Skip to content
2000
Volume 3, Issue 1
  • ISSN: 2950-3752
  • E-ISSN: 2950-3760

Abstract

Objective

Visual descriptor methods like Local Binary Pattern (LBP) capture anatomical structures in captured images along with their disparities, which can be exploited by suitable methods for the diagnosis of medical anomalies. In our study, we have proposed a Local Mean Gradient Pattern (LMGP), based partly on LBP, a feature extraction algorithm for the classification of Computed Tomography (CT) images of the brain into normal, ischemic, or hemorrhage categories.

Methods

The AISD and Kaggle datasets containing patients’ brain CT scan images (acute ischemic stroke, hemorrhage, and normal cases) were taken. Initially, adaptive histogram equalization (AHE) techniques were applied as preprocessing operations to enhance the quality of the CT images. Furthermore, features were extracted from the preprocessed data using several feature extraction techniques, including our proposed LMGP feature descriptor. The features were then scaled using the standard scaling technique. Subsequently, preprocessed images were fed into different classifiers to build models for classifying brain CT scan images.

Results

The effectiveness of our methodology LMGP was determined using different metrics, such as recall, precision, F1 score, logarithmic loss, accuracy (ACC), and area under the curve (ROC). Conclusively, LMGP performed best when the RBF-SVM classifier was used for the classification and gave an accuracy of 94% and 96% in the case of five-fold and ten-fold cross-validation, respectively.

Conclusion

LMGP offers a distinctive and robust method of feature extraction from CT scan images by combining local information along with gradient change in pixels of the image. The efficacy of our proposed methodology (LMGP) was evaluated by using distinct classifiers, and the results were compared with eight different feature extraction methods. Overall, LMGP effectively outperformed all other feature descriptor methods in this study.

© 2026 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cai/10.2174/0129503752322204241127104725
2025-02-26
2026-03-05

