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Makine Görme Teknolojisini Kullanarak Zaman ve Maliyeti Verimli Damar Tesbiti ve Enjeksiyon Noktası Önerisi

Year 2019, Volume: 2 Issue: 1, 58 - 64, 18.07.2019

Abstract

Damar tespiti veneponksiyon için önemli bir görevdir.
Aşırı venopunktur, deneyimli bir tıbbi personelin bulunmaması nedeniyle acil
durumlarda ciddi sorunlara neden olabilir. Bu yazıda, zaman ve düşük maliyetli
portatif ven örüntü tanıma sistemi önerilmiştir. Near Infrared Light (NIR)
görüntüsü elde etmek için bir ven kamera mobil uygulaması kullanılır ve daha
sonra ven desenini ve enjeksiyon noktasını bulmak için NIR görüntüsüne bir dizi
makine görme algoritması uygulanır. Kızılötesi Radyasyona (IR) duyarlı kamera,
belirli bir dalga boyu aralığında bir Yakın Kızılötesi ışık (NIR) üretmek için
kullanılabilir. Bu kamera, çekilen görüntüdeki damar bölgesi ve etrafındaki
dokular arasında kabaca bir parlaklık farkı yaratır. Damar bölgeleri,
görüntüdeki çevre dokulara kıyasla daha koyu görünüyor. Geçmişte yapılan
çalışmalar, maliyet, zaman ve taşınabilirliğin, bu tür kamera sistemlerinin
uygulanması sırasında karşılaşılan başlıca zorluklar olduğunu gösteriyor.
Pahalı bir IR kamera tasarlamak ve kullanmak yerine kızılötesi bir görüntü elde
etmek için bir ven kamera mobil uygulaması kullanılarak bu zorlukların
üstesinden gelinebilir. Bir ven kamera uygulaması tarafından yakalanan
görüntülerin kalitesi, pahalı bir IR kamera tarafından çekilen görüntülerin
kalitesi ile hemen hemen aynıdır. Yakalanan görüntü, ortanca filtreleme,
Kontrast sınırlı uyarlamalı histogram eşitleme (CLAHE) işlemi, adaptif
eşikleme, morfolojik 
Yakalanan görüntü, ortanca
filtreleme, Kontrast sınırlı uyarlamalı histogram eşitleme (CLAHE) işlemi, adaptif
eşikleme, morfolojik işlemler, çevre çıkarma ve damar bölgesini belirlemek için
mesafe dönüşümü gibi işlemler dizisi ile işlenir ve enjeksiyon için bir yer
önerir
. Özellikle, CLAHE kontrast geliştirme için
kullanılan kilit işlemdir. Damar paterni tespit problemini çözecek teknikler
olmasına rağmen, ilk defa damar paterni tanıma için Yakın Kızılötesi Işığa
(NIR) görüntülere CLAHE işlemini içeren bir makine vizyon algoritması
uygulanır. Önerilen algoritma aynı zamanda damar delinmesi için çok önemli olan
enjeksiyon noktası önerisi de yapabilir. Yaklaşımımız MATLAB yazılımında
uygulanmıştır ve hem açık hem de koyu ten tonlarına uygulanabilir. Değişken
cilt tonlarına (açık ve koyu) 21 katılımcıyla yapılan değerlendirmeler,
önerilen yaklaşımın özellikle el arkasındaki (% 95.24 doğrulukla) ve el
bileğinde (% 76.19 doğrulukla) damar desenlerini tespit etmede etkili olduğunu
göstermektedir.

