上頜竇是感染或發炎最常見的部位，可以透過枕骨透視方向之頭顱放射性影像（SXR）診斷。在開發電腦輔助診斷之前，需要先在上頜竇內進行區域分割。這項研究提出了一個電腦輔助檢測系統，從透過基於紋理的新形態分析技術（ToMA），增強原始的 SXR 影像並分割出上頜竇。影像分割使用基於增強圖像的監督式數據集訓練的可轉移的全卷積網路（T-FCN）。T-FCN 經過多個學習階段的訓練，這使得網路能夠根據較新的數據集調整權重的再利用率。根據實驗結果，提出的 CAD 系統達到 85.70% 的分割準確率，同時訓練時間在增強 SXR 數據集縮短了 50%。

Maxillary sinuses are the most prevalent locations for infection or inflammation that can be diagnosed through occipitomental-view skull radiography (SXR). Prior to the development of computer-aided diagnosis, region segmentation within maxillary sinuses is required. This study presents a computer-aided detection system that segments maxillary sinuses from plain SXR images that have been enhanced through novel texture-based morphological analysis (ToMA). The segmentation incorporates transferable fully convolution network (T-FCN) that trains the network based on supervised data of enhanced images. T-FCN is trained with multiple learning stages, which enables reutilization of network weights to be adjusted based on newer dataset. According to the experiments, the proposed CAD system achieved segmentation accuracy at 85.70% with 50% faster learning time on the enhanced SXR dataset.

[1] U.S. National Library of Medicine, “X-Rays,”MedlinePlus, Mar. 2016. [Online].Available: https://medlineplus.gov/xrays.html.
[2] U.S. National Library of Medicine, “Computed Tomography,”MedlinePlus, Sep. 2016. [Online]. Available: https:// www.nibib.nih.gov/ science-education/science-topics/computed-tomography-ct
[3] A.G. Filler,“The history, development and impact of computed imaging in neurological diagnosis and neurosurgery: CT, MRI and DTI,”Nature Precedings, Jul. 2009. [Online]. Available: http://dx.doi.org/10.1038/npre.2009.3267.5.
[4] P. Allisy-Roberts and J. Williams, “Farr’s physics for medical imaging,”Elsevier Publishing. 2008. 2nd Ed., Ch. 1. Available: https:// doi.org/ 10.1016/B978-0-7020-2844-1.50005-3.
[5] U. Neitzel, “Status and prospects of digital detector technology for CR and DR,”Radiation Protection Dosimetry. vol. 114, no. 1, pp. 32-38. Available: https://doi.org. 10.1093/rpd/nch532.
[6] A. Rose, “Vision: human and electronic,”Plenum Press. 1974.
[7] E. Konen, M. Faibel, Y. Kleinbaum, M. Wolf, A. Lusky, C. Hoffman, A. Eval, and R. Tadmor, “The value of the occipitomental (Waters’) view in diagnosis of sinusitis: a comparative study with computer tomography,”Clinical Radiology, vol. 55, no. 11, pp. 856-860, 2000.
[8] N. Bhattacharyya and M.P. Fried, “The accuracy of computed tomography in the
diagnosis of chronic rhinosinusitis,”Laryngoscope, vol. 113, no. 1, pp. 125-129, 2003.
[9] T.M. Aaløkken, T. Hagtvedt, I. Dalen, and A. Kolbenstvedt, “Conventional sinus radiography compared with CT in the diagnosis of acute sinusitis,”Dentomaxillofacial Radiology, vol. 32, no. 1, pp. 60-62, 2003.
[10] P.-Y. Chiu, J.-D. Chen, C.-F. Chang, J.-W. Wei, and C.-Y. Chang, “The diagnostic value of sinus radiography in the evaluation of sinusitis,”Chinese Journal of Radiology, vol. 35, no. -, pp. 143-148, 2010.
