Modified Squamous with Biomedical Image Processing

Author(s)

anindita chatterjee , Himadri Nath Moulick , Dr. Poulami Das ,

Download Full PDF Pages: 06-11 | Views: 820 | Downloads: 212 | DOI: 10.5281/zenodo.3427109

Volume 3 - December 2014 (12)

Abstract

SQUAMOUS CELL CARCINOMA(SCC) of the lip is an infiltrating and destructive malignant epithelial tumour, with high potential for lymphatic and/or blood metastasizes. Lip SCC is 15-30% of all SCC the cephalic extremity and 1/5 of the upper aerodigestive tract cancers. We conducted a prospective study in Dermatology Clinic from Craiova, between 2004-2010, with the aim of highlighting the epidemiological aspects, clinical and therapeutically evolution of patients with lip SCC.Lip SCC onset occurs frequently on premalignant lesions, especially on chronic keratoziccheilitis, pointing out the importance of early diagnosis and appropriate treatment for preblastomatouscheilitis. Early establishment of treatment of lip SCC offers the safety of therapeutic accomplishment. Option for surgical treatment of T0, T1N0M0 lip SCC is justified by the very good oncological, aesthetic and functional results in most cases. Surgical treatment of primary T0, T1 lesions, respecting the oncological surgery principles makes it not recommended to "filling in" the results with other therapeutic methods. Patients should be regularly examined for a period of at least three years to capture the moment of occurrence of metastases, or a possible relapse of a lip SCC. Actions are needed to educate the population about the risk factors and to detect precancerous lesions and SCC of rim in early stage.  To present incisional biopsy importance as an effective clinical approach for the diagnosis of lip squamous cell carcinoma and actinic cheilitis malignancy as well as the professional’s lack of knowledge on these two diseases. The physician and dentist must be aware of the main clinical features of lip squamous cell carcinoma so that they can establish its correct diagnosis and early treatment.

Keywords

Multi-model image alignment , extrinsic method , intrinsic method, Smoothing ,Enhancement, Thresholding, Histogram Analysis    

References

i.               A. Bonnaccorsi, “On the Relationship between Firm Size and Export Intensity,” Journal of International Business Studies, XXIII (4), pp. 605-635, 1992. (journal style)

ii.        http://www.cancercenter.com/squamous-cell-cancer

iii.      http://www.localhealth.com/article/lip-cancer/causes

iv.      A. Kutluhan, M. Kiris, Z. Kaya, E. Kisli, V. Yurttas, M. Içli and M. Kösem[2003] Squamous Cell Carcinoma of the Lower Lip and Supra-Omohyoid Neck Dissection, PP. 304-308

v.       EgilsKornevs, AndrejsSkagers, Juris Tars, AndrisBigestans, GunarsLauskis, OlafsLibermanis [2005] 5 year experience with lower lip cancer.

vi.      http://cancer.stanford.edu/skincancer/squamous_cell_carcinoma/staging.html

vii.    NarenN.Venkatesan, MD Raghu Athre, [December 2011] Lip Cancer and Reconstruction.

viii.   VIRGIL PATRASCU, RALUCA CIUREA, Lip Squamous Carcinoma - Epidemiologic, Clinical, Evolutive and Therapeutical Aspects.

ix.      J. Suryatenggara, B.K. Ane, M. Pandjaitan and W. Steinberg [2009]Patternrecognition on 2D cervical cytological digital images for early detectionof cervix cancer, pp. 257-262.

x.       H. S. Wu and J. Barba [November 1994]An algorithm for noisy cell contour extraction via area merging, vol. 38, pp. 604-607.

xi.      J. M. Sharif, M. F. Miswan, M. A. Ngadi, MdSahHj Salam, Muhammad Mahadi bin Abdul Jamil [February 2012]Red Blood Cell Segmentation Using Masking and Watershed Algorithm: A Preliminary Study, in 2012 International Conference on Biomedical Engineering (ICoBE),  pp. 27-28.

xii.    Prasanna G. Shete, Dr.Gajanan K. Kharate And Sanket C. Rege [November- 2012]Breast Cancer Cell Detection Using Digital Image Processing,Vol. 1 Issue 9.

xiii.   PornchaiPhukpattaranont and PleumjitBoonyaphiphat, Segmentation of Cancer Cells in Microscopic Images using Neural Network and Mathematical Morphology.

xiv.  Anita Chaudhary, SonitSukhraj Singh [February 2012]LUNG CANCER DETECTION USING DIGITAL IMAGE PROCESSING, Volume 2, Issue 2.

xv.    ThanatipChankong, NiponTheera-Umpon, SansaneeAuephanwiriyakul [2009] Cervical cell classification using Fourier transform,ICBME 2008, proceeding 23, pp. 476-480.

xvi.  Vipin Kumar Jain, Dr.RituVijay,Lungs Cancer Detection from MRI Image Using Image Processing.

xvii. BustanurRosidi, NorainiJalil, Nur. M. Pista, Lukman H. Ismail, EkoSupriyantoTati L. Mengko, Classification of Cervical Cells Based onLabeled Colour Intensity Distribution.

xviii.        Zhi-Hua Zhou, Yuan Jiang, Yu-Bin Yang, Shi-Fu Chen, Lung Cancer Cell Identification Based on Artificial Neural Network Ensembles

xix.  BasimAlhadidi, Mohammad H. Zu’bi and Hussan N. Suleiman [2007] Mammogram Breast Cancer Image Detection Using Image Processing Function,PP. 217-221.

xx.    HosseinGhayoumiZadeh, SiamakJanianpour and JavadHaddadnia [February 2013] Recognition and Classification of the Cancer Cells by Using Image Processing and LabVIEWVol.5, No.1.

xxi.  Fred L. Bookstein, Principal warps: Thin-plate splines and the decomposition of deformations, IEEE Transactions on Pattern Analysis and Machine Intelligence 11 (1989), no. 6, 567–585.

xxii. F. Brezzi and M. Fortin, Mixed and hybrid finite element methods, Springer, 1991.

xxiii.        Morten Bro-Nielsen, Medical image registration and surgery simulation, Ph.D. thesis, MM, Technical University of Denmark, 1996.

xxiv.        ChaimBroit, Optimal registration of deformed images, Ph.D. thesis, Computer and Information Science, UniPensylvania, 1981.

xxv.Gary E. Christensen and H. J. Johnson, Consistent image registration, IEEE Transaction on Medical Imaging 20 (2001), no. 7, 568–582.

xxvi.        Gary Edward Christensen, Deformable shape models for anatomy, Ph.D. thesis, Sever Institute of Technology, Washington University, 1994.

xxvii.      A. Collignon, A. Vandermeulen, P. Suetens, and G. Marchal, 3d multimodality medical image registration based on information theory, Kluwer Academic Publishers: Computational Imaging and Vision 3 (1995), 263–274.

xxviii.     R. Courant and David Hilbert, Methods of mathematical physiks, vol. II, Wiley, New York, 1962.

xxix.        Bernd Fischer and Jan Modersitzki, Fast inversion of matrices arising in image processing, Num. Algo. 22 (1999), 1–11.

xxx.Fast diffusion registration, AMS Contemporary Mathematics, Inverse Problems, Image Analysis, and Medical Imaging, vol. 313, 2002, pp. 117–129.

xxxi.        Combination of automatic non-rigid and landmark based registration: the best of both worlds, Medical Imaging 2003: Image Processing (J.M. Fitzpatrick M. Sonka, ed.), Proceedings of the SPIE 5032, 2003, pp. 1037–1048.

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