spectra of the minerals;therefore, the advantage of this method is the interpretability of the results and the process. This method could be used for mineral recognition and classification using hyperspectral images. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： hyperspectral image mineral mapping classification and regression tree spectral characteristic field-measured spectra

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