References

  • [1] Kuensting LL, DeBoer S, Holleran R, Shultz BL, Steinmann RA, Venella J: Difficult venous access in children: taking control. J Emerg Nurs. 2009, 35 (5): 419-424. 10.1016/j.jen.2009.01.014. PMID:19748021
  • [2] Larsen P, Eldridge D, Brinkley J, Newton D, Goff D, Hartzog T, Saad ND, Perkin R: Pediatric peripheral intravenous access: Does nursing experience and competence really make a difference?. J Infus Nurs. 2010, 33 (4): 226-235. 10.1097/NAN.0b013e3181e3a0a8. PMID:20631584
  • [3] Hadaway LC, Mill DA: On the road to successful IV starts. Nursing. 2005, 35 (Suppl O): 1-14. 10.1097/00152193-200505001-00001. PMID:15855836
  • [4] Yen K, Riegert A, Gorelick MH: Derivation of the DIVA score: a clinical prediction rule for the identification of children with difficult intravenous access. Pediatr Emerg Care. 2008, 24 (3): 143-147. 10.1097/PEC.0b013e3181666f32. PMID:18347490
  • [5] Ravi Varma N, Sandip D. Sahane, Sachin S. Thakre: Infrared Veinviewer A R Digitech International Journal Of Engineering, Education And Technology (ARDIJEET). ISSN 2320-883X, Volume 2 ISSUE 1, 2014.
  • [6] M. A. A. Hegazy, M. H. Cho, M. H. Cho, and S. Y. Lee, “U-net based metal segmentation on projection domain for metal artifact reduction in dental CT,” Biomed. Eng. Lett., Apr. 2019.
  • [7] T. Nochino, Y. Ohno, T. Kato, M. Taniike, and S. Okada, “Sleep stage estimation method using a camera for home use,” Biomed. Eng. Lett., Apr. 2019.
  • [8] “Analytic simulator and image generator of multiple-scattering Compton camera for prompt gamma ray imaging | SpringerLink.” [Online]. Available: https://link.springer.com/article/10.1007/s13534-018-0083-2. [Accessed: 30-Apr-2019].
  • [9] Zharov VP, Ferguson S, Eidt JF, Howard PC, Fink LM, Waner M: Infrared imaging of subcutaneous veins. Lasers Surg Med. 2004, 34 (1): 56-61. 10.1002/lsm.10248. PMID:14755425
  • [10] Mansoor M, Sravani SN, Naqvi SZ, Zahra Naqvi S, Badshah I, Saleem M: Real-time low-cost infrared vein imagingsystem.International Conference of Signal Processing, Image Processing & Pattern Recognition (ICSIPR), 2013:117–121. doi:10.1109/ICSIPR.2013.6497970
  • [11] Roggan A, Friebel M, Dorsch K, Hahn A, Muller G: Optical properties of circulating human blood in the wavelength range 400–2500 nm. J Biomed Opt. 1999, 4 (1): 36-46. 10.1117/1.429919. PMID:23015168
  • [12] Soujanya Ganesh: Depth And Sizi Limits For The Visibility of Veins Using The Veinviewer Imaging System, Graduate Program in Biomedical Engineering From the University of Tennessee And The University of Memphis. May 2007.
  • [13] R. C. Gonzalez and R. E. Woods, Digital Image Processing. Boston, MA, USA: Addison-Wesley, 2008.
  • [14] M. Kaur and J. Kaur, Survey of contrast enhancement techniques based on histogram equalization, Int. J. Adv Comput. Sci. Appl., vol. 2, no. 7, pp. 137–141, 2011.
  • [15] T. Arici, S. Dikbas, and Y. Altunbasak, A histogram modification framework and its application for image contrast enhancement, IEEE Trans. Image Process., vol. 18, no. 9, pp. 1921–1935, Sep. 2009.
  • [16] R. C. Gonzalez, R. E. Woods and S. L. Eddins, Digital Image Processing, 2nd Edition, Pearson Education, New Jersey, 2002.
  • [17] Naoya Tobisawa, Takeshi Namita, Yuji Kato and Koichi Shimizu, Injection Assist System with Surface and Transillumination Images, IEEE 2011.
  • [18] Manam Mansoor1, Sravani.S.N2 , Sumbul Zahra Naqvi, Imran Badshah , Mohammed Saleem “Real-time law cast infrared vein imaging system” International Conference on Signal Processing, Image Processing and Pattern Recognition [ICSIPR] 2013.
  • [19] Deepak Prasanna.R a*, Neelamegam.P a, Sriram.S b, Nagarajan Raju a,“Enhancement of vein patterns in hand image for biometricand biomedical application using various image enhancementtechniques”, International Conference On Modeling Optimization And Computing 2012.
  • [20] A. Marcotti, M. B. Hidalgo And L. Mathé “Non-Invasive Vein Detection Method Usinginfrared Light” Ieee Latin America Transactions, Vol. 11, No. 1, Feb. 2013.
  • [21] Chin Lung Lin and Kuo-Chin Fan, Biometric Verification using Thermal images of palm dorsa vein patterns, IEEE Transactions circuit System 14(2):199-2-213.
  • [22] Alexandre Amato, “Vein Camera” Available: Apple store.
  • [23] Y.-T. Kim, Contrast enhancement using brightness preserving bihistogram equalization, IEEE Trans. Consum. Electron., vol. 43, no. 1, pp. 1–8, Feb. 1997.
  • [24] P. K. Sinha and Q. H. Hong, “An improved median filter,” IEEE Trans. Med. Imaging, vol. 9, no. 3, pp. 345–346, Sep. 1990.
  • [25] S. Sulochana and R.Vidhya, “Image Denoising using Adaptive Thresholding in Framelet Transform Domain,” Int. J. Adv. Comput. Sci. Appl. IJACSA, vol. 3, no. 9, 2012.
  • [26] “Medical Image Segmentation - an overview | ScienceDirect Topics.” [Online]. Available: https://www.sciencedirect.com/topics/engineering/medical-image-segmentation. [Accessed: 03-May-2019].
  • [27] H. Hassanpour, N. Samadiani, and S. M. Mahdi Salehi, “Using morphological transforms to enhance the contrast of medical images,” Egypt. J. Radiol. Nucl. Med., vol. 46, no. 2, pp. 481–489, Jun. 2015.
  • [28] “Large Scale Image Feature Extraction from Medical Image Analysis | IJAERS Journal - Academia.edu.” [Online]. Available: https://www.academia.edu/21513225/Large_Scale_Image_Feature_Extraction_from_Medical_Image_Analysis. [Accessed: 03-May-2019].
  • [29] “Medical Image Fusion: A Brief Introduction | Biomedical and Pharmacology Journal.” [Online]. Available: https://biomedpharmajournal.org/vol11no3/medical-image-fusion-a-brief-introduction/. [Accessed: 03-May-2019].

