Mathematical Morphology for Color Images: An Image-Dependent Approach

This paper proposes one possibility to generalize the morphological operations (particularly, dilation, erosion, opening, and closing) to color images. First, properties of a desirable generalization are stated and a brief review is done on former approaches. Then, the method is explained, which is based on a total ordering of the colors in an image induced by its color histogram; this is valid for just one image and may present problems in smoothly coloured images. To solve these drawbacks a refinement consisting of smoothing the histogram and using a joint histogram of several images is presented. Results of applying the so-defined morphological operations on several sets of images are shown and discussed

Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression

[EN] Purpose: The development of automatic and reliable algorithms for the detection and segmentation of the vertebrae are of great importance prior to any diagnostic task. However, an important problem found to accurately segment the vertebrae is the presence of the ribs in the thoracic region. To overcome this problem, a probabilistic atlas of the spine has been developed dealing with the proximity of other structures, with a special focus on ribs suppression. Methods: The data sets used consist of Computed Tomography images corresponding to 21 patients suffering from spinal metastases. Two methods have been combined to obtain the final result: firstly, an initial segmentation is performed using a fully automatic level-set method; secondly, to refine the initial segmentation, a 3D volume indicating the probability of each voxel of belonging to the spine has been developed. In this way, a probability map is generated and deformed to be adapted to each testing case. Results: To validate the improvement obtained after applying the atlas, the Dice coefficient (DSC), the Hausdorff distance (HD), and the mean surface-to-surface distance (MSD) were used. The results showed up an average of 10 mm of improvement accuracy in terms of HD, obtaining an overall final average of 15.51 2.74 mm. Also, a global value of 91.01 3.18% in terms of DSC and a MSD of 0.66 0.25 mm were obtained. The major improvement using the atlas was achieved in the thoracic region, as ribs were almost perfectly suppressed. Conclusion: The study demonstrated that the atlas is able to detect and appropriately eliminate the ribs while improving the segmentation accuracy.The authors thank the financial support of the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under Grants TEC2012-33778 and BFU2015-64380-C2-2-R (D.M.) and DPI2013-4572-R (J.D., E.D.)Ruiz-España, S.; Domingo, J.; Díaz-Parra, A.; Dura, E.; D'ocon-Alcaniz, V.; Arana, E.; Moratal, D. (2017). Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression. Medical Physics. 44(9):4695-4707. https://doi.org/10.1002/mp.12431S46954707449Harris, R. I., & Macnab, I. (1954). STRUCTURAL CHANGES IN THE LUMBAR INTERVERTEBRAL DISCS. The Journal of Bone and Joint Surgery. British volume, 36-B(2), 304-322. doi:10.1302/0301-620x.36b2.304Oliveira, M. F. de, Rotta, J. M., & Botelho, R. V. (2015). Survival analysis in patients with metastatic spinal disease: the influence of surgery, histology, clinical and neurologic status. Arquivos de Neuro-Psiquiatria, 73(4), 330-335. doi:10.1590/0004-282x20150003Chou, R. (2011). Diagnostic Imaging for Low Back Pain: Advice for High-Value Health Care From the American College of Physicians. Annals of Internal Medicine, 154(3), 