Segmentation of endothelial cells of the cornea from the distance map of confocal microscope images

We propose a novel algorithm for segmenting cells of the cornea endothelium layer on confocal microscope images. To get an inter-cellular space with minimum gray-scale value and to enhance cell borders, we apply a difference of Gaussian filter before image binarization by thresholding with the minimum gray-scale value. Removal of segmented noise and artifacts is performed by automatic thresholding (using an image frequency analysis to obtain a global threshold value per image). Final segmentation of cells is achieved by fitting the largest inscribed circles into the centers of cell regions defined by the distance map of the binary images. Parameters of interest such as cell count and density, pleomorphism, polymegathism, and F-measure are computed on a publicly available data-set (Confocal Corneal Endothelial Microscopy Data Set - Rotterdam Ophthalmic Data Repository) and compared against the results of the segmentation methods included with the data set, and the results of state of the art automatic methods. The obtained results achieve higher accuracy compared to the results of the segmentation included with the data set (e.g., -proposed versus dataset in R-2 and mean relative error-, cell count: 0.823, - 0.241 versus 0.017, 0.534; cell density: 0.933, - 0.067 versus 0.154, 0.639; cell polymegathism: 0.652, - 0.079 versus 0.075, 0.886; cell pleomorphism: 0.242, - 0.128 versus 0.0352, - 0.222, respectively), and are in good agreement with the results of the state of the art method

Sparse and Non-Sparse Multiple Kernel Learning for Recognition

Abstract. The development of Multiple Kernel Techniques has become of particular interest for machine learning researchers in Computer Vision topics like image processing, object classification, and object state recognition. Sparsity-inducing norms along with non-sparse formulations promote different degrees of sparsity at the kernel coefficient level, at the same time permitting non-sparse combination within each individual kernel. This makes MKL models very suitable for different problems, allowing adequate selection of the regularizer according to different norms and the nature of the problem. We formulate and discuss MKL regularizations and optimization approaches, as well as demonstrate MKL effectiveness compared to the state-of-the-art SVM models using a Computer Vision Recognition problem

Combined Hierarchical Watershed Segmentation and SVM Classification for Pap Smear Cell Nucleus Extraction

Abstract. In this paper, we propose a two-phase approach to nuclei segmentation/classification in Pap smear test images. The first phase, the segmentation phase, includes a morphological algorithm (watershed) and a hierarchical merging algorithm (waterfall). In the merging step, waterfall uses spectral and shape information as well as the class information. In the second phase, classification, the goal is to obtain nucleus regions and cytoplasm areas by classifying the regions resulting from the first phase based on their spectral and shape features, merging of the adjacent regions belonging to the same class. Between the two phases, three unsupervised segmentation quality criteria were tested in order to determine the best one selecting the best level after merging. The classification of individual regions is obtained using a Support Vector Machine (SVM) classifier. The segmentation and classification results are compared to the segmentation provided by expert pathologists and demonstrate the efficacy of the proposed method

Development of Healthcare Applications using Facilities available in modern Mobile Devices

Mobile devices have proliferated at affordable prices nowadays. These devices provide high computational power and high-quality communication facilities, with near and remote users. In addition, most mobile devices include flexible sensors such as microphones, accelerometers and cameras, which are suitable for biomedical applications. Several students in the Biomedical Engineering Program, at our research center (Center for Studies on Electronics and Information Technologies, CEETI) have taken advantage of the mobile devices internal hardware capabilities to develop health care applications. These applications have been developed for Android-based devices. In this article two applications are presented, based on mobile platform, ECAH-M for speech analysis and i-Walker a pedometer.Los dispositivos móviles han proliferado a precios accesibles en la actualidad. Los mismos están dotados con facilidades con alto poder computacional y comunicación de alta calidad tanto para usuarios cercanos como remotos. Además, la mayoría incluye sensores flexibles tales como micrófonos, acelerómetros y cámaras, los cuales los hacen factibles para aplicaciones biomédicas. Varios estudiantes del Programa de Pregrado de Ingeniería Biomédica del Centro de Estudios de Electrónica y Tecnologías de la Información (CEETI) de la Universidad Central "Marta Abreu" de las Villas, han utilizado las ventajas de las capacidades internas del hardware de dispositivos móviles, con el fin de desarrollar aplicaciones para cuidados de la salud. Dichas aplicaciones se han desarrollado para dispositivos basados en el sistema operativo Androide. En este artículo presentamos un par de ellas, ECAH-M para el análisis del habla y el i-Walker, un pedómetro