Araştırma Makalesi
BibTex RIS Kaynak Göster

Two Novel Filters for Cleaning Medical Images from Hair in Skin Cancer Diagnosis

Yıl 2023, , 1139 - 1149, 28.12.2023
https://doi.org/10.21605/cukurovaumfd.1410803

Öz

Low accessibility of traditional dermoscopic devices, since they are still expensive, as well as the lack of professional experience of expertized physicians, are the most obstructive factors in early diagnosis of skin cancer. Nevertheless, previous studies in this field have focused mainly on high-quality dermoscopic images rather than their digital counterparts, which are more economical and practical as they require less expertise during capturing. However, their exploitation in diagnosis is challenging due to the high presence of noise, resulting in an exhausting filtering process. One of the main difficulties regarding filtering is hair cleaning due to the wide variations in colour, shape, and thickness. Hair cleaning requires a comprehensive approach considering stringent data conservation, which is crucial in diagnosis as it may sabotage the diagnosis itself. The paper outlines two novel filters designed for this purpose and examines their performance with respect to two filters, which are extensively used in this field.

Kaynakça

  • 1. The International Agency for Research on Cancer (IARC), 2020. WHO-All Cancers, The Global Cancer Observatory. https://gco.iarc.fr /today/fact-sheets-cancers, Erişim tarihi: 01.02.2023, Lyon, France.
  • 2. Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., Bray, F., 2021. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries, CA: A Cancer Journal for Clinicians, 71(3), 209-249.
  • 3. Halk Sağlığı Genel Müdürlüğü, 2018 Yılı Türkiye Kanser İstatistikleri, https://hsgm.sagli k.gov.tr/depo/birimler/kanserdb/Dokumanlar/Istatistikler/Kanser_Rapor_2018.pdf, Erişim tarihi: 12.03.2023, Ankara.
  • 4. Açıkgöz, A., Çımrın, D., Ergör, G., 2018. Determination of Breast, Prostate, Colorectal and Lung Cancer Environmental Risk Factors and Risk Levels: Case-Control Study. Cukurova Medical Journal, 43(2), 411-421.
  • 5. Gültop, F., Özkan, S., 2022. The Importance of Health Literacy in Cancer Awareness. Turkish Bulletin of Hygiene and Experimental Biology, 79(3), 579-586.
  • 6. Siegel, R.L., Miller, K.D., Fuchs, H.E., Jemal, A., 2022. Cancer Statistics 2022, CA: A Cancer Journal for Clinicians, 72(1), 7-33.
  • 7. Demir, F., 2021. Derin Öğrenme Tabanlı Yaklaşımla Kötü Huylu Deri Kanserinin Dermatoskopik Görüntülerden Saptanması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 33(2), 617-624.
  • 8. Bisla, D., Choromanska, A., Berman, R.S., Stein, J.A., Polsky, D., 2019. Towards Automated Melanoma Detection with Deep Learning: Data Purification and Augmentation. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, 1-11.
  • 9. Codella, N.C., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., 2018. Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC). 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI’2018), Washington DC, 168-172.
  • 10. Lee, T., Ng, V., Gallagher, R., Coldman, A., McLean, D., 1997. Dullrazor®: A Software Approach to Hair Removal from Images, Computers in Biology and Medicine, 27(6), 533-543.
  • 11. Xie, F.Y., Qin, S.Y., Jiang, Z.G., Meng, R.S., 2009. PDE-Based Unsupervised Repair of Hair-Occluded Information in Dermoscopy Images of Melanoma, Computerized Medical Imaging and Graphics, 33(4), 275-282.
  • 12. Huang, A., Shun-Yuen, K., Wen-Yu, C., Min-Yin, L., Min-Hsiu, C., Gwo-Shing, C., 2013. A Robust Hair Segmentation and Removal Approach for Clinical Images of Skin Lesions. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, 3315-3318.
  • 13. Fiorese, M., Peserico, E., Silletti, A., 2011. VirtualShave: Automated Hair Removal from Digital Dermatoscopic Images. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Boston, 5145-5148.
  • 14. Attia, M., Hossny, M., Zhou, H., Nahavandi, S., Asadi, H., Yazdabadi, A., 2019. Digital Hair Segmentation Using Hybrid Convolutional and Recurrent Neural Networks Architecture. Computer Methods and Programs in Biomedicine, 177(2019), 17-30.
