Contribución a la detección y análisis de microcalcificaciones en mamografías mediante tratamiento digital de imagen

La presente Tesis trata de la detección de microcalcificaciones en mamografía. De entre los diferentes hallazgos que se pueden encontrar en mamografía las microcalcificaciones son uno de los importantes porque permiten la detección precoz, ya que casi la mitad de los tumores clínicamente ocultos son detectados gracias a la presencia de las microcalcificaciones. Las microcalcificaciones aisladas apenas son significativas desde un punto de vista radiológico y sí lo son cuando están formando grupos. Para su detección se utiliza tratamiento digital de imagen, concretamente la combinación de la morfología matemática y los campos aleatorios de Markov. Se dedica un capítulo a presentar cada una de estas dos áreas. En el capítulo siguiente dedicado al algoritmo se concreta el modelo de campo aleatorio de Markov al caso de interacción entre pares de pixels dentro de un vecindario de orden 2. También se incluye en el modelo la contribución debida a pixels individualmente. Para aprovechar el hecho de que las microcalcificaciones es probable que aparezcan agrupadas se amplía el orden del vecindario a una región de radio 0.5 cm para aquellos pixels etiquetados como microcalcificación. En la parte que respecta a morfología matemática se propone utilizar operadores que destacan las microcalcificaciones sin destacar otras regiones contrastadas como pueden ser las regiones correspondientes a las fibras. Para minimizar la excesiva sensibilidad a regiones contrastadas muy pequeñas se proponen transformaciones morfológicas con operadores conexos como la apertura superficial. Para el capítulo de resultados se han utilizado las mamografías de la base de datos suministrada por cortesía del National Expert and Training Centre for Breast Cancer Screening and the Departament of Radiology at teh University of Nijmegen, the NetherlandsMossi García, JM. (1998). Contribución a la detección y análisis de microcalcificaciones en mamografías mediante tratamiento digital de imagen [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/5643Palanci

Evaluation of fractal dimension effectiveness for damage detection in retinal background

[EN] This work investigates the characterization of bright lesions in retinal fundus images using texture analysis techniques. Exudates and drusen are evidences of retinal damage in diabetic retinopathy (DR) and age-related macular degeneration (AMD) respectively. An automatic detection of pathological tissues could make possible an early detection of these diseases. In this work, fractal analysis is explored in order to discriminate between pathological and healthy retinal texture. After a deep preprocessing step, in which spatial and colour normalization are performed, the fractal dimension is extracted locally by computing the Hurst exponent (H) along different directions. The greyscale image is described by the increments of the fractional Brownian motion model and the H parameter is computed by linear regression in the frequency domain. The ability of fractal dimension to detect pathological tissues is demonstrated using a home-made system, based on fractal analysis and Support Vector Machine, able to achieve around a 70% and 83% of accuracy in E-OPHTHA and DIARETDB1 public databases respectively. In a second experiment, the fractal descriptor is combined with texture information, extracted by the Local Binary Patterns, improving the bright lesion detection. Accuracy, sensitivity and specificity values higher than 89%, 80% and 90% respectively suggest that the method presented in this paper is a robust algorithm for describing retina texture and can be useful in the automatic detection of DR and AMD.This paper was supported by the European Union's Horizon 2020 research and innovation programme under the Project GALAHAD [H2020-ICT-2016-2017, 732613]. In addition, this work was partially funded by the Ministerio de Economia y Competitividad of Spain, Project SICAP [DPI2016-77869-C2-1-R]. The work of Adrian Colomer has been supported by the Spanish Government under a FPI Grant [BES-2014-067889]. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.Colomer, A.; Naranjo Ornedo, V.; Janvier, T.; Mossi García, JM. (2018). Evaluation of fractal dimension effectiveness for damage detection in retinal background. Journal of Computational and Applied Mathematics. 337:341-353. https://doi.org/10.1016/j.cam.2018.01.005S34135333