181. doi:10.7326/0003-4819-154-3-201102010-00008Brayda-Bruno, M., Tibiletti, M., Ito, K., Fairbank, J., Galbusera, F., Zerbi, A., … Sivan, S. S. (2013). Advances in the diagnosis of degenerated lumbar discs and their possible clinical application. European Spine Journal, 23(S3), 315-323. doi:10.1007/s00586-013-2960-9Quattrocchi, C. C., Santini, D., Dell’Aia, P., Piciucchi, S., Leoncini, E., Vincenzi, B., … Zobel, B. B. (2007). A prospective analysis of CT density measurements of bone metastases after treatment with zoledronic acid. Skeletal Radiology, 36(12), 1121-1127. doi:10.1007/s00256-007-0388-1Doi, K. (2007). Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Computerized Medical Imaging and Graphics, 31(4-5), 198-211. doi:10.1016/j.compmedimag.2007.02.002Ruiz-España, S., Arana, E., & Moratal, D. (2015). Semiautomatic computer-aided classification of degenerative lumbar spine disease in magnetic resonance imaging. Computers in Biology and Medicine, 62, 196-205. doi:10.1016/j.compbiomed.2015.04.028Alomari, R. S., Ghosh, S., Koh, J., & Chaudhary, V. (2014). Vertebral Column Localization, Labeling, and Segmentation. Lecture Notes in Computational Vision and Biomechanics, 193-229. doi:10.1007/978-3-319-12508-4_7Hamarneh, G., & Li, X. (2009). Watershed segmentation using prior shape and appearance knowledge. Image and Vision Computing, 27(1-2), 59-68. doi:10.1016/j.imavis.2006.10.009Ghebreab, S., & Smeulders, A. W. (2004). Combining Strings and Necklaces for Interactive Three-Dimensional Segmentation of Spinal Images Using an Integral Deformable Spine Model. IEEE Transactions on Biomedical Engineering, 51(10), 1821-1829. doi:10.1109/tbme.2004.831540Mastmeyer, A., Engelke, K., Fuchs, C., & Kalender, W. A. (2006). A hierarchical 3D segmentation method and the definition of vertebral body coordinate systems for QCT of the lumbar spine. Medical Image Analysis, 10(4), 560-577. doi:10.1016/j.media.2006.05.005Rasoulian, A., Rohling, R., & Abolmaesumi, P. (2013). Lumbar Spine Segmentation Using a Statistical Multi-Vertebrae Anatomical Shape+Pose Model. IEEE Transactions on Medical Imaging, 32(10), 1890-1900. doi:10.1109/tmi.2013.2268424Ma, J., & Lu, L. (2013). Hierarchical segmentation and identification of thoracic vertebra using learning-based edge detection and coarse-to-fine deformable model. Computer Vision and Image Understanding, 117(9), 1072-1083. doi:10.1016/j.cviu.2012.11.016Kim, Y., & Kim, D. (2009). A fully automatic vertebra segmentation method using 3D deformable fences. Computerized Medical Imaging and Graphics, 33(5), 343-352. doi:10.1016/j.compmedimag.2009.02.006Klinder, T., Ostermann, J., Ehm, M., Franz, A., Kneser, R., & Lorenz, C. (2009). Automated model-based vertebra detection, identification, and segmentation in CT images. Medical Image Analysis, 13(3), 471-482. doi:10.1016/j.media.2009.02.004Štern, D., Likar, B., Pernuš, F., & Vrtovec, T. (2011). Parametric modelling and segmentation of vertebral bodies in 3D CT and MR spine images. Physics in Medicine and Biology, 56(23), 7505-7522. doi:10.1088/0031-9155/56/23/011Korez, R., Ibragimov, B., Likar, B., Pernus, F., & Vrtovec, T. (2015). A Framework for Automated Spine and Vertebrae Interpolation-Based Detection and Model-Based Segmentation. IEEE Transactions on Medical Imaging, 34(8), 1649-1662. doi:10.1109/tmi.2015.2389334Castro-Mateos, I., Pozo, J. M., Pereanez, M., Lekadir, K., Lazary, A., & Frangi, A. F. (2015). Statistical Interspace Models (SIMs): Application to Robust 3D Spine Segmentation. IEEE Transactions on Medical Imaging, 34(8), 1663-1675. doi:10.1109/tmi.2015.2443912Pereanez, M., Lekadir, K., Castro-Mateos, I., Pozo, J. M., Lazary, A., & Frangi, A. F. (2015). Accurate Segmentation of Vertebral Bodies and Processes Using Statistical Shape Decomposition and Conditional Models. IEEE Transactions on Medical Imaging, 34(8), 1627-1639. doi:10.1109/tmi.2015.2396774Michael Kelm, B., Wels, M., Kevin Zhou, S., Seifert, S., Suehling, M., Zheng, Y., & Comaniciu, D. (2013). Spine detection in CT and MR using iterated marginal space learning. Medical Image Analysis, 17(8), 1283-1292. doi:10.1016/j.media.2012.09.007Yan Kang, Engelke, K., & Kalender, W. A. (2003). A new accurate and precise 3-D segmentation method for skeletal structures in volumetric CT data. IEEE Transactions on Medical Imaging, 22(5), 586-598. doi:10.1109/tmi.2003.812265Huang, J., Jian, F., Wu, H., & Li, H. (2013). An improved level set method for vertebra CT image segmentation. BioMedical Engineering OnLine, 12(1), 48. doi:10.1186/1475-925x-12-48Lim, P. H., Bagci, U., & Bai, L. (2013). Introducing Willmore Flow Into Level Set Segmentation of Spinal Vertebrae. IEEE Transactions on Biomedical Engineering, 60(1), 115-122. doi:10.1109/tbme.2012.2225833Forsberg, D., Lundström, C., Andersson, M., & Knutsson, H. (2013). Model-based registration for assessment of spinal deformities in idiopathic scoliosis. Physics in Medicine and Biology, 59(2), 311-326. doi:10.1088/0031-9155/59/2/311Yao, J., Burns, J. E., Forsberg, D., Seitel, A., Rasoulian, A., Abolmaesumi, P., … Li, S. (2016). A multi-center milestone study of clinical vertebral CT segmentation. Computerized Medical Imaging and Graphics, 49, 16-28. doi:10.1016/j.compmedimag.2015.12.006Shi, C., Wang, J., & Cheng, Y. (2015). Sparse Representation-Based Deformation Model for Atlas-Based Segmentation of Liver CT Images. Image and Graphics, 410-419. doi:10.1007/978-3-319-21969-1_36Domingo, J., Dura, E., Ayala, G., & Ruiz-España, S. (2015). Means of 2D and 3D Shapes and Their Application in Anatomical Atlas Building. Lecture Notes in Computer Science, 522-533. doi:10.1007/978-3-319-23192-1_44Hyunjin Park, Bland, P. H., & Meyer, C. R. (2003). Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Transactions on Medical Imaging, 22(4), 483-492. doi:10.1109/tmi.2003.809139Cabezas, M., Oliver, A., Lladó, X., Freixenet, J., & Bach Cuadra, M. (2011). A review of atlas-based segmentation for magnetic resonance brain images. Computer Methods and Programs in Biomedicine, 104(3), e158-e177. doi:10.1016/j.cmpb.2011.07.015Fortunati, V., Verhaart, R. F., van der Lijn, F., Niessen, W. J., Veenland, J. F., Paulides, M. M., & van Walsum, T. (2013). Tissue segmentation of head and neck CT images for treatment planning: A multiatlas approach combined with intensity modeling. Medical Physics, 40(7), 071905. doi:10.1118/1.4810971Zhuang, X., Bai, W., Song, J., Zhan, S., Qian, X., Shi, W., … Rueckert, D. (2015). Multiatlas whole heart segmentation of CT data using conditional entropy for atlas ranking and selection. Medical Physics, 42(7), 3822-3833. doi:10.1118/1.4921366Zhou, J., Yan, Z., Lasio, G., Huang, J., Zhang, B., Sharma, N., … D’Souza, W. (2015). Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape composition prior in CT. Computerized Medical Imaging and Graphics, 46, 47-55. doi:10.1016/j.compmedimag.2015.07.003Linguraru, M. G., Sandberg, J. K., Li, Z., Shah, F., & Summers, R. M. (2010). Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation. Medical Physics, 37(2), 771-783. doi:10.1118/1.3284530Xu, Y., Xu, C., Kuang, X., Wang, H., Chang, E. I.-C., Huang, W., & Fan, Y. (2016). 