  • 15. Akyel, C., Arıcı, N., 2020. Cilt Kanserinde Kıl Temizliği ve Lezyon Bölütlemesinde Yeni Bir Yaklaşım. Politeknik Dergisi, 23(3), 821-828.
  • 16. Akyel, C., Arıcı, N., 2022. Hair Removal and Lesion Segmentation with FCN8-ResNetC and Image Processing in Images of Skin Cancer. Bilişim Teknolojileri Dergisi, 15(2), 231-238.
  • 17. Tschandl, P., Rosendahl, C., Kittler, H., 2018. The HAM10000 Dataset, A Large Collection of Multi-Source Dermatoscopic Images of Common Pigmented Skin Lesions. Scientific Data, 5(1), 1-9.
  • 18. Combalia, M., Codella, N., Rotemberg, V., Carrera, C., Dusza, S., Gutman, D., Helba, B., Kittler, H., Kurtansky, N.R., Liopyris, K., Marchetti, M.A., Podlipnik, S., Puig, S., Rinner, C., Tschandl, P., Weber, J., Halpern, A., Malvehy, J., 2022. Validation of Artificial Intelligence Prediction Models for Skin Cancer Diagnosis Using Dermoscopy Images: the 2019 International Skin Imaging Collaboration Grand Challenge. The Lancet Digital Health, 4(5), e330-e339.
  • 19. American Cancer Society, Skin Cancer Image Gallery, https://www.cancer.org/cancer/types/ skin-cancer/skin-cancer-image-gallery.html, Erişim tarihi: 04.06.2023.
  • 20. Gürsel, A.T., Yalçın, T., 2021. Improving Digital Image Quality for Convolution Neural Network Based Computer-Aided Diagnosis (CNN-CAD) of Skin Cancer. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 36(4), 1099-1110.
  • 21. Kumar, S., Kumar, P., Gupta, M., Nagawat, A. K., 2010. Performance Comparison of Median and Wiener Filter in Image De-noising. International Journal of Computer Applications, 12(4), 27-31.
  • 22. Gonzalez, R., Woods, R., 2002. Digital Image Processing. Prentice Hall, New Jersey, 261-266.
  • 23. Orieux, F., Giovannelli, J.F., Rodet, T., 2010. Bayesian Estimation of Regularization and Point Spread Function Parameters for Wiener–Hunt Deconvolution. Journal of the Optical Society of America A, 27(7), 1593-1607.
  • 24. Kushol, R., Kabir, Md. H., Salekin, M.S., Rahman, A.B.M.A., 2017. Contrast Enhancement by Top-Hat and Bottom-Hat Transform with Optimal Structuring Element: Application to Retinal Vessel Segmentation. Image Analysis and Recognition (ICIAR’2017), Montreal, Canada, 533-540.
  • 25. Zuiderveld, K.,1994. Contrast Limited Adaptive Histogram Equalization. Graphics Gems IV. Academic Press Professional, Inc., USA , 474-485.
  • 26. Pisano, E.D., Zong, S., Hemminger, B.M., DeLuca, M., Johnston, R.E., Muller, K., Braeuning, M.P., Pizer, S.M., 1998. Contrast Limited Adaptive Histogram Equalization Image Processing to Improve the Detection of Simulated Spiculations in Dense Mammograms. Journal of Digital Imaging, 11(4), 193-200.
  • 27. Parveen, N.R.S., Sathik, M.M., 2009. Enhancement of Bone Fracture Images by Equalization Methods. 2009 International Conference on Computer Technology and Development (ICCTD), Kota Kinabalu, 391-394.
  • 28. Murillo-Bracamontes, E.A., Martinez-Rosas, M.E., Miranda-Velasco, M.M., Martinez-Reyes, H.L., Martinez-Sandoval, J.R., Cervantes-de-Avila, H., 2012. Implementation of Hough Transform for Fruit Image Segmentation. Procedia Engineering, 35(2012), 230-239.
  • 29. Kohli, P., Chadha, A., 2019. Enabling Pedestrian Safety Using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash. Future of Information and Communication Conference (FICC 2019), San Francisco, CA, USA, 261-279.
  • 30. OpenCV, Image Thresholding, https://docs.op encv.org/4.x/d7/d4d/tutorial_py_thresholding.html, Erişim Tarihi: 13.09.2023.
  • 31. Bouganssa, I., Sbihi, M., Zaim, M., 2019. Laplacian Edge Detection Algorithm for Road Signal Images and FPGA Implementation. International Journal of Machine Learning and Computing, 9(1), 57-61.
  • 32. Dhar, R., Gupta, R., Baishnab, K.L., 2014. An Analysis of CANNY and LAPLACIAN of GAUSSIAN Image Filters in Regard to Evaluating Retinal Image. 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE’2014), Coimbatore, 1-6.