Using latent features for short-term person re-identification with RGB-D cameras

This paper presents a system for people re-identification in uncontrolled scenarios using RGB-depth cameras. Compared to conventional RGB cameras, the use of depth information greatly simplifies the tasks of segmentation and tracking. In a previous work, we proposed a similar architecture where people were characterized using color-based descriptors that we named bodyprints. In this work, we propose the use of latent feature models to extract more relevant information from the bodyprint descriptors by reducing their dimensionality. Latent features can also cope with missing data in case of occlusions. Different probabilistic latent feature models, such as probabilistic principal component analysis and factor analysis, are compared in the paper. The main difference between the models is how the observation noise is handled in each case. Re-identification experiments have been conducted in a real store where people behaved naturally. The results show that the use of the latent features significantly improves the re-identification rates compared to state-of-the-art works.The work presented in this paper has been funded by the Spanish Ministry of Science and Technology under the CICYT contract TEVISMART, TEC2009-09146.Oliver Moll, J.; Albiol Colomer, A.; Albiol Colomer, AJ.; Mossi García, JM. (2016). Using latent features for short-term person re-identification with RGB-D cameras. Pattern Analysis and Applications. 19(2):549-561. https://doi.org/10.1007/s10044-015-0489-8S549561192http://kinectforwindows.org/http://www.gpiv.upv.es/videoresearch/personindexing.htmlAlbiol A, Albiol A, Oliver J, Mossi JM (2012) Who is who at different cameras. Matching people using depth cameras. Comput Vis IET 6(5):378–387Bak S, Corvee E, Bremond F, Thonnat M (2010) Person re-identification using haar-based and dcd-based signature. 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Int J Math Models Methods Appl Sci 1(4):300–307Cheng YM, Zhou WT, Wang Y, Zhao CH, Zhang SW (2009) Multi-camera-based object handoff using decision-level fusion. In: Conference on image and signal processing. pp. 1–5Dikmen M, Akbas E, Huang TS, Ahuja N (2010) Pedestrian recognition with a learned metric. In: Asian conference in computer visionDoretto G, Sebastian T, Tu P, Rittscher J (2011) Appearance-based person reidentification in camera networks: problem overview and current approaches. J Ambient Intell Humaniz Comput 2:1–25Farenzena M, Bazzani L, Perina A, Murino V, Cristani M (2010) Person re-identification by symmetry-driven accumulation of local features. In: Proceedings of the 2010 IEEE computer society conference on computer vision and pattern recognition (CVPR 2010). IEEE Computer Society, San Francisco, CA, USAFodor I (2002) A survey of dimension reduction techniques. Technical report. Lawrence Livermore National LaboratoryFreund Y, Iyer R, Schapire RE, Singer Y (2003) An efficient boosting algorithm for combining preferences. J Mach Learn Res 4:933–969Gandhi T, Trivedi M (2006) Panoramic appearance map (pam) for multi-camera based person re-identification. Advanced Video and Signal Based Surveillance, IEEE Conference on, p. 78Garcia J, Gardel A, Bravo I, Lazaro J (2014) Multiple view oriented matching algorithm for people reidentification. Ind Inform IEEE Trans 10(3):1841–1851Gheissari N, Sebastian TB, Hartley R (2006) Person reidentification using spatiotemporal appearance. CVPR 2:1528–1535Gray D, Brennan S, Tao H (2007) Evaluating appearance models for recognition, reacquisition, and tracking. In: Proceedings of IEEE international workshop on performance evaluation for tracking and surveillance (PETS)Gray D, Tao H (2008) Viewpoint invariant pedestrian recognition with an ensemble of localized features. 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Detection of Parked Vehicles using Spatio-temporal Maps

This paper presents a video-based approach to detect the presence of parked vehicles in street lanes. Potential applications include the detection of illegally and double-parked vehicles in urban scenarios and incident detection on roads. The technique extracts information from low-level feature points (Harris corners) to create spatiotemporal maps that describe what is happening in the scene. The method neither relies on background subtraction nor performs any form of object tracking. The system has been evaluated using private and public data sets and has proven to be robust against common difficulties found in closed-circuit television video, such as varying illumination, camera vibration, the presence of momentary occlusion by other vehicles, and high noise levels. © 2011 IEEE.This work was supported by the Spanish Government project Movilidad y automocion en Redes de Transporte Avanzadas (MARTA) under the Consorcios Estrategicos Nacionales de Investigacion Tecnologica (CENIT) program and the Comision Interministerial Ciencia Y Tecnologia (CICYT) under Contract TEC2009-09146. The Associate Editor for this paper was R. W. Goudy.Albiol Colomer, AJ.; Sanchis Pastor, L.; Albiol Colomer, A.; Mossi García, JM. (2011). Detection of Parked Vehicles using Spatio-temporal Maps. IEEE Transactions on Intelligent Transportation Systems. 12(4):1277-1291. https://doi.org/10.1109/TITS.2011.2156791S1277129112