3D-SIFT-Flow for atlas-based CT liver image segmentation. Medical Physics, 43(5), 2229-2241. doi:10.1118/1.4945021Michopoulou, S. K., Costaridou, L., Panagiotopoulos, E., Speller, R., Panayiotakis, G., & Todd-Pokropek, A. (2009). Atlas-Based Segmentation of Degenerated Lumbar Intervertebral Discs From MR Images of the Spine. IEEE Transactions on Biomedical Engineering, 56(9), 2225-2231. doi:10.1109/tbme.2009.2019765Taso, M., Le Troter, A., Sdika, M., Ranjeva, J.-P., Guye, M., Bernard, M., & Callot, V. (2013). Construction of an in vivo human spinal cord atlas based on high-resolution MR images at cervical and thoracic levels: preliminary results. Magnetic Resonance Materials in Physics, Biology and Medicine, 27(3), 257-267. doi:10.1007/s10334-013-0403-6Lévy, S., Benhamou, M., Naaman, C., Rainville, P., Callot, V., & Cohen-Adad, J. (2015). White matter atlas of the human spinal cord with estimation of partial volume effect. NeuroImage, 119, 262-271. doi:10.1016/j.neuroimage.2015.06.040Hardisty, M., Gordon, L., Agarwal, P., Skrinskas, T., & Whyne, C. (2007). Quantitative characterization of metastatic disease in the spine. Part I. Semiautomated segmentation using atlas-based deformable registration and the level set method. Medical Physics, 34(8), 3127-3134. doi:10.1118/1.2746498Forsberg, D. (2015). Atlas-Based Registration for Accurate Segmentation of Thoracic and Lumbar Vertebrae in CT Data. Lecture Notes in Computational Vision and Biomechanics, 49-59. doi:10.1007/978-3-319-14148-0_5Ibañez MV Schroeder W Cates L Insight software Consortium. The ITK Software Guide 2016 http://www.itk.org/ItkSoftwareGuide.pdfLoader C R package: Local regression, likelihood and density estimation. CRAN repository 2013 2016 http://cran.r-project.org/web/packages/locfitPARK, H., HERO, A., BLAND, P., KESSLER, M., SEO, J., & MEYER, C. (2010). Construction of Abdominal Probabilistic Atlases and Their Value in Segmentation of Normal Organs in Abdominal CT Scans. IEICE Transactions on Information and Systems, E93-D(8), 2291-2301. doi:10.1587/transinf.e93.d.2291Pohl, K. M., Fisher, J., Bouix, S., Shenton, M., McCarley, R. W., Grimson, W. E. L., … Wells, W. M. (2007). Using the logarithm of odds to define a vector space on probabilistic atlases. Medical Image Analysis, 11(5), 465-477. doi:10.1016/j.media.2007.06.003Baddeley, A., & Molchanov, I. (1998). Journal of Mathematical Imaging and Vision, 8(1), 79-92. doi:10.1023/a:1008214317492De Bruijne, M., van Ginneken, B., Viergever, M. A., & Niessen, W. J. (2003). Adapting Active Shape Models for 3D Segmentation of Tubular Structures in Medical Images. Information Processing in Medical Imaging, 136-147. doi:10.1007/978-3-540-45087-0_12Zhang, K., Zhang, L., Song, H., & Zhou, W. (2010). Active contours with selective local or global segmentation: A new formulation and level set method. Image and Vision Computing, 28(4), 668-676. doi:10.1016/j.imavis.2009.10.009Kalpathy-Cramer, J., Awan, M., Bedrick, S., Rasch, C. R. N., Rosenthal, D. I., & Fuller, C. D. (2013). Development of a Software for Quantitative Evaluation Radiotherapy Target and Organ-at-Risk Segmentation Comparison. Journal of Digital Imaging, 27(1), 108-119. doi:10.1007/s10278-013-9633-4Huttenlocher, D. P., Klanderman, G. A., & Rucklidge, W. J. (1993). Comparing images using the Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(9), 850-863. doi:10.1109/34.232073Aspert, N., Santa-Cruz, D., & Ebrahimi, T. (s. f.). MESH: measuring errors between surfaces using the Hausdorff distance. Proceedings. IEEE International Conference on Multimedia and Expo. doi:10.1109/icme.2002.103587