Cilt Kanseri Tanısında Tıbbi Görüntüleri Kıldan Temizlemek İçin Kullanılan İki Yeni Filtre

Yıl 2023, , 1139 - 1149, 28.12.2023
https://doi.org/10.21605/cukurovaumfd.1410803

Öz

Geleneksel dermoskopik cihazların pahalı olması nedeniyle ulaşılabilirliğinin düşük olması ve uzman hekimlerin mesleki deneyimlerinin yeterli olmayışı cilt kanserinin erken teşhisinde en engelleyici faktörlerdir. Ancak bu alanda daha önce yapılan çalışmalar, çekim sırasında daha az uzmanlık gerektirdiğinden daha ekonomik ve pratik olan dijital benzerlerinden ziyade ağırlıklı olarak yüksek kaliteli dermoskopik görüntülere odaklanmıştır. Bununla birlikte, gürültünün yüksek varlığı nedeniyle tanıda bunların kullanımı zordur ve bu da zahmetli bir filtreleme işlemine neden olur. Filtrelemeyle ilgili en büyük zorluklardan biri, renk, şekil ve kalınlıktaki büyük farklılıklar nedeniyle kılın temizlenmesidir. Kıl temizliği, teşhisin kendisini sabote edebileceğinden teşhis için çok önemli olan verilerin sıkı bir şekilde korunmasını dikkate alan kapsamlı bir yaklaşım gerektirir. Makalede bu amaç için tasarlanan iki yeni filtrenin ana hatları verilmekte ve bu alanda yaygın olarak kullanılan iki filtreye göre performansları incelenmektedir.

