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Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Background

Pancreatic cancer is one of the most serious problems that has taken many lives worldwide. The diagnostic procedure using the traditional approaches was manual by visually analyzing the large volumes of the dataset, making it time-consuming and prone to subjective errors. Hence the need for the computer-aided diagnosis system (CADs) emerged that comprises the machine and deep learning approaches for denoising, segmentation and classification of pancreatic cancer.

Introduction

There are different modalities used for the diagnosis of pancreatic cancer, such as Positron Emission Tomography/Computed Tomography (PET/CT), Magnetic Resonance Imaging (MRI), Multiparametric-MRI (Mp-MRI), Radiomics and Radio-genomics. Although these modalities gave remarkable results in diagnosis on the basis of different criteria. CT is the most commonly used modality that produces detailed and fine contrast images of internal organs of the body. However, it may also contain a certain amount of gaussian and rician noise that is necessary to be preprocessed before segmentation of the required region of interest (ROI) from the images and classification of cancer.

Methods

This paper analyzes different methodologies used for the complete diagnosis of pancreatic cancer, including the denoising, segmentation and classification, along with the challenges and future scope for the diagnosis of pancreatic cancer.

Results

Various filters are used for denoising and image smoothening and filters as gaussian scale mixture process, non-local means, median filter, adaptive filter and average filter have been used more for better results.

Conclusion

In terms of segmentation, atlas based region-growing method proved to give better results as compared to the state of the art whereas, for the classification, deep learning approaches outperformed other methodologies to classify the images as cancerous and non- cancerous. These methodologies have proved that CAD systems have become a better solution to the ongoing research proposals for the detection of pancreatic cancer worldwide.

© 2024 Bentham Science Publishers
© 2024 The Author(s). Published by Bentham Science Publisher. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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2023-07-07
2024-11-26

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References

  1. VrielingA. Bueno-de-MesquitaH.B. BoshuizenH.C. MichaudD.S. SeverinsenM.T. OvervadK. OlsenA. TjønnelandA. Clavel-ChapelonF. Boutron-RuaultM.C. KaaksR. RohrmannS. BoeingH. NöthlingsU. TrichopoulouA. MoutsiouE. DilisV. PalliD. KroghV. PanicoS. TuminoR. VineisP. van GilsC.H. PeetersP.H. LundE. GramI.T. RodríguezL. AgudoA. LarrañagaN. SánchezM.J. NavarroC. BarricarteA. ManjerJ. LindkvistB. SundM. YeW. BinghamS. KhawK.T. RoddamA. KeyT. BoffettaP. DuellE.J. JenabM. GalloV. RiboliE. Cigarette smoking, environmental tobacco smoke exposure and pancreatic cancer risk in the European Prospective Investigation into Cancer and Nutrition.Int. J. Cancer2010126102394240310.1002/ijc.2490719790196
     [Google Scholar]
  2. VincentA. HermanJ. SchulickR. HrubanR.H. GogginsM. Pancreatic cancer.Lancet2011378979160762010.1016/S0140‑6736(10)62307‑021620466
     [Google Scholar]
  3. YasudaK. MukaiH. FujimotoS. NakajimaM. KawaiK. The diagnosis of pancreatic cancer by endoscopic ultrasonography.Gastrointest. Endosc.19883411810.1016/S0016‑5107(88)71220‑13280392
     [Google Scholar]
  4. WarshawA.L. GuZ.Y. WittenbergJ. WaltmanA.C. Preoperative staging and assessment of resectability of pancreatic cancer.Arch. Surg.1990125223023310.1001/archsurg.1990.014101401080182154172
     [Google Scholar]
  5. KurtM. OzkanM. CakirogluM. KocamanO. YilmazB. CanG. KorkmazU. DandilE. EksiZ. Age-based computer-aided diagnosis approach for pancreatic cancer on endoscopic ultrasound images.Endosc. Ultrasound20165210110710.4103/2303‑9027.18047327080608
     [Google Scholar]
  6. KarmazanovskyG. FedorovV. KubyshkinV. KotchatkovA. Pancreatic head cancer: Accuracy of CT in determination of resectability.Abdom. Imaging200530448850010.1007/s00261‑004‑0279‑z15759205
     [Google Scholar]
  7. AdamekH.E. AlbertJ. BreerH. WeitzM. SchillingD. RiemannJ.F. Pancreatic cancer detection with magnetic resonance cholangiopancreatography and endoscopic retrograde cholangiopancreatography: A prospective controlled study.Lancet2000356922519019310.1016/S0140‑6736(00)02479‑X10963196
     [Google Scholar]
  8. AgarwalB Abu-HamdaE MolkeKL CorreaAM HoL Endoscopic ultrasound-guided fine needle aspiration and multidetector spiral CT in the diagnosis of pancreatic cancer.Am J Gastroenterol200499584485010.1111/j.1572‑0241.2004.04177.x
     [Google Scholar]
  9. HigashiT. SagaT. NakamotoY. IshimoriT. FujimotoK. DoiR. ImamuraM. KonishiJ. Diagnosis of pancreatic cancer using fluorine-18 fluorodeoxyglucose positron emission tomography (FDG PET) —Usefulness and limitations in “clinical reality”—.Ann. Nucl. Med.200317426127910.1007/BF0298852112932109
     [Google Scholar]
  10. HockeM. SchulzeE. GottschalkP. TopalidisT. DietrichC.F. Contrast-enhanced endoscopic ultrasound in discrimination between focal pancreatitis and pancreatic cancer.World J. Gastroenterol.200612224625010.3748/wjg.v12.i2.24616482625
     [Google Scholar]
  11. LambinP. LeijenaarR.T.H. DeistT.M. PeerlingsJ. de JongE.E.C. van TimmerenJ. SanduleanuS. LarueR.T.H.M. EvenA.J.G. JochemsA. van WijkY. WoodruffH. van SoestJ. LustbergT. RoelofsE. van ElmptW. DekkerA. MottaghyF.M. WildbergerJ.E. WalshS. Radiomics: The bridge between medical imaging and personalized medicine.Nat. Rev. Clin. Oncol.2017141274976210.1038/nrclinonc.2017.14128975929
     [Google Scholar]
  12. MacovskiA. Noise in MRI.Magn. Reson. Med.199636349449710.1002/mrm.19103603278875425
     [Google Scholar]
  13. GudbjartssonH. PatzS. The rician distribution of noisy mri data.Magn. Reson. Med.199534691091410.1002/mrm.19103406188598820
     [Google Scholar]
  14. FuB. ZhaoX. SongC. LiX. WangX. A salt and pepper noise image denoising method based on the generative classification.Multimedia Tools Appl.2019789120431205310.1007/s11042‑018‑6732‑8
     [Google Scholar]
  15. SathuaS.K. DashA. BeheraA. Removal of salt and pepper noise from gray-scale and color images: An adaptive approach.J Comput Sci Technol201751
     [Google Scholar]
  16. KirtiT. JitendraK. AshokS. Poisson noise reduction from X-ray images by region classification and response median filtering.Sadhana201742685586310.1007/s12046‑017‑0654‑4
     [Google Scholar]
  17. HuangT. YangG. TangG. A fast two-dimensional median filtering algorithm.IEEE Trans. Acoust. Speech Signal Process.1979271131810.1109/TASSP.1979.1163188
     [Google Scholar]
  18. LimJ.S. Two-dimensional signal and image processing.Prentice HallEnglewood Clis, NJ1990710
     [Google Scholar]
  19. BurrusC.S. GopinathR.A. GuoH. OdegardJ.E. SelesnickI.W. An Introduction to wavelets and wavelet transforms: A primer.Prentice HallNew Jersey1998
     [Google Scholar]
  20. BuadesA. CollB. MorelJ.M. A non-local algorithm for image denoising.IEEE Comput Soc Conf Comput Vision Pattern Recogn20052606510.1109/CVPR.2005.38
     [Google Scholar]
  21. RothS. BlackM.J. Fields of experts: A framework for learning image priors.2005 IEEE Comp Soc Conf Comp Vis and Patt Recog (CVPR'05)200528666710.1109/CVPR.2005.160
     [Google Scholar]
  22. Aja-FernandezS. Alberola-LopezC. WestinC.F. Noise and signal estimation in magnitude MRI and Rician distributed images: a LMMSE approach.IEEE Trans. Image Process.20081781383139810.1109/TIP.2008.92538218632347
     [Google Scholar]
  23. BarbuA. Training an active random field for real-time image denoising.IEEE Trans. Image Process.200918112451246210.1109/TIP.2009.202825419635701
     [Google Scholar]
  24. LuisierF. BluT. WolfeP.J. A CURE for noisy magnetic resonance images: chi-square unbiased risk estimation.IEEE Trans. Image Process.20122183454346610.1109/TIP.2012.219156522491082
     [Google Scholar]
  25. RajanJ. VeraartJ. Van AudekerkeJ. VerhoyeM. SijbersJ. Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images.Magn. Reson. Imaging201230101512151810.1016/j.mri.2012.04.02122819583
     [Google Scholar]
  26. BhadauriaH.S. DewalM.L. Medical image denoising using adaptive fusion of curvelet transform and total variation.Comput. Electr. Eng.20133951451146010.1016/j.compeleceng.2012.04.003
     [Google Scholar]
  27. GolshanH.M. HasanzadehR.P.R. YousefzadehS.C. An MRI denoising method using image data redundancy and local SNR estimation.Magn. Reson. Imaging20133171206121710.1016/j.mri.2013.04.00423668996
     [Google Scholar]
  28. MohanJ. KrishnaveniV. GuoY. MRI denoising using nonlocal neutrosophic set approach of Wiener filtering.Biomed. Signal Process. Control20138677979110.1016/j.bspc.2013.07.005
     [Google Scholar]
  29. IsaI.S. SulaimanS.N. MustaphaM. DarusS. Evaluating denoising performances of fundamental filters for t2-weighted MRI images.Procedia Comput. Sci.20156076076810.1016/j.procs.2015.08.231
     [Google Scholar]
  30. ManjónJ.V. CoupéP. BuadesA. MRI noise estimation and denoising using non-local PCA.Med. Image Anal.2015221354710.1016/j.media.2015.01.00425725303
     [Google Scholar]
  31. SeethaJ. RajaS.S. Denoising of MRI images using filtering methods.2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)201676576910.1109/WiSPNET.2016.7566236
     [Google Scholar]
  32. BiswasR. PurkayasthaD. RoyS. Denoising of MRI images using curvelet transform.Advances in Systems, Control and Automation.SingaporeSpringer2018575583
     [Google Scholar]
  33. YuanJ. An improved variational model for denoising magnetic resonance images.Comput. Math. Appl.20187692212222210.1016/j.camwa.2018.05.044
     [Google Scholar]
  34. SharmaK.K. GurjarD. JyotyanaM. KumariV. Denoising of Brain MRI Images Using a Hybrid Filter Method of Sylvester-Lyapunov Equation and Non Local Means. InSmart Innovations in Communication and Computational Sciences.SingaporeSpringer2019495505
     [Google Scholar]
  35. LiuL. YangH. FanJ. LiuR.W. DuanY. Rician noise and intensity nonuniformity correction (NNC) model for MRI data.Biomed. Signal Process. Control20194950651910.1016/j.bspc.2018.11.008
     [Google Scholar]
  36. CabezasM. OliverA. LladóX. FreixenetJ. Bach CuadraM. A review of atlas-based segmentation for magnetic resonance brain images.Comput. Methods Programs Biomed.20111043e158e17710.1016/j.cmpb.2011.07.01521871688
     [Google Scholar]
  37. WolzR. ChuC. MisawaK. FujiwaraM. MoriK. RueckertD. Automated abdominal multi-organ segmentation with subject-specific atlas generation.IEEE Trans. Med. Imaging20133291723173010.1109/TMI.2013.226580523744670
     [Google Scholar]
  38. ChuC. OdaM. KitasakaT. MisawaK. FujiwaraM. HayashiY. NimuraY. RueckertD. MoriK. Multi-organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images.International Conference on Medical Image Computing and Computer-Assisted Intervention Springer: Berlin Heidelberg2013165172
     [Google Scholar]
  39. GibsonE. GigantiF. HuY. BonmatiE. BandulaS. GurusamyK. DavidsonB. PereiraS.P. ClarksonM.J. BarrattD.C. Automatic multi-organ segmentation on abdominal CT with dense v-networks.IEEE Trans. Med. Imaging20183781822183410.1109/TMI.2018.280630929994628
     [Google Scholar]
  40. ZhouY. XieL. FishmanE.K. YuilleA.L. Deep supervision for pancreatic cyst segmentation in abdominal CT scans.International Conference on Medical Image Computing and Computer-Assisted InterventionSpringer20172223010.1007/978‑3‑319‑66179‑7_26
     [Google Scholar]
  41. HusseinS. KandelP. BolanC.W. WallaceM.B. BagciU. Lung and pancreatic tumor characterization in the deep learning era: novel supervised and unsupervised learning approaches.IEEE Trans. Med. Imaging20193881777178710.1109/TMI.2019.289434930676950
     [Google Scholar]
  42. JoshiA.A. HuH.H. LeahyR.M. GoranM.I. NayakK.S. Automatic intra-subject registration-based segmentation of abdominal fat from water-fat MRI.J. Magn. Reson. Imaging201337242343010.1002/jmri.2381323011805
     [Google Scholar]
  43. RajuP.D. NeelimaG. Image segmentation by using histogram thresholding.Int. J. Comput. Sci. Eng. Technol.201221776779
     [Google Scholar]
  44. JiangH TanH FujitaH. A hybrid method for pancreas extraction from CT image based on level set methods.Comput Math Methods Med.2013201347951610.1155/2013/479516
     [Google Scholar]
  45. OkadaT. LinguraruM.G. HoriM. SummersR.M. TomiyamaN. SatoY. Abdominal multi-organ CT segmentation using organ correlation graph and prediction-based shape and location priors.International Conference on Medical Image Computing and Computer-Assisted Intervention201310.1007/978‑3‑642‑40760‑4_35
     [Google Scholar]
  46. ShimizuA. KimotoT. KobatakeH. NawanoS. ShinozakiK. Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography.Int. J. CARS201051859810.1007/s11548‑009‑0384‑020033509
     [Google Scholar]
  47. ZanatyE.A. Improved region growing method for magnetic resonance images (MRIs) segmentation.American J Remote Sens201312536010.11648/j.ajrs.20130102.16
     [Google Scholar]
  48. AliH. ElmogyM. El-DaydamonyE. AtwanA. Multi-resolution mri brain image segmentation based on morphological pyramid and fuzzy c-mean clustering.Arab. J. Sci. Eng.201540113173318510.1007/s13369‑015‑1791‑x
     [Google Scholar]
  49. KarasawaK. OdaM. KitasakaT. MisawaK. FujiwaraM. ChuC. ZhengG. RueckertD. MoriK. Multi-atlas pancreas segmentation: Atlas selection based on vessel structure.Med. Image Anal.201739182810.1016/j.media.2017.03.00628410505
     [Google Scholar]
  50. MadzakA. OlesenS.S. HaldorsenI.S. DrewesA.M. FrøkjærJ.B. Secretin-stimulated MRI characterization of pancreatic morphology and function in patients with chronic pancreatitis.Pancreatology201717222823610.1016/j.pan.2017.01.00928162928
     [Google Scholar]
  51. HavaeiM. DutilF. PalC. LarochelleH. JodoinP.M. A convolutional neural network approach to brain tumor segmentation.Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries2015195208
     [Google Scholar]
  52. KoyuncuH. CeylanR. A hybrid tool on denoising and enhancement of abdominal CT images before organ & tumour segmentation.2017 IEEE 37th International Conference on Electronics and Nanotechnology (ELNANO)20172495410.1109/ELNANO.2017.7939757
     [Google Scholar]
  53. RufJ. Lopez HänninenE. BöhmigM. KochI. DeneckeT. PlotkinM. LangrehrJ. WiedenmannB. FelixR. AmthauerH. Impact of FDG-PET/MRI image fusion on the detection of pancreatic cancer.Pancreatology20066651251910.1159/00009699317106215
     [Google Scholar]
  54. SăftoiuA. VilmannP. GorunescuF. GheoneaD.I. GorunescuM. CiureaT. PopescuG.L. IordacheA. HassanH. IordacheS. Neural network analysis of dynamic sequences of EUS elastography used for the differential diagnosis of chronic pancreatitis and pancreatic cancer.Gastrointest. Endosc.20086861086109410.1016/j.gie.2008.04.03118656186
     [Google Scholar]
  55. GeG. WongG.W. Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles.BMC Bioinformatics20089127510.1186/1471‑2105‑9‑27518547427
     [Google Scholar]
  56. HaywardJ. AlvarezS.A. RuizC. SullivanM. TsengJ. WhalenG. Machine learning of clinical performance in a pancreatic cancer database.Artif. Intell. Med.201049318719510.1016/j.artmed.2010.04.00920483571
     [Google Scholar]
  57. ChangY.H. ThibaultG. MadinO. AzimiV. MeyersC. JohnsonB. LinkJ. MargolinA. GrayJ.W. Deep learning based nucleus classification in pancreas histological images.2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)20176727510.1109/EMBC.2017.8036914
     [Google Scholar]
  58. ChengP.M. MalhiH.S. Transfer learning with convolutional neural networks for classification of abdominal ultrasound images.J. Digit. Imaging201730223424310.1007/s10278‑016‑9929‑227896451
     [Google Scholar]
  59. ZhaoL. ZhaoH. YanH. Gene expression profiling of 1200 pancreatic ductal adenocarcinoma reveals novel subtypes.BMC Cancer201818160310.1186/s12885‑018‑4546‑829843660
     [Google Scholar]
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Survey of Denoising, Segmentation and Classification of Pancreatic Cancer Imaging
Curr. Med. Imaging 20, e150523216892 (2024); https://doi.org/10.2174/1573405620666230515090523
/content/journals/cmir/10.2174/1573405620666230515090523
/content/journals/cmir/10.2174/1573405620666230515090523
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  • Article Type:     Review Article
Keyword(s): Classification; CT; Deep learning; Denoising; Pancreatic cancer; Segmentation

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