  • From This Site

    /content/journals/cai/10.2174/0129503752322204241127104725
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. NanniL. LuminiA. BrahnamS. Local binary patterns variants as texture descriptors for medical image analysis.Artif. Intell. Med.201049211712510.1016/j.artmed.2010.02.00620338737
     [Google Scholar]
  2. CastellinoR.A. Computer aided detection (CAD): An overview.Cancer Imaging200551171910.1102/1470‑7330.2005.001816154813
     [Google Scholar]
  3. MuralaS. WuQ.M.J. Local mesh patterns versus local binary patterns: Biomedical image indexing and retrieval.IEEE J. Biomed. Health Inform.201418392993810.1109/JBHI.2013.228852224235315
     [Google Scholar]
  4. DubeyS.R. SinghS.K. SinghR.K. Local wavelet pattern: A new feature descriptor for image retrieval in medical CT databases.IEEE Trans. Image Process.201524125892590310.1109/TIP.2015.249344626513789
     [Google Scholar]
  5. YahiaS. SalemY.B. AbdelkrimM.N. 3D Textures Analysis Based on Features Extraction.Advanced Methods for Human Biometrics. DerbelN. KanounO. ChamSpringer International Publishing202123125610.1007/978‑3‑030‑81982‑8_11
     [Google Scholar]
  6. KhellahF.M. Texture classification using dominant neighborhood structure.IEEE Trans. Image Process.201120113270327910.1109/TIP.2011.214342221511565
     [Google Scholar]
  7. MuralaS. JonathanW.Q.M. Local ternary co-occurrence patterns: A new feature descriptor for MRI and CT image retrieval.Neurocomputing201311939941210.1016/j.neucom.2013.03.018
     [Google Scholar]
  8. DubeyS.R. SinghS.K. SinghR.K. Local diagonal extrema pattern: A new and efficient feature descriptor for CT image retrieval.IEEE Signal Process. Lett.20152291215121910.1109/LSP.2015.2392623
     [Google Scholar]
  9. GautamA. RamanB. RaghuvanshiS. A hybrid approach for the delineation of brain lesion from CT images.Biocybern. Biomed. Eng.201838350451810.1016/j.bbe.2018.04.003
     [Google Scholar]
  10. KaurN. NazirN. A review of local binary pattern based texture feature extraction.2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)03-04 Sept, 2021, Noida, India202114
     [Google Scholar]
  11. HeikkiläM. PietikäinenM. SchmidC. Description of interest regions with center-symmetric local binary patterns.Computer Vision, Graphics and Image Processing.Berlin, HeidelbergSpringer200610.1007/11949619_6
     [Google Scholar]
  12. GautamA. RamanB. Local gradient of gradient pattern: A robust image descriptor for the classification of brain strokes from computed tomography images.Pattern Anal. Appl.202023279781710.1007/s10044‑019‑00838‑8
     [Google Scholar]
  13. WanS. LeeH.C. HuangX. XuT. XuT. ZengX. ZhangZ. SheikineY. ConnollyJ.L. FujimotoJ.G. ZhouC. Integrated local binary pattern texture features for classification of breast tissue imaged by optical coherence microscopy.Med. Image Anal.20173810411610.1016/j.media.2017.03.00228327449
     [Google Scholar]
  14. BerbarM.A. Features extraction using encoded local binary pattern for detection and grading diabetic retinopathy.Health Inf. Sci. Syst.20221011410.1007/s13755‑022‑00181‑z35782197
     [Google Scholar]
  15. PatelA. SchreuderF.H.B.M. KlijnC.J.M. ProkopM. GinnekenB. MarqueringH.A. RoosY.B.W.E.M. BaharogluM.I. MeijerF.J.A. ManniesingR. Intracerebral haemorrhage segmentation in non-contrast CT.Sci. Rep.2019911785810.1038/s41598‑019‑54491‑631780815
     [Google Scholar]
  16. XiaoyangT. TriggsB. Enhanced local texture feature sets for face recognition under difficult lighting conditions.IEEE Trans. Image Process.20101961635165010.1109/TIP.2010.204264520172829
     [Google Scholar]
  17. El merabet, Y.; Ruichek, Y.; El idrissi, A. Attractive-and-repulsive center-symmetric local binary patterns for texture classification.Eng. Appl. Artif. Intell.20197815817210.1016/j.engappai.2018.11.011
     [Google Scholar]
  18. LiaoS. LawM.W.K. ChungA.C.S. Dominant local binary patterns for texture classification.IEEE Trans. Image Process.20091851107111810.1109/TIP.2009.201568219342342
     [Google Scholar]
  19. BianconiF. GonzálezE. FernándezA. Dominant local binary patterns for texture classification: Labelled or unlabelled?Pattern Recognit. Lett.20156581410.1016/j.patrec.2015.06.025
     [Google Scholar]
  20. GuoZ. ZhangL. ZhangD. A completed modeling of local binary pattern operator for texture classification.IEEE Trans. Image Process.20101961657166310.1109/TIP.2010.204495720215079