Kaynakça

  • 1. The International Agency for Research on Cancer (IARC), 2020. WHO-All Cancers, The Global Cancer Observatory. https://gco.iarc.fr /today/fact-sheets-cancers, Erişim tarihi: 01.02.2023, Lyon, France.
  • 2. Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., Bray, F., 2021. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries, CA: A Cancer Journal for Clinicians, 71(3), 209-249.
  • 3. Halk Sağlığı Genel Müdürlüğü, 2018 Yılı Türkiye Kanser İstatistikleri, https://hsgm.sagli k.gov.tr/depo/birimler/kanserdb/Dokumanlar/Istatistikler/Kanser_Rapor_2018.pdf, Erişim tarihi: 12.03.2023, Ankara.
  • 4. Açıkgöz, A., Çımrın, D., Ergör, G., 2018. Determination of Breast, Prostate, Colorectal and Lung Cancer Environmental Risk Factors and Risk Levels: Case-Control Study. Cukurova Medical Journal, 43(2), 411-421.
  • 5. Gültop, F., Özkan, S., 2022. The Importance of Health Literacy in Cancer Awareness. Turkish Bulletin of Hygiene and Experimental Biology, 79(3), 579-586.
  • 6. Siegel, R.L., Miller, K.D., Fuchs, H.E., Jemal, A., 2022. Cancer Statistics 2022, CA: A Cancer Journal for Clinicians, 72(1), 7-33.
  • 7. Demir, F., 2021. Derin Öğrenme Tabanlı Yaklaşımla Kötü Huylu Deri Kanserinin Dermatoskopik Görüntülerden Saptanması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 33(2), 617-624.
  • 8. Bisla, D., Choromanska, A., Berman, R.S., Stein, J.A., Polsky, D., 2019. Towards Automated Melanoma Detection with Deep Learning: Data Purification and Augmentation. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, 1-11.
  • 9. Codella, N.C., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., 2018. Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC). 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI’2018), Washington DC, 168-172.
  • 10. Lee, T., Ng, V., Gallagher, R., Coldman, A., McLean, D., 1997. Dullrazor®: A Software Approach to Hair Removal from Images, Computers in Biology and Medicine, 27(6), 533-543.
  • 11. Xie, F.Y., Qin, S.Y., Jiang, Z.G., Meng, R.S., 2009. PDE-Based Unsupervised Repair of Hair-Occluded Information in Dermoscopy Images of Melanoma, Computerized Medical Imaging and Graphics, 33(4), 275-282.
  • 12. Huang, A., Shun-Yuen, K., Wen-Yu, C., Min-Yin, L., Min-Hsiu, C., Gwo-Shing, C., 2013. A Robust Hair Segmentation and Removal Approach for Clinical Images of Skin Lesions. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, 3315-3318.
  • 13. Fiorese, M., Peserico, E., Silletti, A., 2011. VirtualShave: Automated Hair Removal from Digital Dermatoscopic Images. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Boston, 5145-5148.
  • 14. Attia, M., Hossny, M., Zhou, H., Nahavandi, S., Asadi, H., Yazdabadi, A., 2019. Digital Hair Segmentation Using Hybrid Convolutional and Recurrent Neural Networks Architecture. Computer Methods and Programs in Biomedicine, 177(2019), 17-30.
  • 15. Akyel, C., Arıcı, N., 2020. Cilt Kanserinde Kıl Temizliği ve Lezyon Bölütlemesinde Yeni Bir Yaklaşım. Politeknik Dergisi, 23(3), 821-828.
  • 16. Akyel, C., Arıcı, N., 2022. Hair Removal and Lesion Segmentation with FCN8-ResNetC and Image Processing in Images of Skin Cancer. Bilişim Teknolojileri Dergisi, 15(2), 231-238.
  • 17. Tschandl, P., Rosendahl, C., Kittler, H., 2018. The HAM10000 Dataset, A Large Collection of Multi-Source Dermatoscopic Images of Common Pigmented Skin Lesions. Scientific Data, 5(1), 1-9.
  • 18. Combalia, M., Codella, N., Rotemberg, V., Carrera, C., Dusza, S., Gutman, D., Helba, B., Kittler, H., Kurtansky, N.R., Liopyris, K., Marchetti, M.A., Podlipnik, S., Puig, S., Rinner, C., Tschandl, P., Weber, J., Halpern, A., Malvehy, J., 2022. Validation of Artificial Intelligence Prediction Models for Skin Cancer Diagnosis Using Dermoscopy Images: the 2019 International Skin Imaging Collaboration Grand Challenge. The Lancet Digital Health, 4(5), e330-e339.