Time and Cost Efficient Vein Pattern Recognition and Injection Point Suggestion using Machine Vision Technology

Year 2019, Volume: 2 Issue: 1, 58 - 64, 18.07.2019

Abstract

Vein detection is an important task for venepuncture. Excessive venepuncture can cause significant problems during an emergency situation due to the lack of experienced medical staff. In this paper, time and cost-efficient portable vein pattern recognition system is proposed. A vein camera mobile application is used to obtain a Near-Infrared Light (NIR) image and then a set of machine vision algorithms are applied to the NIR image in order to find a vein pattern and injection point. Infrared Radiation (IR) sensitive camera can be utilized to produce a Near-Infrared light (NIR) in a specified wavelength range. This camera roughly creates a brightness difference between the vein region and surrounding tissues in the captured image. The vein regions appear to be darker in comparison to surrounding tissues in the images. Past studies depict that cost, time and portability are the main challenges faced during the implementation of this type of camera systems. These challenges can be overcome by using a vein camera mobile application to take an infrared image instead of designing and using an expensive IR camera. The quality of the images captured by a vein camera application is almost the same as the quality of the images captured by an expensive IR camera. The captured image is processed by a sequence of operations such as median filtering, Contrast-limited adaptive histogram equalization (CLAHE) operation, adaptive thresholding, morphological operations, perimeter extraction, and distance transform to determine the vein region, and suggests a location for injection. In particular, CLAHE is the key operation that is employed for contrast enhancement. Although there are techniques to handle vein pattern detection problem, this is the first time a machine vision algorithm including the CLAHE operation is applied to Near-Infrared Light (NIR) images for vein pattern recognition. The proposed algorithm is also capable of injection point suggestion which is very important for venepuncture. Our approach is implemented in MATLAB software and can be applied to both fair and dark skin tones. Evaluations with 21 participants with varying skin tones (fair and dark) show that the proposed approach is especially effective for detecting vein patterns at the back of the hand (with 95.24% accuracy) and wrist (with 76.19% accuracy).

