27,455 research outputs found
Active Contour Models for Manifold Valued Image Segmentation
Image segmentation is the process of partitioning a image into different
regions or groups based on some characteristics like color, texture, motion or
shape etc. Active contours is a popular variational method for object
segmentation in images, in which the user initializes a contour which evolves
in order to optimize an objective function designed such that the desired
object boundary is the optimal solution. Recently, imaging modalities that
produce Manifold valued images have come up, for example, DT-MRI images, vector
fields. The traditional active contour model does not work on such images. In
this paper, we generalize the active contour model to work on Manifold valued
images. As expected, our algorithm detects regions with similar Manifold values
in the image. Our algorithm also produces expected results on usual gray-scale
images, since these are nothing but trivial examples of Manifold valued images.
As another application of our general active contour model, we perform texture
segmentation on gray-scale images by first creating an appropriate Manifold
valued image. We demonstrate segmentation results for manifold valued images
and texture images
A Q-Ising model application for linear-time image segmentation
A computational method is presented which efficiently segments digital
grayscale images by directly applying the Q-state Ising (or Potts) model. Since
the Potts model was first proposed in 1952, physicists have studied lattice
models to gain deep insights into magnetism and other disordered systems. For
some time, researchers have realized that digital images may be modeled in much
the same way as these physical systems (i.e., as a square lattice of numerical
values). A major drawback in using Potts model methods for image segmentation
is that, with conventional methods, it processes in exponential time. Advances
have been made via certain approximations to reduce the segmentation process to
power-law time. However, in many applications (such as for sonar imagery),
real-time processing requires much greater efficiency. This article contains a
description of an energy minimization technique that applies four Potts
(Q-Ising) models directly to the image and processes in linear time. The result
is analogous to partitioning the system into regions of four classes of
magnetism. This direct Potts segmentation technique is demonstrated on
photographic, medical, and acoustic images.Comment: 7 pages, 8 figures, revtex, uses subfigure.sty. Central European
Journal of Physics, in press (2010
Evaluation of Statistical Features for Medical Image Retrieval
In this paper we present a complete system allowing the classification of medical images in order to detect possible diseases present in them. The proposed method is developed in two distinct stages: calculation of descriptors and their classification. In the first stage we compute a vector of thirty-three statistical features: seven are related to statistics
of the first level order, fifteen to that of second level where thirteen are calculated by means of co-occurrence matrices and two with absolute gradient; the last thirteen finally are calculated using run-length matrices. In the second phase, using the descriptors already calculated, there is the actual image classification. Naive Bayes, RBF, Support VectorMa-
chine, K-Nearest Neighbor, Random Forest and Random Tree classifiers are used. The results obtained from the proposed system show that the analysis carried out both on textured and on medical images lead to have a high accuracy
Texture descriptors applied to digital mammography
Breast cancer is the second cause of death among women cancers. Computer Aided Detection has been demon- strated an useful tool for early diagnosis, a crucial as- pect for a high survival rate. In this context, several re- search works have incorporated texture features in mam- mographic image segmentation and description such as Gray-Level co-occurrence matrices, Local Binary Pat- terns, and many others. This paper presents an approach for breast density classi¯cation based on segmentation and texture feature extraction techniques in order to clas- sify digital mammograms according to their internal tis- sue. The aim of this work is to compare di®erent texture descriptors on the same framework (same algorithms for segmentation and classi¯cation, as well as same images). Extensive results prove the feasibility of the proposed ap- proach.Postprint (published version
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