Impulsive noise removal from color images with morphological filtering

This paper deals with impulse noise removal from color images. The proposed noise removal algorithm employs a novel approach with morphological filtering for color image denoising; that is, detection of corrupted pixels and removal of the detected noise by means of morphological filtering. With the help of computer simulation we show that the proposed algorithm can effectively remove impulse noise. The performance of the proposed algorithm is compared in terms of image restoration metrics and processing speed with that of common successful algorithms.Comment: The 6th international conference on analysis of images, social networks, and texts (AIST 2017), 27-29 July, 2017, Moscow, Russi

On the importance of metrics in practical applications

[EN] Students motivation for learning mathematical concepts can be increased when showing the usefulness of these concepts in practical problems. One important mathematical concept is the concept of metric space and, more related to the applications, the concept of metric function. In this work we aim to illustrate how important is to appropriately choose the metric when dealing with a practical problem. In particular, we focus on the problem of detection of noisy pixels in colour images. In this context, it is very important to appropriately measure the distances and similarities between the image pixels, which is done by means of an appropriate metric. We study the performance of different metrics, including recent fuzzy metrics, within a specific filter to show that it is indeed a critical choice to appropriately solve the task.Camarena, J.; Morillas, S.; Cisneros, F. (2011). On the importance of metrics in practical applications. Modelling in Science Education and Learning. 4:119-128. doi:10.4995/msel.2011.3066SWORD119128

Análisis del Filtro FPGA en Imágenes de Tomografía Computarizada para la Reducción de Dosis Radiactiva

[EN] X-Ray or CT (computed tomography) images may have noise due to image acquisition process. As contaminated images complicate diagnosis many filters have been developed to overcome this problem. In this work we study the behavior of a Fuzzy method called FPGA, which detect and correct impulsive and Gaussian noise, used over a medical image obtained from the mini-MIAS database that has been altered with impulsive and/or Gaussian noise. The aim of the study is verify if FPGA is a candidate to be used as a method to reduce the radiation dose in CT. Results show that FPGA outperforms the rest of the methods studied and it reveals itself as a good candidate to be employed in CT images to reduce the radiation dose.[ES] Las imágenes de Rayos-X o de tomografía computarizada (CT) pueden contener ruido debido al proceso de adquisición. Este ruido complica sustancialmente el proceso diagnóstico, por lo que será necesario el desarrollo de filtros efectivos. En este trabajo se estudia el comportamiento del filtro Fuzzy Peer Group Averaging (FPGA) sobre una colección de imágenes mamográficas que ha sido previamente contaminada con ruido impulsivo y gaussiano. El objetivo del trabajo es averiguar si FPGA es adecuado para la mejora de imágenes CT obtenidas con una dosis de radiación reducida. Los resultados indican que FPGA se comporta, efectivamente, mejor que el resto de métodos estudiados en este trabajo y por tanto resulta un candidato adecuado.This work was partially funded by ANITRAN PROMETEO/2010/039, the Spanish Ministry of Science and Innovation (Project TIN2008-06570-C04-04), and the spin-off Titania (Grupo Dominguis).Parcero Iglesias, E.; Vidal Gimeno, VE.; Verdú Martín, GJ.; Josep Arnal García; Mayo Nogueira, P. (2014). Análisis del Filtro FPGA en Imágenes de Tomografía Computarizada para la Reducción de Dosis Radiactiva. Sociedad Nuclear Española. http://hdl.handle.net/10251/70824

Analysis of FPGA filter in computed tomography images for radioactive dose reduction

[EN] X-Ray or CT (computed tomography) images may have noise due to image acquisition process. As contaminated images complicate diagnosis many filters have been developed to overcome this problem. In this work we study the behavior of a Fuzzy method called FPGA, which detect and correct impulsive and Gaussian noise, used over a medical image obtained from the mini-MIAS database that has been altered with impulsive and/or Gaussian noise. The aim of the study is verify if FPGA is a candidate to be used as a method to reduce the radiation dose in CT. Results show that FPGA outperforms the rest of the methods studied and it reveals itself as a good candidate to be employed in CT images to reduce the radiation dose.[ES] Las imágenes de Rayos-X o de tomografía computarizada (CT) pueden contener ruido debido al proceso de adquisición. Este ruido complica sustancialmente el proceso diagnóstico, por lo que será necesario el desarrollo de filtros efectivos. En este trabajo se estudia el comportamiento del filtro Fuzzy Peer Group Averaging (Fuzzy PGA) sobre una colección de imágenes mamográficas que ha sido previamente contaminada con ruido impulsivo y gaussiano. El objetivo del trabajo es averiguar si Fuzzy PGA es adecuado para la mejora de imágenes CT obtenidas con una dosis de radiación reducida. Los resultados indican que Fuzzy PGA se comporta, efectivamente, mejor que el resto de métodos estudiados en este trabajo y por tanto resulta un candidato adecuado.Parcero Iglesias, E.; Vidal Gimeno, VE.; Verdú Martín, GJ.; Arnal García, J. (2014). Analysis of FPGA filter in computed tomography images for radioactive dose reduction. Grupo Senda. http://hdl.handle.net/10251/49701