References

  • [1] Kuensting LL, DeBoer S, Holleran R, Shultz BL, Steinmann RA, Venella J: Difficult venous access in children: taking control. J Emerg Nurs. 2009, 35 (5): 419-424. 10.1016/j.jen.2009.01.014. PMID:19748021
  • [2] Larsen P, Eldridge D, Brinkley J, Newton D, Goff D, Hartzog T, Saad ND, Perkin R: Pediatric peripheral intravenous access: Does nursing experience and competence really make a difference?. J Infus Nurs. 2010, 33 (4): 226-235. 10.1097/NAN.0b013e3181e3a0a8. PMID:20631584
  • [3] Hadaway LC, Mill DA: On the road to successful IV starts. Nursing. 2005, 35 (Suppl O): 1-14. 10.1097/00152193-200505001-00001. PMID:15855836
  • [4] Yen K, Riegert A, Gorelick MH: Derivation of the DIVA score: a clinical prediction rule for the identification of children with difficult intravenous access. Pediatr Emerg Care. 2008, 24 (3): 143-147. 10.1097/PEC.0b013e3181666f32. PMID:18347490
  • [5] Ravi Varma N, Sandip D. Sahane, Sachin S. Thakre: Infrared Veinviewer A R Digitech International Journal Of Engineering, Education And Technology (ARDIJEET). ISSN 2320-883X, Volume 2 ISSUE 1, 2014.
  • [6] M. A. A. Hegazy, M. H. Cho, M. H. Cho, and S. Y. Lee, “U-net based metal segmentation on projection domain for metal artifact reduction in dental CT,” Biomed. Eng. Lett., Apr. 2019.
  • [7] T. Nochino, Y. Ohno, T. Kato, M. Taniike, and S. Okada, “Sleep stage estimation method using a camera for home use,” Biomed. Eng. Lett., Apr. 2019.
  • [8] “Analytic simulator and image generator of multiple-scattering Compton camera for prompt gamma ray imaging | SpringerLink.” [Online]. Available: https://link.springer.com/article/10.1007/s13534-018-0083-2. [Accessed: 30-Apr-2019].
  • [9] Zharov VP, Ferguson S, Eidt JF, Howard PC, Fink LM, Waner M: Infrared imaging of subcutaneous veins. Lasers Surg Med. 2004, 34 (1): 56-61. 10.1002/lsm.10248. PMID:14755425
  • [10] Mansoor M, Sravani SN, Naqvi SZ, Zahra Naqvi S, Badshah I, Saleem M: Real-time low-cost infrared vein imagingsystem.International Conference of Signal Processing, Image Processing & Pattern Recognition (ICSIPR), 2013:117–121. doi:10.1109/ICSIPR.2013.6497970
  • [11] Roggan A, Friebel M, Dorsch K, Hahn A, Muller G: Optical properties of circulating human blood in the wavelength range 400–2500 nm. J Biomed Opt. 1999, 4 (1): 36-46. 10.1117/1.429919. PMID:23015168
  • [12] Soujanya Ganesh: Depth And Sizi Limits For The Visibility of Veins Using The Veinviewer Imaging System, Graduate Program in Biomedical Engineering From the University of Tennessee And The University of Memphis. May 2007.
  • [13] R. C. Gonzalez and R. E. Woods, Digital Image Processing. Boston, MA, USA: Addison-Wesley, 2008.
  • [14] M. Kaur and J. Kaur, Survey of contrast enhancement techniques based on histogram equalization, Int. J. Adv Comput. Sci. Appl., vol. 2, no. 7, pp. 137–141, 2011.