[11] Anonymous,“Doses from medical X-Ray procedures,”Health Physics Society, vol.-, no. -, pp. 1-5, Available: https://hps.org/ (Accessed on Jan. 2018).
[12] D. Brenner and J. Hall, “Computed tomography - an increasing source of radiation exposure,”The New England Journal of Medicine, vol 357, pp. 2277-2284, 2007.
[13] F.A. Mettler, W. Huda, T.T. Yoshizumi, and M. Mahesh, “Effective doses in radiology and diagnostic nuclear medicine: a catalog,”RSNA Radiology, vol. 248, no. 1, pp. 254-263, 2008.
[14] J. Giles, “Study warns of ‘avoidable’ risks of CT scans,”Nature, vol. 431, pp. 391-391, 2004.
[15] D. Brenner, C. Elliston, E. Hal, and W. Berdon,“Estimated risks of radiation-induced fatal cancer from pediatric CT,”AJR American Journal of Roentgenology, vol 176, pp. 289-296, 2001.
[16] U.S. Food and Drug Administration, “What are the radiation risks from CT?,”Radiation-Emitting Products. Available: https:// www.fda.gov/ (Accessed on Jan. 2018).
[17] J. Nol, G. Isouard, and J. Mirecki, “Uncovering the causes of unnecessary repeated medical imaging examinations, or part of, in two hospital deparments,”The Radiographer, vol. 52, no. 3, pp. 26-31, 2005.
[18] D.L. Hamilos, “Chronic sinusitis,”Journal of Allergy and Clinical Immunology, vol. 106, pp. 213-227, 2000.
[19] L.R. Willett, J.L. Carson, and J.W. Williams Jr,“Current diagnosis and management of sinusitis,”Journal of General Internal Medicine, vol. 9, pp. 38-45, 1994.
[20] R.H. Lazar, R.T. Younis, and L.S. Parvey,“Comparison of plain radiographic coronal CT, and intraoperative findings in children with chronic sinusitis,”Otolaryngology Head and Neck Surgery, vol. 102, pp. 29-34, 1992.
[21] J.W. Williams Jr, L. Roberts Jr, B. Distell, and D.L. Simel,“Diagnosing sinusitis by X-ray: Is a single waters’view adequate?,”Journal of General Internal Medicine, vol. 7, pp. 481-485, 1992.
[22] A.O. Hussein, B.H. Ahmed, M.A.A. Omer, M.F.M. Manafal, and A.B. Elhaj, “As-
sessment of clinical X-ray and CT in diagnosis of paranasal sinus diseases,”International Journal of Science and Research, vol. 3, pp. 7–11, 2014.
[23] S.M. Pizer, E.P. Amburn, J.D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B.T. Haar Romeny, and J.B. Zimmerman, “Adaptive histogram equalization and its variations,”Computer Vision, Graphics, and Image Processing, vol. 39, pp. 355–368, 1987.
[24] A. Bahn, A. Thakur, and G. Vyas,“Analysis of histogram based compound contrast enhancement with noise reduction method for endodontic therapy,”in Proceedings of International Conference of the Next Generation Information Technolology Summit, Noida, India, 2014, pp. 533–537.
[25] I. L. Iznita, S. A. Vijanth, P. A. Venkatachalam, and S. N. Lee, “Computerized segmentation of sinus images,”in Proceedings Innovative Technologies in Intelligent Systems and Industrial Applications, Monash, Australia, 2009, pp. 125–128.
[26] W. De Vos, J. Casselman, and G. R. J. Swennen,“Cone-beam computerized tomography (CBCT) imaging of the oral and maxillofacial region: a systematic review of the literature,”International Journal of Oral Maxillofacial Surgery, vol. 38, pp. 609–625, 2009.
[27] M. Sundaram, K. Ramar, N. Arumugam, and G. Prabin,“Histogram-based contrast
enhancement for mammogram images,”in Proceedings of International Conference of
Signal Processing Communication, and Computer Network Technology, Thuckalay, India, pp. 2011, pp. 842-846.
[28] H. Ibrahim and S.C. Hoo, “Local contrast enhancement utilizing bidirectional switching equalization of separated and clipped subhistograms,”Mathematical Problem Engineering, vol. 2014, Art. no 848615. DOI:10.115/2014/848615.
[29] Y.-T. Kim, “Contrast enhancement using brightness preserving bi-histogram equalization,”IEEE Transactions on Consumer Electronics, vol. 43, no. 1, pp. 1–8, 1997.
[30] C.-S. Hung and S.-J. Ruan, ”Efficient adaptive thresholding algorithm for inhomogeneous document background removal,”Multimedia Tools and Applications,
vol. 75, no. 2, Jan. 2016, pp. 1243-1259.
[31] Z. Wei, Y. Hua S. Hui-Sheng, and F. Hong-qi, “X-Ray image enhancement based on multiscale morphology,”in Proceedings og the 1st International Conference of
Bioinformatics and Biomedical Engineering, Wuhan, China, 2007, pp. 702-705.
[32] N. Dey, A.S. Ashour, S. Beagum, D.S. Psitola, M. Gospodinov, E.P. Gospodinova, and J.M.R.S. Tavares, “Parameter optimization for local polynomial approximation based intersection confidence interval filter using genetic algorithm: an application for brain MRI image de-noising,”Journal of Imaging, vol. 1, pp. 60-84–625, 2015.
[33] R.K. Srivastava, K. Greff, and J. Schmidhuber,“Training very deep networks,”Computing Research Repository, vol. abs/1507.06228, [Online], 2015. Available: arXiv: 1507.06228.
[34] X. Liang, S. Liu, Y. Wei, L. Liu, L. Lin, and S. Yan, “Computational baby learning,”Computing Research Repository, vol. abs/1411.2861, [Online], 2014. Available: arXiv:1411.2861.
[35] A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,”Computing Research Repository, vol. abs/1511.06434, [Online], 2015. Available: arXiv:1511.06434.
[36] I.J. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio, “Max-out Networks,”Computing Research Repository, vol. abs/1302.4389, [Online], 2013. Available: arXiv:1302.4389.
[37] I.J. Goodfellow, Y. Bulatov, J. Ibarz, S. Arnoud, and V. Shet, “Multi-digit number recognition from Street View imagery using deep convolutional neural networks,”Computing Research Repository, vol. abs/ 1312.6082, [Online], 2014. Available: arXiv:1312.6082.
[38] L.J. Ba and R. Caruana,“Do deep nets really need to be deep,”Computing Research Repository, vol. abs/1312.6184, [Online], 2014. Available: arXiv:1312.6184.
[39] S. Zagoruyko and N. Komodakis, “Wide residual networks,”Computing Research
Repository, vol. abs/1605.07146, [Online], 2017. Available: arXiv:1605.07146.
[40] C.P. Lee,“A study on L2-loss (squared hinge-loss) multiclass SVM,”Neural Computing, vol. 25, no. 5, pp. 1302–1323, 2013.
[41] J. Dean, “Large-scale deep learning for intelligent computer systems,”BayLearn Keynote Speech, 2015.
[42] A. Krizhevsky,“Learning multiple layers of features from tiny images,”Tech Report of Canadian Institute for Advanced Research, Ch. 3, pp. 32-36, 2009.
[43] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: a large-scale hierarchical image database,”in Computer Vision and Pattern Recognition 2009, Miami, Florida, 2009.
[44] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,”in Proceedings of the IEEE, 1998.
[45] A. Krizhevsky, I. Sutskever, and G.E. Hinton, “ImageNet classification with deep convolutional neural networks,”in International Conference on Neural Information Processing Systems, Lake Tahoe, Florida, pp. 1097-1105, 2012.
[46] M.D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,”Computing Research Repository, vol. abs/1311.2901, [Online], 2013. Available: arXiv:1311.2901.
[47] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich,“Going deeper with convolutions,”Computing Research Repository, vol. abs/1409.4842, [Online], 2014. Available: arXiv:1409.4842.
[48] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,”Computing Research Repository, vol. abs/1409.1556, [Online], 2014. Available: arXiv:1409.1556.
[49] K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: surpassing human-level performance on ImageNet classification,”Computing Research Repository, vol. abs/1502.01852, [Online], 2015. Available: arXiv:1502.01852.
[50] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov,
“Dropout: a simple way to prevent neural networks from overfitting,”Journal of
Machine Learning Research, vol. 15, pp. 1929-1958, 2014.
[51] S. Ioffe and C. Szegedy, “Batch normalization: accelerating deep network training by reducing intrnal covariate shift,”Computing Research Repository, vol. abs/1502.03167, [Online], 2015. Available: arXiv:1502.03167.
[52] T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen,
“Improved techniques for training GANs,”Computing Research Repository, vol. abs/1606.03498, [Online], 2015. Available: arXiv:1606.03498.
[53] A. van den Oord, N. Kalchbrenner, and K. Kavukvuoglu, “Pixel recurrent neural networks,”Computing Research Repository, vol. abs/ 1601.06759, [Online], 2016. Available: arXiv:1601.06759.
[54] X. Liang, S. Liu, Y. Wei, L. Liu, L. Lin, and S. Yan, “Computational baby learning,”Computing Research Repository, vol. abs/1411.2861, [Online], 2014. Available: arXiv:1411.2861.
[55] B. Zoph, V. Vasudevan, J. Shlens, Q.V. Le, “Learning transferable architectures for scalable image recognition,”Computing Research Repository, vol. abs/1707.07012, [Online], 2017. Available: arXiv:1707.07012.
[56] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,”Computing Research Repository, vol. abs/1311.2524, [Online], 2013. Available: arXiv:1311.2524.
[57] R. Girshick,“Fast R-CNN,”Computing Research Repository, vol. abs/1504.08083, [Online], 2015. Available: arXiv:1504.08083.
[58] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,”Computing Research Repository, vol. abs/1506.01497, [Online], 2015. Available: arXiv:1506.01497.
[59] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: unified, real-time object detection,”in IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Nevada, 2016.
[60] J. Long, E. Shelhamer and T. Darrell, “Fully convolutional networks for semantic segmentation,”in IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Nevada, 2015.
[61] V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A deep convolutional encoder-decoder architecture for image segmentation,”Computing Research Repository, vol. abs/1511.00561, [Online], 2015. Available: arXiv:1511.00561.
[62] F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” Computing Research Repository, vol. abs/ 1511.07122, [Online], 2015. Available: arXiv:vol. abs/1511.07122.
[63] G. Lin, A. Milan, C. Shen, and I. Reid,“RefineNet: Multi-path refinement networks for high-resolution semantic segmentation,”Computing Research Repository, vol. abs/
1611.06612, [Online], 2016. Available: arXiv:vol. abs/1611.06612.
[64] L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam, “Rethinking atrous convolution for semantic image segmentation,”Computing Research Repository, vol. abs/1706.05587, [Online], 2017. Available: arXiv:vol. abs/1706.05587.
[65] M.I. Razzak, S. Naz, and A. Zaib, “Deep learning for medical image process-
ing: overview, challenges and future,”Computing Research Repository, vol. abs/
1704.06825, [Online], 2017. Available: arXiv:vol. abs/1704.06825.
[66] W. Shen, M. Zhou, F. Yang, C. Yang, and J. Tian,“Multi-scale convolutional neural networks for lung nodule classification,”in International Conference on Information Processing in Medical Imaging, Isle of Skye, Scotland, 2015.
[67] A.A.A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S.J. van Riel, M.M.W. Wille, M. Naqibullah, C.I. Sanchez, and B. van Ginneken,“Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks,”IEEE Transactions on Medical Imaging, vol. 35, [Online], 2016. Available: 10.1109/TMI.2016.2536809.
