Deteksi Region of Interest Tulang pada Citra B-mode secara Otomatis Menggunakan Region Proposal Networks

Tita Karlita, I Made Gede Sunarya, Joko Priambodo, Rika Rokhana, Eko Mulyanto Yuniarno, I Ketut Eddy Purnama, Mauridhi Hery Purnomo

Abstract


Pencitraan tulang menggunakan ultrasound adalah sebuah teknik pencitraan yang aman karena bebas radiasi dan non-invasive. Namun, mendeteksi dan menentukan lokasi tulang untuk menemukan region of interest (RoI) adalah pekerjaan yang tidak mudah karena sifat citra b-mode yang memiliki derau yang tinggi dan reverberation artifacts. Kualitas citra dipengaruhi oleh pengguna dan batas antar area jaringan tidak jelas sehingga menyulitkan interpretasi. Dalam makalah ini, pendekatan deep learning menggunakan Region Proposal Networks diaplikasikan untuk mendeteksi RoI tulang dalam citra b-mode secara otomatis. Model arsitektur Faster Region-based Convolutional Neural Network disesuaikan agar dapat mendeteksi RoI tulang. Untuk mengevaluasi hasil, eksperimen secara in-vivo dilakukan menggunakan spesimen lengan manusia. Sebanyak 1.066 citra b-mode tulang dari enam subjek yang berbeda digunakan dalam fase latih dan fase pengujian. Metode yang diusulkan berhasil menentukan RoI tulang dengan baik dengan nilai mAP, akurasi deteksi, dan akurasi penempatan RoI masing-masing sebesar sebesar 0,87, 98,33%, dan 95.99%.

Keywords


B-mode; Deep Learning; Deteksi; Faster R-CNN; Tulang; Region of Interest; Region Proposal Networks; Ultrasound

Full Text:

PDF

References


D.A. Dharmawan, "Deteksi Kanker Serviks Otomatis Berbasis Jaringan Saraf Tiruan LVQ dan DCT," J. Nas. Tek. Elektro dan Teknol. Inf. Vol. 3, No. 4, hal. 269–272, 2014.

N.P. Husain dan C. Fatichah, "Segmentasi Citra Sel Tunggal Smear Serviks Menggunakan Radiating Component Normalized Generalized GVFS," J. Nas. Tek. Elektro dan Teknol. Inf., Vol. 6, No. 1, hal. 107–114, 2017.

N. Syakrani, Y. Widhiyasana, dan A.A. Efendi, "Deteksi Tumor Hati dengan Graph Cut dan Taksiran Volume Tumornya," J. Nas. Tek. Elektro dan Teknol. Inf., Vol. 7, No. 1, hal. 35-43,2018.

O. Herliana, T.S. Widodo, dan I. Soesanti, "Klasifikasi Nomsupervised Citra Thermal Kanker Payudara Berbasis Fuzzy C-MEANS," J. Nas. Tek. Elektro dan Teknol. Inf., Vol. 1, No. 3, hal. 55-59, 2012.

T. Karlita, E.M. Yuniarno, I.K.E. Purnama, dan M.H. Purnomo, "Automatic Bone Outer Contour Extraction from B-Modes Ultrasound Images Based on Local Phase Symmetry and Quadratic Polynomial," Second Int. Work. Pattern Recognit. (IWPR 2017) 2017, pp. 165–170.

P.J.S. Gonçalves dan P. Torres, "Extracting Bone Contours in Ultrasound Images: Energetic Versus Probabilistic Methods," Rom. Rev. Precis. Mech. Opt. Mechatronics. Vol. 20, No. 37, hal. 105–110, 2010.

I. Hacihaliloglu, P. Guy, A.J. Hodgson, dan R. Abugharbieh, "Automatic Extraction of Bone Surfaces from 3D Ultrasound Images in Orthopaedic Trauma Cases," Int. J. Comput. Assist. Radiol. Surg., Vol. 10, hal. 1279–1287, 2015.

J. Kowal, C. Amstutz, F. Langlotz, H. Talib, dan M.G. Ballester, Automated Bone Contour Detection in Ultrasound B-Mode Images For Minimally Invasive Registration in Computer-Assisted Surgery – An In Vitro Evaluation," Int. J. Med. Robot. Comput. Assist. Surg. MRCAS, Vol. 3, No. 4, hal. 341–348, 2007.

R.W. Prager, R.N. Rohling, A.H. Gee, dan L. Berman, "Rapid Calibration for 3-D Freehand Ultrasound," Ultrasound Med. Biol. Vol. 24, No. 6, hal. 855–869, 1998.

A.K. Jain dan R.H. Taylor, "Understanding Bone Responses in B-Mode Ultrasound Images and Automatic Bone Surface Extraction Using a Bayesian Probabilistic Framework," Proc. SPIE, Med. Imaging 2004 Ultrason. Imaging Signal Process., 2004, Vol. 5373, hal. 131-142.

V. Chan dan A. Perlas, "Basics of Ultrasound Imaging," in Atlas Ultrasound-Guided Proced. Interv. Pain Manag., S.N. Narouze, Ed., Toronto, ON, Canada, Springer Science+Business Media, 2011, hal. 13–20.

