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Volume 3, Issue 1
  • ISSN: 2950-3779
  • E-ISSN: 2950-3787

Abstract

Background

In the research and practice of medical sciences, accurate classification of biomedical images with computer programs may provide an important basis for the study and diagnosis of many diseases.

Methods

This paper proposes a new statistical approach that can accurately classify biomedical images based on their statistical features. In the first step of the proposed approach, a number of SIFT features of different types are computed for each pixel in a biomedical image and a statistical feature that describes the distribution of each type of SIFT features is obtained for the image. In the second step, a dynamic programming approach is used to efficiently analyze the dependence among different statistical features associated with an image and compute the probability for an image to belong to each possible class; the class with the largest probability is determined as the result of classification.

Results

Experimental results show that the proposed approach can lead to classification results with accuracy higher than that of a few state-of-the-art approaches for the classification of biomedical images.

Conclusion

The proposed approach can achieve classification accuracy comparable to that of several state-of-the-art classification approaches. It is thus potentially useful for applications where large models are not appropriate for classification tasks due to limitations in computational or communication resources.

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2024-05-28
2025-05-04

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References

  1. HeldM. SchmitzM.H.A. FischerB. WalterT. NeumannB. OlmaM.H. PeterM. EllenbergJ. GerlichD.W. CellCognition: Time-resolved phenotype annotation in high-throughput live cell imaging.Nat. Methods20107974775410.1038/nmeth.148620693996
     [Google Scholar]
  2. MisselwitzB. StrittmatterG. PeriaswamyB. SchlumbergerM.C. RoutS. HorvathP. KozakK. HardtW.D. Enhanced cellclassifier: A multi-class classification tool for microscopy images.BMC Bioinformatics20101113010.1186/1471‑2105‑11‑3020074370
     [Google Scholar]
  3. PauG. FuchsF. SklyarO. BoutrosM. HuberW. EBImage-An R package for image processing with applications to cellular phenotypes.Bioinformatics201026797998110.1093/bioinformatics/btq04620338898
     [Google Scholar]
  4. ZhouJ. LamichhaneS. SterneG. YeB. PengH. BIOCAT: A pattern recognition platform for customizable biological image classification and annotation.BMC Bioinformatics201314129110.1186/1471‑2105‑14‑29124090164
     [Google Scholar]
  5. GloryE. MurphyR.F. Automated subcellular location determination and high-throughput microscopy.Dev. Cell200712171610.1016/j.devcel.2006.12.00717199037
     [Google Scholar]
  6. NanniL. BrahnamS. LuminiA. A very high performing system to discriminate tissues in mammograms as benign and malignant.Expert Syst. Appl.20123921968197110.1016/j.eswa.2011.08.050
     [Google Scholar]
  7. NosakaR. FukuiK. HEp-2 cell classification using rotation invariant co-occurrence among local binary patterns.Pattern Recognit.20144772428243610.1016/j.patcog.2013.09.018
     [Google Scholar]
  8. SongT. LiH. MengF. WuQ. CaiJ. LETRIST: Locally encoded transform feature histogram for rotation-invariant texture classification.IEEE Trans. Circ. Syst. Video Tech.20182871565157910.1109/TCSVT.2017.2671899
     [Google Scholar]
  9. UhlmannV. SinghS. CarpenterA.E. CP-CHARM: Segmentation-free image classification made accessible.BMC Bioinformatics20161715110.1186/s12859‑016‑0895‑y26817459
     [Google Scholar]
  10. YangF. XuY.Y. ShenH.B. Many local pattern texture features: Which is better for image-based multilabel human protein subcellular localization classification?ScientificWorldJournal2014201411410.1155/2014/42904925050396
     [Google Scholar]
  11. ZhuZ. YouX. ChenC.L.P. TaoD. OuW. JiangX. ZouJ. An adaptive hybrid pattern for noise-robust texture analysis.Pattern Recognit.20154882592260810.1016/j.patcog.2015.01.001
     [Google Scholar]
  12. HaralickR.M. ShanmugamK. DinsteinI.H. Textural features for image classification.IEEE Trans. Syst. Man Cybern.1973SMC-3661062110.1109/TSMC.1973.4309314
     [Google Scholar]
  13. FogelI. SagiD. Gabor filters as texture discriminator.Biol. Cybern.198961210311310.1007/BF00204594
     [Google Scholar]
  14. NanniL. BrahnamS. GhidoniS. MenegattiE. BarrierT. A comparison of methods for extracting information from the co-occurrence matrix for subcellular classification.Expert Syst. Appl.201340187457746710.1016/j.eswa.2013.07.047
     [Google Scholar]
  15. BarkerJ. HoogiA. DepeursingeA. RubinD.L. Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles.Med. Image Anal.2016301607110.1016/j.media.2015.12.00226854941
     [Google Scholar]
  16. XuY. ZhuJ.Y. ChangE.I.C. LaiM. TuZ. Weakly supervised histopathology cancer image segmentation and classification.Med. Image Anal.201418359160410.1016/j.media.2014.01.01024637156
     [Google Scholar]
  17. CristianiniN. TaylorS.J. An Introduction To Support Vector Machines And Other Kernel-based Learning Methods.Cambridge, UKCambridge University Press200010.1017/CBO9780511801389
     [Google Scholar]
  18. OtaloraS. Combining unsupervised feature learning and riesz wavelets for histopathology image representation: application to identifying anaplastic medulloblastomaProceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention Munich201558158810.1007/978‑3‑319‑24553‑9_71
     [Google Scholar]
  19. VuT.H. MousaviH.S. MongaV. RaoG. RaoU.K.A. Histopathological image classification using discriminative feature-oriented dictionary learning.IEEE Trans. Med. Imaging201635373875110.1109/TMI.2015.249353026513781
     [Google Scholar]
  20. AmakdoufH. ZouhriA. El MallahiM. TahiriA. ChenouniD. QjidaaH. Artificial intelligent classification of biomedical color image using quaternion discrete radial Tchebichef moments.Multimedia Tools Appl.20218023173319210.1007/s11042‑020‑09781‑x
     [Google Scholar]
  21. BoraK. ChowdhuryM. MahantaL.B. KunduM.K. DasA.K. Pap smear image classification using convolutional neural networkProceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing 18 December 2016, Guwahati Assam India20161810.1145/3009977.3010068
     [Google Scholar]
  22. ChingT. HimmelsteinD.S. Beaulieu-JonesB.K. KalininA.A. DoB.T. WayG.P. FerreroE. AgapowP.M. ZietzM. HoffmanM.M. XieW. RosenG.L. LengerichB.J. IsraeliJ. LanchantinJ. WoloszynekS. CarpenterA.E. ShrikumarA. XuJ. CoferE.M. LavenderC.A. TuragaS.C. AlexandariA.M. LuZ. HarrisD.J. DeCaprioD. QiY. KundajeA. PengY. WileyL.K. SeglerM.H.S. BocaS.M. SwamidassS.J. HuangA. GitterA. GreeneC.S. Opportunities and obstacles for deep learning in biology and medicine.J. R. Soc. Interface2018151412017038710.1098/rsif.2017.038729618526
     [Google Scholar]
  23. HanX.H. LeiJ. ChenY.W. HEp-2 cell classification using k-support spatial pooling in deep CNNsProceedings of International Workshop on Deep Learning in Medical Image Analysis Granada, Spain201631110.1007/978‑3‑319‑46976‑8_1
     [Google Scholar]
  24. RakhlinA. ShvetsA. IglovikovV. KalininA.A. Deep convolutional neural networks for breast cancer histology image analysisProceedings of International Conference Image Analysis and Recognition Póvoa de Varzim Portugal201873774410.1007/978‑3‑319‑93000‑8_83
     [Google Scholar]
  25. SenanE.M. AlsaadeF.W. Al-MashhadaniM.I.A. AldhyaniT.H.H. Al-AdhailehM.H. Classification of histopathological images for early detection of breast cancer using deep learning.Journal of Applied Science and Engineering2021243323329
     [Google Scholar]
  26. SwarupC. Biologically inspired cnn network for brain tumor abnormalities detection and features extraction from mri Images.Human-centric Computing and Information Sciences20221222
     [Google Scholar]
  27. DasA. MohapatraS.K. MohantyM.N. Design of deep ensemble classifier with fuzzy decision method for biomedical image classification.Appl. Soft Comput.202211510817810.1016/j.asoc.2021.108178