  • [15] T. Arici, S. Dikbas, and Y. Altunbasak, A histogram modification framework and its application for image contrast enhancement, IEEE Trans. Image Process., vol. 18, no. 9, pp. 1921–1935, Sep. 2009.
  • [16] R. C. Gonzalez, R. E. Woods and S. L. Eddins, Digital Image Processing, 2nd Edition, Pearson Education, New Jersey, 2002.
  • [17] Naoya Tobisawa, Takeshi Namita, Yuji Kato and Koichi Shimizu, Injection Assist System with Surface and Transillumination Images, IEEE 2011.
  • [18] Manam Mansoor1, Sravani.S.N2 , Sumbul Zahra Naqvi, Imran Badshah , Mohammed Saleem “Real-time law cast infrared vein imaging system” International Conference on Signal Processing, Image Processing and Pattern Recognition [ICSIPR] 2013.
  • [19] Deepak Prasanna.R a*, Neelamegam.P a, Sriram.S b, Nagarajan Raju a,“Enhancement of vein patterns in hand image for biometricand biomedical application using various image enhancementtechniques”, International Conference On Modeling Optimization And Computing 2012.
  • [20] A. Marcotti, M. B. Hidalgo And L. Mathé “Non-Invasive Vein Detection Method Usinginfrared Light” Ieee Latin America Transactions, Vol. 11, No. 1, Feb. 2013.
  • [21] Chin Lung Lin and Kuo-Chin Fan, Biometric Verification using Thermal images of palm dorsa vein patterns, IEEE Transactions circuit System 14(2):199-2-213.
  • [22] Alexandre Amato, “Vein Camera” Available: Apple store.
  • [23] Y.-T. Kim, Contrast enhancement using brightness preserving bihistogram equalization, IEEE Trans. Consum. Electron., vol. 43, no. 1, pp. 1–8, Feb. 1997.
  • [24] P. K. Sinha and Q. H. Hong, “An improved median filter,” IEEE Trans. Med. Imaging, vol. 9, no. 3, pp. 345–346, Sep. 1990.
  • [25] S. Sulochana and R.Vidhya, “Image Denoising using Adaptive Thresholding in Framelet Transform Domain,” Int. J. Adv. Comput. Sci. Appl. IJACSA, vol. 3, no. 9, 2012.
  • [26] “Medical Image Segmentation - an overview | ScienceDirect Topics.” [Online]. Available: https://www.sciencedirect.com/topics/engineering/medical-image-segmentation. [Accessed: 03-May-2019].
  • [27] H. Hassanpour, N. Samadiani, and S. M. Mahdi Salehi, “Using morphological transforms to enhance the contrast of medical images,” Egypt. J. Radiol. Nucl. Med., vol. 46, no. 2, pp. 481–489, Jun. 2015.
  • [28] “Large Scale Image Feature Extraction from Medical Image Analysis | IJAERS Journal - Academia.edu.” [Online]. Available: https://www.academia.edu/21513225/Large_Scale_Image_Feature_Extraction_from_Medical_Image_Analysis. [Accessed: 03-May-2019].
  • [29] “Medical Image Fusion: A Brief Introduction | Biomedical and Pharmacology Journal.” [Online]. Available: https://biomedpharmajournal.org/vol11no3/medical-image-fusion-a-brief-introduction/. [Accessed: 03-May-2019].
There are 29 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Zakria Qadir 0000-0002-9596-1765

Cem Direkoğlu

Publication Date July 18, 2019
Submission Date May 28, 2019
Published in Issue Year 2019 Volume: 2 Issue: 1