5 research outputs found
Mammography Image Enhancement using Linear, Nonlinear and Wavelet Filters with Histogram Equalization
In the worldwide, breast cancer is one of the major diseases among the women. In the modern medical science, there are plenty of newly devised methodologies and techniques for the timely detection of breast cancer. However, there are difficulties still exist for detecting breast cancer at an early stage for its diagnoses because of poor visualization and artifacts present in the mammography. Thus the Digital mammographic image preprocessing often requires, enhancement of the image to improve the quality while preserving important details. The proposed method works in three stages. First it removes all the artifacts present in the image. Second it denoise the image by using Linear, nonlinear and wavelet filters. Third, contrast of the image increased by histogram equalization. This method definitely helps to computer aided diagnosis system to increase the accuracy. The experimental results are tested on two standard datasets MIAS and DDSM.
A Panorama on Multiscale Geometric Representations, Intertwining Spatial, Directional and Frequency Selectivity
The richness of natural images makes the quest for optimal representations in
image processing and computer vision challenging. The latter observation has
not prevented the design of image representations, which trade off between
efficiency and complexity, while achieving accurate rendering of smooth regions
as well as reproducing faithful contours and textures. The most recent ones,
proposed in the past decade, share an hybrid heritage highlighting the
multiscale and oriented nature of edges and patterns in images. This paper
presents a panorama of the aforementioned literature on decompositions in
multiscale, multi-orientation bases or dictionaries. They typically exhibit
redundancy to improve sparsity in the transformed domain and sometimes its
invariance with respect to simple geometric deformations (translation,
rotation). Oriented multiscale dictionaries extend traditional wavelet
processing and may offer rotation invariance. Highly redundant dictionaries
require specific algorithms to simplify the search for an efficient (sparse)
representation. We also discuss the extension of multiscale geometric
decompositions to non-Euclidean domains such as the sphere or arbitrary meshed
surfaces. The etymology of panorama suggests an overview, based on a choice of
partially overlapping "pictures". We hope that this paper will contribute to
the appreciation and apprehension of a stream of current research directions in
image understanding.Comment: 65 pages, 33 figures, 303 reference
Visibility recovery on images acquired in attenuating media. Application to underwater, fog, and mammographic imaging
136 p.When acquired in attenuating media, digital images of ten suffer from a particularly complex degradation that reduces their visual quality, hindering their suitability for further computational applications, or simply decreasing the visual pleasan tness for the user. In these cases, mathematical image processing reveals it self as an ideal tool to recover some of the information lost during the degradation process. In this dissertation,we deal with three of such practical scenarios in which this problematic is specially relevant, namely, underwater image enhancement, fogremoval and mammographic image processing. In the case of digital mammograms,X-ray beams traverse human tissue, and electronic detectorscapture them as they reach the other side. However, the superposition on a bidimensional image of three-dimensional structures produces low contraste dimages in which structures of interest suffer from a diminished visibility, obstructing diagnosis tasks. Regarding fog removal, the loss of contrast is produced by the atmospheric conditions, and white colour takes over the scene uniformly as distance increases, also reducing visibility.For underwater images, there is an added difficulty, since colour is not lost uniformly; instead, red colours decay the fastest, and green and blue colours typically dominate the acquired images. To address all these challenges,in this dissertation we develop new methodologies that rely on: a)physical models of the observed degradation, and b) the calculus of variations.Equipped with this powerful machinery, we design novel theoreticaland computational tools, including image-dependent functional energies that capture the particularities of each degradation model. These energie sare composed of different integral terms that are simultaneous lyminimized by means of efficient numerical schemes, producing a clean,visually-pleasant and use ful output image, with better contrast and increased visibility. In every considered application, we provide comprehensive qualitative (visual) and quantitative experimental results to validateour methods, confirming that the developed techniques out perform other existing approaches in the literature
Visibility Recovery on Images Acquired in Attenuating Media. Application to Underwater, Fog, and Mammographic Imaging
When acquired in attenuating media, digital images often suffer from a
particularly complex degradation that reduces their visual quality, hindering
their suitability for further computational applications, or simply
decreasing the visual pleasantness for the user. In these cases, mathematical
image processing reveals itself as an ideal tool to recover some
of the information lost during the degradation process. In this dissertation,
we deal with three of such practical scenarios in which this problematic
is specially relevant, namely, underwater image enhancement, fog
removal and mammographic image processing. In the case of digital mammograms,
X-ray beams traverse human tissue, and electronic detectors
capture them as they reach the other side. However, the superposition
on a bidimensional image of three-dimensional structures produces lowcontrasted
images in which structures of interest suffer from a diminished
visibility, obstructing diagnosis tasks. Regarding fog removal, the loss
of contrast is produced by the atmospheric conditions, and white colour
takes over the scene uniformly as distance increases, also reducing visibility.
For underwater images, there is an added difficulty, since colour is not
lost uniformly; instead, red colours decay the fastest, and green and blue
colours typically dominate the acquired images. To address all these challenges,
in this dissertation we develop new methodologies that rely on: a)
physical models of the observed degradation, and b) the calculus of variations.
Equipped with this powerful machinery, we design novel theoretical
and computational tools, including image-dependent functional energies
that capture the particularities of each degradation model. These energies
are composed of different integral terms that are simultaneously
minimized by means of efficient numerical schemes, producing a clean,
visually-pleasant and useful output image, with better contrast and increased
visibility. In every considered application, we provide comprehensive
qualitative (visual) and quantitative experimental results to validate
our methods, confirming that the developed techniques outperform other
existing approaches in the literature
Advances in Image Processing, Analysis and Recognition Technology
For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches