Iván R. Terol-Villalobos

Morphological Background Detection and Enhancement of Images With Poor Lighting

In this paper, some morphological transformations are used to detect the background in images characterized by poor lighting. Lately, contrast image enhancement has been carried out by the application of two operators based on the Webers law notion. The first operator employs information from block analysis, while the second transformation utilizes the opening by reconstruction, which is employed to define the multibackground notion. The objective of contrast operators consists in normalizing the grey level of the input image with the purpose of avoiding abrupt changes in intensity among the different regions. Finally, the performance of the proposed operators is illustrated through the processing of images with different backgrounds, the majority of them with poor lighting conditions.

Morphological image enhancement and segmentation

Publisher Summary This chapter discusses some basic concepts of mathematical morphology (MM). The notion of morphological slope filters (MSF) and their algebraic properties are introduced, and the sequential morphological slope filters are described in the chapter. The chapter focuses on image enhancement and segmentation by using a class of morphological nonincreasing filters called “morphological slope filters.” The notion of morphological gradients was used to build this class of MSF. The idea of retaining the zones of the image with a strong gradient and attenuating the other ones gives good contrast enhancement. Image segmentation using MSF is described, and new results concerning MSF on graphs are discussed in the chapter. Although image-enhancement techniques are generally empirical, this technique has a well-defined theoretical framework, expressed by a set of properties that permits a better understanding of the technique. By applying the MSF in a sequential way, new properties are found that enable us to retain more features from the original image. By increasing the contrast at each step of the sequence of filters, the filtering process is better controlled.

Morphological contrast measure and contrast enhancement: One application to the segmentation of brain MRI

In this paper a morphological contrast measure is introduced. The quantification of the contrast is based on the analysis of the edges, which are associated with substantial changes in luminance. Due to this, the contrast measure is used to detect the image that presents a high visual contrast when a set of output images is analyzed. The set of output images is obtained by application of morphological contrast mappings with size criteria. These contrast transformations are defined under the notion of partitions generated by the set of flat zones of the image; therefore, they are connected transformations. In addition, an application to the segmentation of white and grey matter in brain magnetic resonance images (MRI) is provided. The detection of white matter is carried out by means of a contrast mapping with specific control parameters; subsequently, white and grey matter are separated and their ratio is calculated and compared with manual segmentations. Also, an example of segmentation of white and grey matter in MRI corrupted by 5% noise is presented in order to observe the performance of the morphological transformations proposed in this work.

Contrast enhancement and image segmentation using a class of morphological nonincreasing filters

In this paper, contrast enhancement and image segmentation are investigated using a class of morphological nonincreasing filters that can be considered toggle mappings. These nonincreasing filters are built using the traditional morphological gradients. These filters have interesting properties and give essential contrast to the images. We apply them sequentially between two or more given parameters in order to obtain intermediate results. This approach improves the control of the filtering process and provides other tools for contrasting images. We presented several new properties and one study of the invariant set of these filters. Using these new propositions, we show that it is possible to obtain better results in image segmentation when we apply the watershed transformation. Also, we propose an algorithm for segmenting images using this class of nonincreasing filters. The method is applied in a geodesic way using two different criteria for segmenting an image. We relate our results with a recent method in mathematical morphology called the flat zone approach and we compare our approach with another method in image processing, the so-called quadtree approach.

Openings and closings with reconstruction criteria: a study of a class of lower and upper levelings

A study of a class of openings and closings is investigated using reconstruction criteria. The main goal in studying these transformations consists of eliminating some inconveniences of the morphological opening (closing) and the opening (closing) by reconstruction. The idea in building these new openings and closings comes from the notions of filters by reconstruction and levelings. In particular, concerning the notion of levelings, a study of a class of lower and upper levelings is carried out. The original work of levelings is due to Meyer, who proposes this notion and introduces some criteria to build the levelings in the general case (extended levelings and self-dual transformations). We see the criteria proposed by Meyer as reconstruction criteria during the reconstruction process from a marker image into the reference image. We show that new openings and closings are obtained, enabling intermediate results between the traditional opening (closing) and the opening (closing) by reconstruction. Some applications are studied to validate these transformations.

Morphological Connected Filtering on Viscous Lattices

This paper deals with the notion of connectivity in viscous lattices. In particular, a new family of morphological connected filters, called connected viscous filters is proposed. Connected viscous filters are completely determined by two criteria: size parameter and connectivity. The connection of these filters is defined on viscous lattices in such a way that they verify several properties of the traditionally known filters by reconstruction. Moreover, reconstruction algorithms used to implement filters by reconstruction can also be employed to implement these new filters. We also show that connected viscous filters have a behavior similar to filters with reconstruction criteria. The interest of these new connected filters is illustrated with different examples.

Image segmentation and filtering based on transformations with reconstruction criteria

In this paper, a class of transformations with reconstruction criteria, derived from the reconstruction transformations, is investigated. The idea to build these transformations consists in stopping the reconstruction process according to a size criterion. This class of transformations was initially proposed for obtaining intermediate results between the morphological opening and the opening by reconstruction. Here, the transformations are presented in the general case, as in the reconstruction transformations case, by imposing some conditions on the marker. We show that the set of markers for the transformations with reconstruction criteria is given by the set of dilated images. The interest of these transformations in image segmentation is shown, and in particular, the form of selecting the markers for segmenting images is described for binary images. Also, the use of the opening and closing with reconstruction criteria to build other morphological tools is illustrated to show the performance of these transformations. In particular, the notion of granulometry and the alternating sequential filters using openings and closings with reconstruction criteria are investigated.

Filtering of mixed Gaussian and impulsive noise using morphological contrast detectors

Several morphological transformations to detect noise are introduced. The initial method is a modification of a procedure presented previously in the current literature. The proposals given in the study allow to detect noise in two ways: (i) using a contrast measure and (ii) applying different proximity criteria into several proposed toggle mappings. In the end, two of the proposals given in this study yield a better performance with respect to methods in which this research is based. However, although the methodology to identify noise works adequately, the results are limited due to the use of the structuring element. In Section 4, an image with two types of noise is cleaned. Such image is contaminated with zero mean Gaussian noise with 0.01 variance and 5% of salt and pepper noise. From this experiment, the proposal giving the best performance is selected; subsequently, this is compared with other recent operators as PDEs, wavelets, morphological connected rank max opening and amoebas.

Application of morphological connected openings and levelings on magnetic resonance images of the brain

In this article, several advanced connected transformations from mathematical morphology for computational neuroanatomy applications are developed. In particular, brain is separated from the skull in MRI T1 using morphological connected openings. The use of connected transformations allow the preservation of regions, without introduce new information. As a result, the segmented brains preserve by complete information of the original images being more reliable for the specialist who deals with information such as white and gray matter.

A Probabilistic Measure of Circularity

The circle is a useful morphological structure: in many situations, the importance is focused on the measuring of the similarity of a perfect circle against the object of interest. Traditionally, the well-known geometrical structures are employed as useful geometrical descriptors, but an adequate characterization and recognition are deeply affected by scenarios and physical limitations (such as resolution and noise acquisition, among others). Hence, this work proposes a new circularity measure which offers several advantages: it is not affected by the overlapping, incompleteness of borders, invariance of the resolution, or accuracy of the border detection. The propounded approach deals with the problem as a stochastic non-parametric task; the maximization of the likelihood of the evidence is used to discover the true border of the data that represent the circle. In order to validate the effectiveness of our proposal, we compared it with two recently effective measures: the mean roundness and the radius ratio.