K.E. Purnama, M.H.F. Wilkinson, A.G. Veldhuizen, P.M.A. Van Ooijen, J. Lubbers, J.G.M. Burgerhof, T.A. Sardjono, dan G.J. Verkerke, "A Framework for Human Spine Imaging Using a Freehand 3D Ultrasound System," Technol. Heal. Care., Vol. 18, No. 1, hal. 1–17. 2010.

N. Baka, S. Leenstra, dan T. van Walsum, "Random Forest-Based Bone Segmentation in Ultrasound," Ultrasound Med. Biol., Vol. 43, No. 10, hal. 2426-2437, 2017.

N. Quader, A. Hodgson, dan R. Abugharbieh, Confidence Weighted Local Phase Features for Robust Bone Surface Segmentation in Ultrasound, Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), Cham, Switzerland: Springer, 2014, Vol. 8680, hal. 76–83.

R. Jia, S.J. Mellon, S. Hansjee, A.P. Monk, D.W. Murray, dan J.A. Noble, "Automatic Bone Segmentation in Ultrasound Images Using Local Phase Features and Dynamic Programming," IEEE 13th Int. Symp. Biomed. Imaging, 2016, hal. 1005–1008.

F. Berton, F. Cheriet, M.C. Miron, dan C. Laporte, "Segmentation of The Spinous Process and Its Acoustic Shadow in Vertebral Ultrasound Images," Comput. Biol. Med. Vol. 72, hal. 201–211, 2016.

L. Lopez-Perez, J. Lemaitre, A. Alfiansyah, dan M.-E. Bellemare, "Bone Surface Reconstruction Using Localized Freehand Ultrasound Imaging," 30th Annual International IEEE EMBS Conference, 2008, hal. 2964-2967.

I. Hacihaliloglu, R. Abugharbieh, A.J. Hodgson, dan R.N. Rohling, "Bone Surface Localization in Ultrasound Using Image Phase-Based Features," Ultrasound Med. Biol., Vol. 35, No. 9, hal. 1475–1487, 2009.

D. Yang, S. Zhang, Z. Yan, C. Tan, K. Li, dan D. Metaxas, "Automated Anatomical Landmark Detection on Distal Femur Surface Using Convolutional Neural Network," Proc. - Int. Symp. Biomed. Imaging, 2015, hal. 17–21.

H. Ravishankar, S.M. Prabhu, V. Vaidya, dan N. Singhal, "Hybrid Approach for Automatic Segmentation of Fetal Abdomen from Ultrasound Images Using Deep Learning," Proc. - Int. Symp. Biomed. Imaging, 2016, hal. 779–782.

J.C. Nascimento dan G. Carneiro, Multi-Atlas Segmentation Using Manifold Learning with Deep Belief Networks, Proc. - Int. Symp. Biomed. Imaging, 2016, hal. 867–871.

G. Carneiro dan J.C. Nascimento, "Combining Multiple Dynamic Models and Deep Learning Architectures for Tracking the Left Ventricle Endocardium in Ultrasound Data," IEEE Trans. Pattern Anal. Mach. Intell., Vol. 35, No. 11, hal. 2592–2607, 2013.

Y. Gao, M.A. Maraci, dan J.A. Noble, "Describing Ultrasound Video Content Using Deep Convolutional Neural Networks," Proc. - Int. Symp. Biomed. Imaging, 2016, hal. 787–790.

P.M.B. Torres, J.M. Sanches, P.J.S. Goncalves, dan J.M.M. Martins, "3D Femur Reconstruction Using a Robotized Ultrasound Probe," Proc. IEEE RAS EMBS Int. Conf. Biomed. Robot. Biomechatronics., 2012, hal. 884–888.

S. Ren, K. He, R. Girshick, dan J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," ArXiv Prepr. ArXiv1506.01497., Vol. 74, hal. 1–14, 2015.

(2018) "Colaboratory - Frequently Asked Questions," [Online] https://research.google.com/colaboratory/faq.html, tanggal akses: 12-Nov-2018.

T. Carneiro, R. Victor, M. Da, T. Nepomuceno, G. Bian, dan V.H.C.D.E. Albuquerque, "Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications," IEEE Access Trends, Perspect. Prospect. Mach. Learn. Appl. to Biomed. Syst. Internet Med. Things., Vol. 6, hal. 61677–61685, 2018.

H. Gao, (2017) "Faster R-CNN Explained - Medium," [Online] https://medium.com/@smallfishbigsea/faster-r-cnn-explained-864d4fb7e3f8, tanggal akses: 12-Nov-2018.




DOI: http://dx.doi.org/10.22146/jnteti.v8i1.492

Refbacks

  • There are currently no refbacks.


Copyright (c) 2019 Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI)

JNTETI (Jurnal Nasional Teknik Elektro dan Teknologi Informasi)

Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik Universitas Gadjah Mada
Jl. Grafika No 2. Kampus UGM Yogyakarta 55281
+62 274 552305
jnteti@ugm.ac.id