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Graph-based segmentation with homogeneous hue and texture vertices

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Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This work presents an automated segmentation method, based on graph theory, which processes superpixels that exhibit spatially similarities in hue and texture pixel groups, rather than individual pixels. The graph shortest path includes a chain of neighboring superpixels which have minimal intensity changes. This method reduces graphics computational complexity because it provides large decreases in the number of vertices as the superpixel size increases. For the starting vertex prediction, the boundary pixel in first column which is included in this starting vertex is predicted by a trained deep neural network formulated as a regression task. By formulating the problem as a regression scheme, the computational burden is decreased in comparison with classifying each pixel in the entire image. This feasibility approach, when applied as a preliminary study in electron microscopy and optical coherence tomography images, demonstrated high measures of accuracy: 0.9670 for the electron microscopy image and 0.9930 for vitreous/nerve-fiber and inner-segment/outer-segment layer segmentations in the optical coherence tomography image.
Czasopismo
Rocznik
Strony
541--549
Opis fizyczny
Bibliogr. 20 poz., rys.
Twórcy
autor
  • School of Biomedical Engineering, International University, Vietnam National University, Ho Chi Minh City, 720351, Vietnam
  • Department of Brain and Cognitive Engineering, Korea University, 145 Anam Rd., Seoul, 02841, South Korea
autor
  • Department of Brain and Cognitive Engineering, Korea University, 145 Anam Rd., Seoul, 02841, South Korea
  • Department of Artificial Intelligence, Korea University, 145 Anam Rd., Seoul, 02841, South Korea
Bibliografia
  • [1] YANG Y., Image segmentation based on fuzzy clus tering with neighb orhood information, Optica Applicata 39(1), 2009, pp. 135–147.
  • [2] TANG R., HAN J., ZHANG X., Efficient iris segmentation method with support vector domain description, Optica Applicata 39(2), 2009, pp. 365–374.
  • [3] NEWELL T., TILLOTSON B., PEARL H., MILLER A., Detection of electrical defects with SEMVision in semiconductor production mode manufacturing, [In] 2016 27th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC), Saratoga Springs, NY, USA, 2016, DOI: 10.1109/ASMC.2016.7491149.
  • [4] MALARVEL M., SETHUMADHAVAN G., BHAGI P.C.R., KAR S., THANGAVEL S., An improved version of Otsu’s method for segmentation of weld defects on X-radiography images, Optik 142, 2017, pp. 109–118, DOI: 10.1016/j.ijleo.2017.05.066.
  • [5] ROY A.G., CONJETI S., KARRI S.P.K., SHEET D., KATOUZIAN A., WACHINGER C., NAVAB N., ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks, Biomedical Optics Express 8(8), 2017, pp. 3627–3642, DOI: 10.1364/BOE.8.003627.
  • [6] KIM H., EOM T.J., KIM J.G., Vascular morphometric changes during tumor growth and chemotherapy in a murine mammary tumor model using OCT angiography: a preliminary study, Current Optics and Photonics 3(1), 2019, pp. 54–65.
  • [7] HAN J.-H., CHA J., Intraoperative imaging based on common-path time-domain reflectometry for brain tumor surgery, Optica Applicata 50(2), 2020, pp. 223–227, DOI: 10.37190/oa200205.
  • [8] GROOTJANS W., USMANIJ E.A., OYEN W.J.G., VAN DER HEIJDEN E.H.F.M., VISSER E.P., VISVIKIS D., HATT M., BUSSINK J., DE GEUS-OEI L.-F., Performance of automatic image segmentation algorithms for calculating total lesion glycolysis for early response monitoring in non-small cell lung cancer patients during concomitant chemoradiotherapy, Radiotherapy and Oncology 119(3), 2016, pp. 473–479, DOI: 10.1016/j.radonc.2016.04.039.
  • [9] DALILA F., ZOHRA A., REDA K., HOCINE C., Segmentation and classification of melanoma and benign skin lesions, Optik 140, 2017, pp. 749–761, DOI: 10.1016/j.ijleo.2017.04.084.
  • [10] ESTEVA A., KUPREL B., NOVOA R.A., KO J., SWETTER S.M., BLAU H.M., THRUN S., Dermatologist-level classification of skin cancer with deep neural networks, Nature 542(7639), 2017, pp. 115–118, DOI: 10.1038/nature21056.
  • [11] PENG B., ZHANG L., ZHANG D., A survey of graph theoretical approaches to image segmentation, Pattern Recognition 46(3), 2013, pp. 1020–1038, DOI: 10.1016/j.patcog.2012.09.015.
  • [12] CHIU S.J., LI X.T., NICHOLAS P., TOTH C.A., IZATT J.A., FARSIU S., Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation, Optics Express 18(18), 2010, pp. 19413–19428, DOI: 10.1364/OE.18.019413.
  • [13] ACHANTA R., SHAJI A., SMITH K., LUCCHI A., FUA P., SÜSSTRUNK S., SLIC superpixels compared to state-of-the-art superpixel methods, IEEE Transactions on Pattern Analysis and Machine Intelligence 34(11), 2012, pp. 2274–2282, DOI: 10.1109/TPAMI.2012.120.
  • [14] ZHANG S., WANG H., HUANG W., YOU Z., Plant diseased leaf segmentation and recognition by fusion of superpixel, K-means and PHOG, Optik 157, 2018, pp. 866–872, DOI: 10.1016/j.ijleo.2017.11.190.
  • [15] NGO L., CHA J., HAN J.-H., Deep neural network regression for automated retinal layer segmentation in optical coherence tomography images, IEEE Transactions on Image Processing 29, 2020, pp. 303–312, DOI: 10.1109/TIP.2019.2931461.
  • [16] DOU Q., CHEN H., YU L., ZHAO L., QIN J., WANG D., MOK V.C.T., SHI L., HENG P.-A., Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks, IEEE Transactions on Medical Imaging 35(5), 2016, pp. 1182–1195, DOI: 10.1109/TMI.2016.2528129.
  • [17] PENG H., LI B., LING H., HU W., XIONG W., MAYBANK S.J., Salient object detection via structured matrix decomposition, IEEE Transactions on Pattern Analysis and Machine Intelligence 39(4), 2017, pp. 818–832, DOI: 10.1109/TPAMI.2016.2562626.
  • [18] BISHOP C.M., Pattern Recognition and Machine Learning, Springer, New York, NY, USA, 2016.
  • [19] YASUI Y., FUJISAWA K., GOTO K., NUMA-optimized parallel breadth-first search on multicore single-node system, [In] 2013 IEEE InternationalConference on Big Data, Silicon Valley, CA, USA, 2013, DOI: 10.1109/BigData.2013.6691600.
  • [20] OSTER S.F., MOJANA F., BRAR M., YUSON R.M.S., CHENG L., FREEMAN W.R., Disruption of the photoreceptor inner segment/outer segment layer on spectral domain-optical coherence tomography is a predictor of poor visual acuity in patients with epiretinal membranes, Retina 30(5), 2010, pp. 713–718, DOI: 10.1097/IAE.0b013e3181c596e3.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b94d707a-5925-455e-ad4d-c6500d19a049
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