[68] D. Yang, S. Zhang, Z. Yan, C. Tan, K. Li and D. Metaxas, “Automated anatomical landmark detection ondistal femur surface using convolutional neural network,”in International Symposium on Biomedical Imaging, New York, New York, 2015.
[69] C. Stolojescu-Crisan and S Holban, “A comparison of X-Ray image segmentation techniques,”Advances in Electrical and Computer Engineering, vol. 13, pp. 85-92, 2013. Available: 10.4316/AECE.2013.03014.
[70] A.R. Natsheh, P.V.S. Ponnapalli, N. Anani, D. Benchebra, A. El-kholy, and P. Norburn, “Segmentation of bone structure in sinus CT images using self-organizing maps,”in IEEE International Conference on Imaging Systems and Techniques, Thessaloniki, Greece, 2010.
[71] C. Last, S. Winkelbach, F.M. Wahl, K.W.G. Eichhorn, and F. Bootz,“A model-based approach to the segmentation of nasal cavity and paranasal sinus boundaries,”Pattern Recognition, vol. 6376, pp. 333-342, 2010.
[72] P.F. Christ, M.E.A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W.H. Sommer, S.-A. Ahmadi, and B.H. Menze, “Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields,”Computing Research Repository, vol. abs/1610.02177, [Online], 2016. Available: arXiv:vol. abs/1610.02177.
[73] P.F. Christ, F. Ettlinger, F. Grun, M. Ezzeldin, A. Elshaera, J. Lipkova, S. Schlecht, F. Ahmaddy, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, F. Hofmann, M. D’Anastasi, S.-A. Ahmadi, G. Kaissis, J. Holch, W. Sommer, R. Braren, V. Heinenmann, and B.H. Menze, “Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks,”Computing Research Repository, vol. abs/1702.05970, [Online], 2017. Available: arXiv:vol. abs/1702.05970.
[74] F. Meyer, “Iterative image transformation for an automatic screening of cervical smears,”J. Histochemistry and Cytochemistry, vol. 27, pp. 128-135, 1979.
[75] K. Zuiderveld,“Contrast limited adaptive histogram equalization,”Graphics Gems IV.P.S. Heckbert (Eds.), Cambridge, MA, Academic Press, Ch. 8.5, pp. 474-485, 1994.
[76] A. Norouzi, M.S.M. Rahim, A. Altameem, and M. Uddin,“Medical image segmen-
tation methods, algorithms, and applications,”IETE Technical Review, vol. 31, pp. 199-213, 2014. Available: 10.1080/02564602.2014.906861.
[77] N. Senthilkumaran and S. Vaithegi, “Image segmentation by using thresholding techniques for medical images,”International Journal of Computer Science and Engineering, vol. 6, pp. 1-13, 2016. Available: 10.5121/cseij.2016.6101.
[78] H.P. Ng, S.H. Ong, K.W.C. Foong, P.S. Goh, and W.L. Nowinski, “Medical image segmentation using K-means clustering and improved watershed algorithm,”in IEEE Southwest Symposium on Image Analysis and Interpretation, Denver, Colorado, pp. 61-65, 2006. Available: 10.1109/SSIAI.2006.1633722.
[79] M.C.J. Christ and R.M.S. Parvathi, “Fuzzy c-means algorithm for medical image segmentation,”in 3rd International Conference on Electronics Computer Technology, Kanyakumari, pp. 33-36, 2011. Available: 10.1109/ICECTECH.2011.5941851.
[80] J. Hill, E. Corona, J. Ao, S. Mitra, and B. Nutter, “Information theoretic clustering for image segmentation,”Advanced Computational Approaches to Biomedical Engineering, vol. -, pp. 47-70, 2014. Available: 10.1007/978-3-642-41539-5.
