Objetivos: Describir la experiencia de 43 pacientes con fístula vesicovaginal (FVV) y la reparación con técnica laparoscópica entre 2009 y 2020, analizar su comportamiento y evolución. Métodos: Análisis de 43 pacientes diagnosticadas de FVV supratrigonales secundarias a histerectomías, la mismas que fueron resueltas laparoscópicamente. Resultados: La FVV es una complicación que se presenta en mujeres de edad media a menudo en periodo fértil, y claramente demostrado con el antecedente de cirugía o procedimiento ginecológico. Las pacientes fueron diagnosticadas de fístula vesicovaginal, las mismas que fueron intervenidas quirúrgicamente mediante técnica laparoscópica. El tiempo operatorio promedio fue de 172 minutos. Ninguna paciente requirió transfusión sanguínea y el tiempo de hospitalización promedio fue de 3,7 días. No se presentaron complicaciones ni recidivas, con un seguimiento promedio de 12 meses. Conclusión: La reparación laparoscópica de la fístula vesicovaginal es una técnica segura, poco invasiva y reproducible en manos entrenadas.

A fracture is the solution of continuity of bone tissue in any bone of the body occurs as a result of excessive stress that exceeds bone resistance, ie is the consequence of a single or multiple overload and occurs in milliseconds. The development of magnetic resonance imaging and computerized tomography have made it possible to know and evaluate the different pathologies of the human being more accurately. Edge detection is a fundamental tool in image medical processing, particularly in the areas of feature detection, which aim at identifying points in a digital image at which the image has discontinuities. In order to improve the computing speed, was used parallel computing which support NVIDIA GPU. This work presents an improved methodology for processing bone fracture images before and after surgery using segmentation and graphic accelerator cards to help the medical specialist in the analysis and evaluation of the images.

The medical diagnosis of most pathologists requires the analysis of the image studies. Therefore, it is important to get the best quality of the images without noise and highlight the details of tissues. The principal aim of this work is to apply different algorithms and filters to reduce the noise of magnetic resonance brain images, due to the noise in these can cause to give a difficult diagnosis. The algorithms considered in this work are the fast mean filter, fast Gaussian filter, and fast median filter; also was used parallel programming in OpenMP. The results show that the parallel implementation of algorithms has more performance in the time processing, localization, and noise reduction than sequential and classic implementation.

Most of the medical diagnostic requires studies of medical images for to give an accurate treatment. Therefore is important the improvement of the medical images in terms of noise, quality, and morphological definition. The medical images series contains approximately 20% of noise caused by the equipment itself, especially in the x-ray modality. Therefore, the principal aim of this project is to develop an algorithm that helps suppress the noise as a preprocessing stage before medical analysis. The algorithm for noise reduction proposed uses classic and optimized mean filter, Gaussian filter, and median filter. This project also uses OpenMP parallel programming to optimize processing time and computational resources. The parallel implementation results of algorithms with sequential and classic implementation show great performance in the quality of the time processing, noise localization, and noise reduction. This improvement helps medical professionals get better details about the different pathologies for effective diagnostics and treatment.

Breast cancer is a serious and become common disease that affects thousands of women in the world each year. Early detection is essential and critical for effective treatment and patient recovery. This work gives an idea of extracting features from the mammogram image to find affected area, which is a crucial step in breast cancer detection and verification. We present the affected area identification through in which place the tumor cells are extracted directly from the grey scale mammogram image. To remove noise from the mammogram image this work presents a simple and efficient technique using fast average filter, to determine the pixel value in the noise less image. To contour detection used shearlet transform and classic filters as like Sobel, Prewitt, and others. To evaluate the quality of contour used SSIM measure. Our experimental results demonstrate that our approach can achieve the better performance in time duration of reduce noise and with shearlet transform select affected area with high efficiency.

Medical images are corrupted by different types of noises caused by the equipment itself. It is very important to obtain precise images to facilitate accurate observations for the given application. Removing of noise from images is now a very challenging issue in the field of medical image processing. This work undertake the study of noise removal techniques in medical image by using fast implementation of different digital filters, such as average, median and Gaussian filter. Processing of X-ray medical images takes a significant time. Now days modern hardware allows to use parallel technology for image processing on CPU and GPU. Using GPU processing technology were proposed parallel implementations of noise reduction algorithm taking into account the data parallelism. The experimental study conducted on medical X-ray image, so that to choose the best filters considering medical task and time of processing. The comparison of the implementation of fast filters algorithm and GPU implementation show great increase in performance. Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms.