Adaptive Marginal Median Filter for Colour Images

This paper describes a new filter for impulse noise reduction in colour images which is aimed at improving the noise reduction capability of the classical vector median filter. The filter is inspired by the application of a vector marginal median filtering process over a selected group of pixels in each filtering window. This selection, which is based on the vector median, along with the application of the marginal median operation constitutes an adaptive process that leads to a more robust filter design. Also, the proposed method is able to process colour images without introducing colour artifacts. Experimental results show that the images filtered with the proposed method contain less noisy pixels than those obtained through the vector median filter

Robustifying Vector Median Filter

This paper describes two methods for impulse noise reduction in colour images that outperform the vector median filter from the noise reduction capability point of view. Both methods work by determining first the vector median in a given filtering window. Then, the use of complimentary information from componentwise analysis allows to build robust outputs from more reliable components. The correlation among the colour channels is taken into account in the processing and, as a result, a more robust filter able to process colour images without introducing colour artifacts is obtained. Experimental results show that the images filtered with the proposed method contain less noisy pixels than those obtained through the vector median filter. Objective measures demonstrate the goodness of the achieved improvement

Nonlinear smoothing of skin lesions images driven by derivative filters

Image segmentation is an important step to suitable extraction of features of objects from images. However, the presence of noise interferes in segmentation quality; for example, by generating the detection of false edges (or false borders). To diminish the problems caused by the presence of noise in images, various smoothing techniques have been proposed to pre-process the original images. Those methods reduce the noise presented in input images, but they can also strongly affect the borders of the objects, leading to the loss of important details, such as the original roughness of the contours or the elimination of the borders of small objects. Among the existing smoothing techniques, one of the most promising is based on the use of anisotropic diffusion, which allows a selective smoothing that decreases the undesirable effects caused by noise presented in the input image and preserves the edges of the objects. However, the success of this smoothing method is strongly reliant on the number of iterations performed that depends on the input image. In this work, we propose the use of derivative filters for the definition of the appropriate number of iterations adopted by the smoothing method based on anisotropic diffusion, when it is applied for the removal of noise usually present in images of skin lesions. The experimental results demonstrate that the developed solution is promising, being able to determine the adequate number of iterations for smoothing the input images avoiding the excessive loss of details of the borders of the lesions presented in images

Restauración de Imágenes Médicas con Diferentes Tipos de Ruido

Las imágenes obtenidas por rayos X o computarizada tomografía computarizada (CT) en condiciones adversas, pueden estar contaminadas con ruido que puede afectar a la detección de enfermedades. Un gran número de técnicas de procesamiento de imágenes han sido propuestas para eliminar el ruido. Estas técnicas dependen del tipo de ruido presente en la imagen. En este trabajo, se propone un método para reducir el ruido gaussiano, impulsivo y speckle. Este filtro, llamado PGMFDNL combina un filtro de difusión no lineal con peer group y fuzzy. El filtro propuesto es capaz de reducir eficazmente el ruido de la imagen sin ningún tipo de información acerca del ruido presente en la imagen. Como resultado, el método propuesto obtiene un buen rendimiento en los diferentes tipos de ruido.Este trabajo fue financiado por el ministerio español de ciencia e innovación: Proyecto TIN2011-26254), ANITRAN PROMETEO/2010/039, Proyecto TIN2008-06570-C04-04, y DGEST ITCG a través del programa PROMEP (México).Sanchez, G.; Vidal Gimeno, VE.; Verdú Martín, GJ.; Mayo Nogueira, P.; Ródenas Escribá, FDA. (2013). Restauración de Imágenes Médicas con Diferentes Tipos de Ruido. http://hdl.handle.net/10251/48285