     [Google Scholar]
  21. ZhaoY. HuangD.S. JiaW. Completed local binary count for rotation invariant texture classification.IEEE Trans. Image Process.201221104492449710.1109/TIP.2012.220427122711773
     [Google Scholar]
  22. NithyaS. RamakrishnanS. Wavelet domain directional binary pattern using majority principle for texture classification.Physica A202054512357510.1016/j.physa.2019.123575
     [Google Scholar]
  23. AgarwalM. SinghalA. LallB. 3D local ternary co-occurrence patterns for natural, texture, face and bio medical image retrieval.Neurocomputing201831333334510.1016/j.neucom.2018.06.027
     [Google Scholar]
  24. HatibaruahR. NathV.K. HazarikaD. 3D-local oriented zigzag ternary co-occurrence fused pattern for biomedical CT image retrieval.Biomed. Eng. Lett.202010334535710.1007/s13534‑020‑00163‑832850176
     [Google Scholar]
  25. ShindeA. RahulkarA. PatilC. Content based medical image retrieval based on new efficient local neighborhood wavelet feature descriptor.Biomed. Eng. Lett.20199338739410.1007/s13534‑019‑00112‑031456898
     [Google Scholar]
  26. BaochangZ. YongshengG. SanqiangZ. JianzhuangL. Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor.IEEE Trans. Image Process.201019253354410.1109/TIP.2009.203588219887313
     [Google Scholar]
  27. FadaeiS. AmirfattahiR. AhmadzadehM.R. Local derivative radial patterns: A new texture descriptor for content-based image retrieval.Signal Processing201713727428610.1016/j.sigpro.2017.02.013
     [Google Scholar]
  28. DarapureddyN. KaratapuN. BattulaT.K. Local derivative vector pattern: Hybrid pattern for content-based medical image retrieval.Rev. Comp. Engin. Stud.202074798610.18280/rces.070401
     [Google Scholar]
  29. VenuD. Classification analysis for local mesh patterns using medical image segmentation.Int. J. Food Nutrit. Sci.2022111252325241
     [Google Scholar]
  30. HsuS.B. LeeC-H. ChangP-C. HanC-C. FanK-C. Local wavelet acoustic pattern: a novel time–frequency descriptor for birdsong recognition.IEEE Trans. Multimed.201820123187319910.1109/TMM.2018.2834866
     [Google Scholar]
  31. PillaiA. SoundrapandiyanR. SatapathyS. SatapathyS.C. JungK-H. KrishnanR. Local diagonal extrema number pattern: A new feature descriptor for face recognition.Future Gener. Comput. Syst.20188129730610.1016/j.future.2017.09.055
     [Google Scholar]
  32. ErM.B. Heart sounds classification using convolutional neural network with 1D-local binary pattern and 1D-local ternary pattern features.Appl. Acoust.202118010815210.1016/j.apacoust.2021.108152
     [Google Scholar]
  33. SreeV.B. ChandraE. Entropy based Local Binary Pattern (ELBP) feature extraction technique of multimodal biometrics as defence mechanism for cloud storage.Alex. Eng. J.201958110311410.1016/j.aej.2018.12.008
     [Google Scholar]
  34. De BruijneM. Symmetry-enhanced attention network for acute ischemic infarct segmentation with non-contrast CT images.24th International ConferenceSept 27–Oct 1, 2021Strasbourg, France431441
     [Google Scholar]
  35. van der WaltS. SchönbergerJ.L. Nunez-IglesiasJ. BoulogneF. WarnerJ.D. YagerN. GouillartE. YuT. Scikit-image: Image processing in Python.PeerJ20142e45310.7717/peerj.45325024921
     [Google Scholar]
  36. PedregosaF. Scikit-learn: Machine learning in python.J. Mach. Learn. Res.20111228252830
     [Google Scholar]
  37. HunterJ.D. Matplotlib: A 2D Graphics Environment.Comput. Sci. Eng.200793909510.1109/MCSE.2007.55
     [Google Scholar]
  38. MohammadzadehA. Multilayer Perceptron (MLP) Neural Networks.Neural Networks and Learning Algorithms in MATLAB. MohammadazadehA. ChamSpringer International Publishing202252110.1007/978‑3‑031‑14571‑1_2
     [Google Scholar]
/content/journals/cai/10.2174/0129503752322204241127104725
Loading
Local Mean Gradient Pattern (LMGP): A Novel Approach for the Classification of Brain CT Scan Images
Curr Arti Intell 3, E29503752322204 (2025); https://doi.org/10.2174/0129503752322204241127104725
/content/journals/cai/10.2174/0129503752322204241127104725
/content/journals/cai/10.2174/0129503752322204241127104725
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): area under the curve; Brain CT scan images; cancer; feature extraction method; local binary pattern; machine learning

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test