  • 19. American Cancer Society, Skin Cancer Image Gallery, https://www.cancer.org/cancer/types/ skin-cancer/skin-cancer-image-gallery.html, Erişim tarihi: 04.06.2023.
  • 20. Gürsel, A.T., Yalçın, T., 2021. Improving Digital Image Quality for Convolution Neural Network Based Computer-Aided Diagnosis (CNN-CAD) of Skin Cancer. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 36(4), 1099-1110.
  • 21. Kumar, S., Kumar, P., Gupta, M., Nagawat, A. K., 2010. Performance Comparison of Median and Wiener Filter in Image De-noising. International Journal of Computer Applications, 12(4), 27-31.
  • 22. Gonzalez, R., Woods, R., 2002. Digital Image Processing. Prentice Hall, New Jersey, 261-266.
  • 23. Orieux, F., Giovannelli, J.F., Rodet, T., 2010. Bayesian Estimation of Regularization and Point Spread Function Parameters for Wiener–Hunt Deconvolution. Journal of the Optical Society of America A, 27(7), 1593-1607.
  • 24. Kushol, R., Kabir, Md. H., Salekin, M.S., Rahman, A.B.M.A., 2017. Contrast Enhancement by Top-Hat and Bottom-Hat Transform with Optimal Structuring Element: Application to Retinal Vessel Segmentation. Image Analysis and Recognition (ICIAR’2017), Montreal, Canada, 533-540.
  • 25. Zuiderveld, K.,1994. Contrast Limited Adaptive Histogram Equalization. Graphics Gems IV. Academic Press Professional, Inc., USA , 474-485.
  • 26. Pisano, E.D., Zong, S., Hemminger, B.M., DeLuca, M., Johnston, R.E., Muller, K., Braeuning, M.P., Pizer, S.M., 1998. Contrast Limited Adaptive Histogram Equalization Image Processing to Improve the Detection of Simulated Spiculations in Dense Mammograms. Journal of Digital Imaging, 11(4), 193-200.
  • 27. Parveen, N.R.S., Sathik, M.M., 2009. Enhancement of Bone Fracture Images by Equalization Methods. 2009 International Conference on Computer Technology and Development (ICCTD), Kota Kinabalu, 391-394.
  • 28. Murillo-Bracamontes, E.A., Martinez-Rosas, M.E., Miranda-Velasco, M.M., Martinez-Reyes, H.L., Martinez-Sandoval, J.R., Cervantes-de-Avila, H., 2012. Implementation of Hough Transform for Fruit Image Segmentation. Procedia Engineering, 35(2012), 230-239.
  • 29. Kohli, P., Chadha, A., 2019. Enabling Pedestrian Safety Using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash. Future of Information and Communication Conference (FICC 2019), San Francisco, CA, USA, 261-279.
  • 30. OpenCV, Image Thresholding, https://docs.op encv.org/4.x/d7/d4d/tutorial_py_thresholding.html, Erişim Tarihi: 13.09.2023.
  • 31. Bouganssa, I., Sbihi, M., Zaim, M., 2019. Laplacian Edge Detection Algorithm for Road Signal Images and FPGA Implementation. International Journal of Machine Learning and Computing, 9(1), 57-61.
  • 32. Dhar, R., Gupta, R., Baishnab, K.L., 2014. An Analysis of CANNY and LAPLACIAN of GAUSSIAN Image Filters in Regard to Evaluating Retinal Image. 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE’2014), Coimbatore, 1-6.
Toplam 32 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Biyomedikal Mühendisliği (Diğer), Elektrik Mühendisliği (Diğer)
Bölüm Makaleler
Yazarlar

Berceste Yılmaz 0000-0002-9424-9311

Amira Tandiroviç Gürsel 0000-0002-9219-3203

Yayımlanma Tarihi 28 Aralık 2023
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

APA Yılmaz, B., & Tandiroviç Gürsel, A. (2023). Cilt Kanseri Tanısında Tıbbi Görüntüleri Kıldan Temizlemek İçin Kullanılan İki Yeni Filtre. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 38(4), 1139-1149. https://doi.org/10.21605/cukurovaumfd.1410803