[81] Q. Min and R.J.T. Sadleir, “A segmentation based lossless compression scheme for volumetric medical image data,”in Irish Machine Vision and Image Processing Conference, Dublin, pp. 101-102, 2011. Available: 10.1109/IMVIP.2011.26.
[82] T.K. Ganga and V. Karthikeyani, “Medical image segmentation using multi resolution histogram,”in 3rd International Conference on Electronics Computer Technology, Kanyakumari, pp. 267-269, 2011. Available: 10.1109/ICECTECH.2011.5942095.
[83] Q. Wei and R.L. Dunbrack, “The role of balanced training and testing data sets for binary classifiers in bioinformatics,”PLoS ONE, vol. 8, no. 7, pp. e67863, 2013. Available: 10.1371/journal.pone.0067863
[84] L. Perez and J. Wang,“The effectiveness of data augmentation in image classification using deep learning,”Computing Research Repository, vol. abs/1712.04621, [Online], 2017. Available: arXiv:vol. abs/1712.04621.
[85] G.E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R.R. Salkhutdi-
nov, “Improving neural networks by preventing co-adaptation of feature detec-
tors,”Computing Research Repository, vol. abs/1207.0580, [Online], 2012. Available: arXiv:vol. abs/1207.0580.
[86] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell,“Caffe: Convolution architecture for fast feature embedding,”Computing Research Repository, vol. abs/1408.5093, [Online], 2014. Available: arXiv:vol. abs/1408.5093.
[87] Anonymous, “Visualizing what ConvNets learn,”Stanford University, CS231n
Convolution Neural Networks for Visual Recognition, [Online], 2018. Available:
cs231n.github.io/understanding-cnn/.
[88] Alper, “Alp’s image segmentation tool,”AIp’s Labeling Tools for Deep Learning, [Online], 2017. Available: https:// alpslabel.wordpress.com/ 2017/03/28/ alps-image-segmentation-tool-aims/
[89] J. Schindelin, I. Arganda-Carreras, and E. Frise, “Fiji: An open-source platform for biological-image analysis,”Nature Methods, vol. 9, no. 7, pp. 676-682, 2012. Available: 10.1038/nmeth.2019.
[90] M.J. Lado, P.G. Tahoces, A.J. Mendez, M. Souto, and J.J. Vidal, “A wavelet-based algorithm for detection clustered microcalcifications in digital mammograms,”Medical Physics, vol. 26, no. 7, pp. 1294-1305, 1999.
[91] S. Singh and K. Bovis, “An evaluation of contrast enhancement techniques for
mammographic breast masses,”IEEE Transactions on Information Technololgy and
Biomedics, vol. 9, no. 1, pp. 109-119, 2005.
[92] W.M. Morrow, R.B. Paranjape, R.M. Rangayyan, and J.E.L. Desautels, “Region-
based contrast enhancement of mammograms,”IEEE Transactions on Medical Imag-
ing, vol. 11, no. 3, pp. 392-406, 1992.
[93] P. Chondro, C.-Y. Yao, L.-C. Chien, and S.-J. Ruan, “Low order adaptive region growing for lung segmentation on plain chest radiographs,”Neurocomputing, vol. 275, no. -, pp. 1002-1011, 2018.
[94] P. Radau, Y. Lu, K. Connelly, G. Paul, A.J. Dick, and G.A. Wright, “Evaluation framework for algorithms segmenting short axis cardiac MRI,”MIDAS Journal, [Online], 2009. Available: https://www.midasjournal.org/browse/publication/658.
[95] M.A. Abd-Alla and A.J. Mahdi, “Maxillary sinus measurements in different age groups of human cadavers,”Tikrit Journal for Dental Sciences, vol. 1, no. -, pp. 107-112, 2013.
[96] S.K. Sharma, M. Jehan, and A. Kumar, “Measurements of maxillary sinus volume and dimensions by computed tomography scan for gender determination,”Journal of Anatomical Society of India, vol. 63, no. -, pp. 36-42, 2014.