Estimating Point of Regard with a Consumer Camera at a Distance

In this work, we have studied the viability of a novel technique to estimate the POR that only requires video feed from a consumer camera. The system can work under uncontrolled light conditions and does not require any complex hardware setup. To that end we propose a system that uses PCA feature extraction from the eyes region followed by non-linear regression. We evaluated three state of the art non-linear regression algorithms. In the study, we also compared the performance using a high quality webcam versus a Kinect sensor. We found, that despite the relatively low quality of the Kinect images it achieves similar performance compared to the high quality camera. These results show that the proposed approach could be extended to estimate POR in a completely non-intrusive way.Mansanet Sandin, J.; Albiol Colomer, A.; Paredes Palacios, R.; Mossi García, JM.; Albiol Colomer, AJ. (2013). Estimating Point of Regard with a Consumer Camera at a Distance. En Pattern Recognition and Image Analysis. Springer Verlag. 7887:881-888. doi:10.1007/978-3-642-38628-2_104S8818887887Baluja, S., Pomerleau, D.: Non-intrusive gaze tracking using artificial neural networks. Technical report (1994)Breiman, L.: Random forests. Machine Learning (2001)Logitech HD Webcam C525, http://www.logitech.com/es-es/webcam-communications/webcams/hd-webcam-c525Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM TIST (2011), Software, http://www.csie.ntu.edu.tw/~cjlin/libsvmDrucker, H., Burges, C., Kaufman, L., Smola, A., Vapnik, V.: Support vector regression machines (1996)Hansen, D.W., Ji, Q. In: the eye of the beholder: A survey of models for eyes and gaze. IEEE Transactions on PAMI (2010)Ji, Q., Yang, X.: Real-time eye, gaze, and face pose tracking for monitoring driver vigilance. Real-Time Imaging (2002)Kalman, R.E.: A new approach to linear filtering and prediction problems. Transactions of the ASME–Journal of Basic Engineering (1960)Microsoft Kinect, http://www.microsoft.com/en-us/kinectforwindowsTimmerman, M.E.: Principal component analysis (2nd ed.). i. t. jolliffe. Journal of the American Statistical Association (2003)Morimoto, C.H., Mimica, M.R.M.: Eye gaze tracking techniques for interactive applications. Comput. Vis. Image Underst. (2005)Pirri, F., Pizzoli, M., Rudi, A.: A general method for the point of regard estimation in 3d space. In: Proceedings of the IEEE Conference on CVPR (2011)Reale, M.J., Canavan, S., Yin, L., Hu, K., Hung, T.: A multi-gesture interaction system using a 3-d iris disk model for gaze estimation and an active appearance model for 3-d hand pointing. IEEE Transactions on Multimedia (2011)Saragih, J.M., Lucey, S., Cohn, J.F.: Face alignment through subspace constrained mean-shifts. In: International Conference of Computer Vision, ICCV (2009)Kar-Han, T., Kriegman, D.J., Ahuja, N.: Appearance-based eye gaze estimation. In: Applications of Computer Vision (2002)Takemura, K., Kohashi, Y., Suenaga, T., Takamatsu, J., Ogasawara, T.: Estimating 3d point-of-regard and visualizing gaze trajectories under natural head movements. In: Symposium on Eye-Tracking Research and Applications (2010)Villanueva, A., Cabeza, R., Porta, S.: Eye tracking: Pupil orientation geometrical modeling. Image and Vision Computing (2006)Williams, O., Blake, A., Cipolla, R.: Sparse and semi-supervised visual mapping with the s3gp. In: IEEE Computer Society Conference on CVPR (2006

Retinal layer segmentation in rodent OCT images: Local intensity profiles & fully convolutional neural networks

[EN] Background and Objective: Optical coherence tomography (OCT) is a useful technique to monitor retinal layer state both in humans and animal models. Automated OCT analysis in rats is of great relevance to study possible toxic effect of drugs and other treatments before human trials. In this paper, two different approaches to detect the most significant retinal layers in a rat OCT image are presented. Methods: One approach is based on a combination of local horizontal intensity profiles along with a new proposed variant of watershed transformation and the other is built upon an encoder-decoder convolutional network architecture. Results: After a wide validation, an averaged absolute distance error of 3.77 +/- 2.59 and 1.90 +/- 0.91 mu m is achieved by both approaches, respectively, on a batch of the rat OCT database. After a second test of the deep-learning-based method using an unseen batch of the database, an averaged absolute distance error of 2.67 +/- 1.25 mu m is obtained. The rat OCT database used in this paper is made publicly available to facilitate further comparisons. Conclusions: Based on the obtained results, it was demonstrated the competitiveness of the first approach since outperforms the commercial Insight image segmentation software (Phoenix Research Labs) as well as its utility to generate labelled images for validation purposes speeding significantly up the ground truth generation process. Regarding the second approach, the deep-learning-based method improves the results achieved by the more conventional method and also by other state-of-the-art techniques. In addition, it was verified that the results of the proposed network can be generalized to new rat OCT images.Animal experiment permission was granted by the Danish Animal Experimentation Council (license number: 2017-15-020101213). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research. This work has received funding from Horizon 2020, the European Union's Framework Programme for Research and Innovation, under grant agreement No. 732613 (GALAHAD Project), the Spanish Ministry of Economy and Competitiveness through project DPI2016-77869 and GVA through project PROMETEO/2019/109.Morales, S.; Colomer, A.; Mossi García, JM.; Del Amor, R.; Woldbye, D.; Klemp, K.; Larsen, M.... (2021). Retinal layer segmentation in rodent OCT images: Local intensity profiles & fully convolutional neural networks. Computer Methods and Programs in Biomedicine. 198:1-14. https://doi.org/10.1016/j.cmpb.2020.105788S11419