The Future We Want: a Learning Experience to Promote SDGs in Higher Education from the United Nations and University of Valencia

This article shares the strategy for mainstreaming the Sustainable Development Goals (SDGs) at the University of Valencia (UV), which, although limited in its scale, may compel other Higher Education Institutions to think in technological and social progress aligned with the 2030 Agenda. It explicates a process driven by the UV, on the occasion of the 75th anniversary of the United Nations (UN), and in collaboration with the Service for Geospatial, Information, and Telecommunications Technologies from the UN Support Base in Valencia (Spain) to prepare the online event: ¿The United Nations We Want¿. It was the culmination of a collaborative project between students and faculties from different scientific, technological, social, legal, humanistic, and health disciplines that structure the University of Valencia. The intention was that new generations experience the role they can have to shape the future we want, while the university community as a whole can become part of transformative institutional change that draws on both top-down and bottom-up strategies in pursuit of Education for Sustainable Development

Association of Candidate Gene Polymorphisms With Chronic Kidney Disease: Results of a Case-Control Analysis in the Nefrona Cohort

Chronic kidney disease (CKD) is a major risk factor for end-stage renal disease, cardiovascular disease and premature death. Despite classical clinical risk factors for CKD and some genetic risk factors have been identified, the residual risk observed in prediction models is still high. Therefore, new risk factors need to be identified in order to better predict the risk of CKD in the population. Here, we analyzed the genetic association of 79 SNPs of proteins associated with mineral metabolism disturbances with CKD in a cohort that includes 2, 445 CKD cases and 559 controls. Genotyping was performed with matrix assisted laser desorption ionizationtime of flight mass spectrometry. We used logistic regression models considering different genetic inheritance models to assess the association of the SNPs with the prevalence of CKD, adjusting for known risk factors. Eight SNPs (rs1126616, rs35068180, rs2238135, rs1800247, rs385564, rs4236, rs2248359, and rs1564858) were associated with CKD even after adjusting by sex, age and race. A model containing five of these SNPs (rs1126616, rs35068180, rs1800247, rs4236, and rs2248359), diabetes and hypertension showed better performance than models considering only clinical risk factors, significantly increasing the area under the curve of the model without polymorphisms. Furthermore, one of the SNPs (the rs2248359) showed an interaction with hypertension, being the risk genotype affecting only hypertensive patients. We conclude that 5 SNPs related to proteins implicated in mineral metabolism disturbances (Osteopontin, osteocalcin, matrix gla protein, matrix metalloprotease 3 and 24 hydroxylase) are associated to an increased risk of suffering CKD

Scellpam: an R package/C++ library to perform parallel partitioning around medoids on scRNAseq data sets

Abstract Background Partitioning around medoids (PAM) is one of the most widely used and successful clustering method in many fields. One of its key advantages is that it only requires a distance or a dissimilarity between the individuals, and the fact that cluster centers are actual points in the data set means they can be taken as reliable representatives of their classes. However, its wider application is hampered by the large amount of memory needed to store the distance matrix (quadratic on the number of individuals) and also by the high computational cost of computing such distance matrix and, less importantly, by the cost of the clustering algorithm itself. Results Therefore, new software has been provided that addresses these issues. This software, provided under GPL license and usable as either an R package or a C++ library, calculates in parallel the distance matrix for different distances/dissimilarities ( $$L_1$$ L 1 , $$L_2$$ L 2 , Pearson, cosine and weighted Euclidean) and also implements a parallel fast version of PAM (FASTPAM1) using any data type to reduce memory usage. Moreover, the parallel implementation uses all the cores available in modern computers which greatly reduces the execution time. Besides its general application, the software is especially useful for processing data of single cell experiments. It has been tested in problems including clustering of single cell experiments with up to 289,000 cells with the expression of about 29,000 genes per cell. Conclusions Comparisons with other current packages in terms of execution time have been made. The method greatly outperforms the available R packages for distance matrix calculation and also improves the packages that implement the PAM itself. The software is available as an R package at https://CRAN.R-project.org/package=scellpam and as C++ libraries at https://github.com/JdMDE/jmatlib and https://github.com/JdMDE/ppamlib The package is useful for single cell RNA-seq studies but it is also applicable in other contexts where clustering of large data sets is required