     [Google Scholar]
  28. BarzekarH. YuZ. C-Net: A reliable convolutional neural network for biomedical image classification.Expert Syst. Appl.202218711600310.1016/j.eswa.2021.116003
     [Google Scholar]
  29. KumarN. SharmaM. SinghV.P. MadanC. MehandiaS. An empirical study of handcrafted and dense feature extraction techniques for lung and colon cancer classification from histopathological images.Biomed. Signal Process. Control20227510359610.1016/j.bspc.2022.103596
     [Google Scholar]
  30. YosinskiJ. CluneJ. BengioY. LipsonH. How transferable are features in deep neural networks?arXiv:1411.17922014
     [Google Scholar]
  31. GinnekenB.V. SetioA.A.A. JacobsC. CiompiF. Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scansProceedings of The IEEE 12th International Symposium on Biomedical Imaging Brooklyn NY201528628910.1109/ISBI.2015.7163869
     [Google Scholar]
  32. NanniL. GhidoniS. BrahnamS. Ensemble of convolutional neural networks for bioimage classification.Applied Computing and Informatics2021171193510.1016/j.aci.2018.06.002
     [Google Scholar]
  33. LiG. SunJ. SongY. QuJ. ZhuZ. KhosraviM.R. Real-time classification of brain tumors in MRI images with a convolutional operator-based hidden Markov model.J. Real-Time Image Process.20211841207121910.1007/s11554‑021‑01072‑4
     [Google Scholar]
  34. NanniL. BrahnamS. GhidoniS. MaguoloG. PaciM. General purpose (GenP) bioimage ensemble combining new data augmentation techniques and handcrafted features
     [Google Scholar]
  35. MansourR.F. AlfarN.M. Abdel-KhalekS. AbdelhaqM. SaeedR.A. AlsaqourR. Optimal deep learning based fusion model for biomedical image classification.Expert Syst.2022393e1276410.1111/exsy.12764
     [Google Scholar]
  36. PassahA. SurS.N. PaulB. SAR image classification: a comprehensive study and analysis.IEEE Access2022102038520399
     [Google Scholar]
  37. ShortenC. KhoshgoftaarT.M. A survey on image data augmentation for deep learning.J. Big Data2019616010.1186/s40537‑019‑0197‑0
     [Google Scholar]
  38. KatherJ.N. WeisC.A. BianconiF. MelchersS.M. SchadL.R. GaiserT. MarxA. ZöllnerF.G. Multi-class texture analysis in colorectal cancer histology.Sci. Rep.2016612798810.1038/srep2798827306927
     [Google Scholar]
  39. LoweD.G. Distinctive Image Features from Scale-Invariant Keypoints.Int. J. Comput. Vis.20046029111010.1023/B:VISI.0000029664.99615.94
     [Google Scholar]
  40. NanniL. PaciM. SantosF.L.C.D. BrahnamS. HyttinenJ. Review on texture descriptors for image classification.Computer Vision and Simulation: Methods, Applications and Technology.Hauppauge, NYNova Publications2016
     [Google Scholar]
  41. HeK. ZhangX. RenS. SunJ. Deep residual learning for image recognitionProceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 27-30 June 2016, Las Vegas NV201677077810.1109/CVPR.2016.90
     [Google Scholar]
  42. HuangG. LiuZ. Van Der MaatenL. WeinbergerK.Q. Densely connected convolutional networksProceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition HonoluluHI226122692017
     [Google Scholar]
  43. WangQ. LiP. ZhangL. ZuoW. Towards effective codebookless model for image classification.Pattern Recognit.201659637110.1016/j.patcog.2016.03.004
     [Google Scholar]
/content/journals/cucs/10.2174/0129503779291012240424070357
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Classification of Biomedical Images with Mined Statistical Features and Dynamic Programming
Curr. Comp. Sci. 3, e280524230384 (2024); https://doi.org/10.2174/0129503779291012240424070357
/content/journals/cucs/10.2174/0129503779291012240424070357
/content/journals/cucs/10.2174/0129503779291012240424070357
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  • Article Type:     Research Article
Keyword(s): Biomedical image classification; dynamic programming; maximum likelihood; mutual dependence; SIFT features; statistical features
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