Image processing techniques play an important role in the diagnostics and detection of diseases and monitoring the patients having these diseases. The chapter presents the medical image processing and morphological analysis in the solution of urology and plastic surgery (hernioplasty) problems. Novel methodology for processing medical images using a color coding of contour representation obtained by Digital Shearlet Transform (DST) has been presented. The object contours in the medical urology images are obtained using the conventional filters, and then results are compared. Since medical images can contain some noise, it makes sense to suppress the noise at the preprocessing step. For this purpose, the optimized in implementation algorithms of the most frequently used filters, such as the mean filter, Gaussian filter, median filter, and 2D cleaner filter, had been developed. A comparison of the optimized and ordinary implementations of noise reduction filter shows great speed improvement of the optimized implementations around 3-20 times). Additionally, the parallel implementation gives 2-3.5 times performance boost. The proposed methodology allows to improve the accuracy and decrease the error of the sought parameters and characteristics by 10-20% on average without a lack of significant details in the structural features of the examined objects. The results of the experimental study show an error decrease in data representation for the plastic surgery (hernioplasty) by 15-25%.

The contour detection has an important role in image processing, especially in the detection and the extraction physical features, those which are useful to their enforcement in the analysis of cadasters. A new methodology is shown for processing satellite images using spatial filters. The contour of satellite images are obtained and compared with conventional processing filters. The results were obtained with the filters Sobel, Prewitt, Roberts, Canny, and LoG for processing satellite images and they were evaluated using Structural Similarity Index (SSIM) for measuring image quality.

Brain tumor detection is well known research area for medical and computer scientists. In last decades there has been much research done on tumor detection, segmentation, & classification. Medical imaging plays a central role in the diagnosis of brain tumors and nowadays uses methods non-invasive, high-resolution techniques, especially magnetic resonance imaging and computed tomography scans. Edge detection is a fundamental tool in image processing, particularly in the areas of feature detection and feature extraction, which aim at identifying points in a digital image at which the image has discontinuities. Shearlets is the most successful frameworks for the efficient representation of multidimensional data, capturing edges and other anisotropic features which frequently dominate multidimensional phenomena. The paper proposes an improved brain tumor detection method by automatically detecting tumor location in MR images, its features are extracted by new shearlet transform.

A new method for measuring the spectrum of surface roughness based on Bragg scattering, is proposed. This method is characterized by its high precision of measurement of the spatial amplitudes and frequencies. Was determined the rang of spatial frequencies (periods) of harmonics in the roughness spectrum surfaces (0,π) (0; π). When the rotation´s angle of grating is (0,π/4) will be a forward scattering or Bragg´s deflector. At rotation´s angle of grating is (π/2,π/4) or (-3π/4,-π/2) will a Bragg´s mirror. The measurements are made in a gradient waveguide, obtained by ion exchange method.

Within the multi-resolution analysis, the study of the image compression algorithm using the Haar wavelet has been performed. We have studied the dependence of the image quality on the compression ratio. Also, the variation of the compression level of the studied image has been obtained.It is shown that the compression ratio in the range of 8-10 is optimal for environmental monitoring. Under these conditions the compression level is in the range of 1.7 - 4.2, depending on the type of images.It is shown that the algorithm used is more convenient and has more advantages than Winrar. The Haar wavelet algorithm has improved the method of signal and image processing.

Determining the mean square deviation σ of surface roughness and the imaginary part of permittivity n''of a planar gradient optical waveguides n(y)= n3+Δn'(y)+ in''(y) = n'(y)+ in''(y), using the integral waveguide scattering method. For experiment two samples are prepared. The first sample using exchange with silver ions and obtained a parabolic profile, subsequently the method of solid-state diffusion of lead oxide in the glass substrate provides a waveguide with a Gaussian distribution profile permittivity. In both cases, laser radiation is used with a wavelength of 0.6328 microns.

Contour detect in the urology medical image. The investigation algorithm FFST revealed that the contours of objects can be obtained as the sum of the coefficients shearlet transform a fixed value for the last scale and the of all possible values of the shift parameter. The results of this task using a modified algorithm FFST for data processing urology image is show. In the results of the corresponding calculations for some images and a comparison with filters Sobel and Prewitt.Shows the relevant calculations for some images and a comparison with Sobel and Prewitt filters respectively.