Gustavo Camps i Valls⁽¹⁾, Antonio J. Serrano López⁽²⁾, Jesús Modia⁽³⁾, José David Martín Guerrero⁽⁴⁾, José Vicente Francés Víllora^(5)
(1)Universitat de València - Burjassot, Valencia. Spain
⁽²⁾⁽³⁾Dpto. Electrónica. Universidad de Valencia - Burjasot. Spain
⁽⁴⁾G.P.D.S. Departament d´Enginyeria Electrònica. Universitat de València - Burjassot. Spain
⁽⁵⁾Dpto. Ingenieria Electrónica. University of Valencia - Burjassot. Spain

	[Health Informatics]	[Medical Electronics & Engineering]

SELECTIVE MEDIAN FILTER ALGORITHM

The main function of the median filters is to force the points with values of gray very different to its neighbors to have next values to them, so that picks of intensity that appear in uniform areas are eliminated. The median filters have been broadly used in the elimination of the impulsional noise (noises of maximum or minimum value). Its ability to eliminate impulses grows with the size of the window of the filter but its main drawback is to blur the images, losing details and definition. In our work we will try to eliminate random noise on images with 256 levels of gray using a selective mask of medium.

Evidently the computacional burden increases with the width of the vicinity area. The algorithm that we present is based on a double outline: first it tries to identify and to locate the random noise in the image and in second place, the filter is applied of medium only under certain circumstances. Therefore it is a Selective Filter of Medium. The central idea of the procedure will be the one of marking all the points that belong to a chain of at least longitude equal to 3 (probably being part of the original image and not of the noise). Then the median filter will be applied on the points that are not marked that will correspond to the isolated plots, that is to say, to the noise. These two processes, the one of marking and the one of applying the medium one are made in fact simultaneously.

Differences in values of gray smaller than 18 demonstrated in general not to be detectables for the human eye and for that reason, the noise that doesn´t differ of the original image in bigger values that 18 won´t be necessary to eliminate it.

Evidently there is a relationship between the intensity of the noise and the ownership or not of these chains to the original image. When an intense noise invades an image it will cause the appearance of chains of noise of those longitudes. However it is not very probable that in an image with little noise chains of noise of longitudes 3 and 4 appear, for what you/they will very probably belong to the original image in this case and they should not be substituted therefore.

A way to identify the uniform areas is by means of a variation of the previous algorithm: the points that belong to uniform areas will belong to a great chain and therefore they will have a high value in this auxiliary matrix.The chains that we look for to eliminate will have values of 3 and of 4 and they will be surrounded by high values. This algorithm has been experienced with quite good results, although it is still in phase of improvement.

Discussion Board

Any Comment to this presentation?

[ABSTRACT] [INTRODUCTION] [SELECTIVE MEDIAN FILTER ALGORITHM] [RESULTS] [FIGURES] [DISCUSSION] [REFERENCES] [Discussion Board]

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	[Health Informatics]	[Medical Electronics & Engineering]

Gustavo Camps i Valls, Antonio J. Serrano López, Jesús Modia, José David Martín Guerrero, José Vicente Francés Víllora
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Last update: 17/01/00