3,010 research outputs found
Unsupervised morphological segmentation for images
This paper deals with a morphological approach to unsupervised image segmentation. The proposed technique relies on a multiscale Top-Down approach allowing a hierarchical processing of the data ranging from the most global scale to the most detailed one. At each scale, the algorithm consists of four steps: image simplification, feature extraction, contour localization and quality estimation. The main emphasis of this paper is to discuss the selection of a simplification filter for segmentation. Morphological filters based on reconstruction proved to be very efficient for this purpose. The resulting unsupervised algorithm is very robust and can deal with very different type of images.Peer ReviewedPostprint (published version
Applying watershed algorithms to the segmentation of clustered nuclei
Cluster division is a critical issue in fluor escence
micr oscopy-based analytical cytology when preparation
protocols do not provide appropriate separation
of objects. Overlooking cluster ed nuclei and
analyzing only isolated nuclei may dramatically incr
ease analysis time or af fect the statistical validation
of the r esults. Automatic segmentation of cluster
ed nuclei r equir es the implementation of specific
image segmentation tools. Most algorithms are inspired by one of the two following strategies: 1)
cluster division by the detection of inter nuclei gradients;
or 2) division by definition of domains of
influence (geometrical approach). Both strategies
lead to completely different implementations, and
usually algorithms based on a single view strategy
fail to corr ectly segment most cluster ed nuclei, or
per for m well just for a specific type of sample. An
algorithm based on morphological watersheds has
been implemented and tested on the segmentation
of micr oscopic nuclei clusters. This algorithm pr ovides
a tool that can be used for the implementation
of both gradient- and domain-based algorithms, and,
mor e importantly, for the implementation of mixed
(gradient- and shape-based) algorithms. Using this
algorithm, almost 90% of the test clusters wer e
corr ectly segmented in peripheral blood and bone
marr ow pr eparations. The algorithm was valid for
both types of samples, using the appr opriate markers
and transfor mations.Contract grant sponsor: ARCADIM Project; Contract grant number: CICYT TIC92-0922-C02-01 (Comisión Interministerial de Ciencia y Tecnología); Contract grant sponsor: European Concerted Action CA-AMCA; Contract grant number: BMH1-CT92-1307; Contract grant sponsor: Comunidad Autónoma de Madrid (CAM); Contract grant sponsor: Universidad Politécnica de Madrid (UPM).Publicad
Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines
In this paper, we address the problem of efficient execution of a computation
pattern, referred to here as the irregular wavefront propagation pattern
(IWPP), on hybrid systems with multiple CPUs and GPUs. The IWPP is common in
several image processing operations. In the IWPP, data elements in the
wavefront propagate waves to their neighboring elements on a grid if a
propagation condition is satisfied. Elements receiving the propagated waves
become part of the wavefront. This pattern results in irregular data accesses
and computations. We develop and evaluate strategies for efficient computation
and propagation of wavefronts using a multi-level queue structure. This queue
structure improves the utilization of fast memories in a GPU and reduces
synchronization overheads. We also develop a tile-based parallelization
strategy to support execution on multiple CPUs and GPUs. We evaluate our
approaches on a state-of-the-art GPU accelerated machine (equipped with 3 GPUs
and 2 multicore CPUs) using the IWPP implementations of two widely used image
processing operations: morphological reconstruction and euclidean distance
transform. Our results show significant performance improvements on GPUs. The
use of multiple CPUs and GPUs cooperatively attains speedups of 50x and 85x
with respect to single core CPU executions for morphological reconstruction and
euclidean distance transform, respectively.Comment: 37 pages, 16 figure
An Efficient Image Segmentation Approach through Enhanced Watershed Algorithm
Image segmentation is a significant task for image analysis which is at the middle layer of image engineering. The purpose of segmentation is to decompose the image into parts that are meaningful with respect to a particular application. The proposed system is to boost the morphological watershed method for degraded images. Proposed algorithm is based on merging morphological watershed result with enhanced edge detection result obtain on pre processing of degraded images. As a post processing step, to each of the segmented regions obtained, color histogram algorithm is applied, enhancing the overall performance of the watershed algorithm. Keywords – Segmentation, watershed, color histogra
3D + t Morphological Processing: Applications to Embryogenesis Image Analysis
We propose to directly process 3D + t image sequences with mathematical morphology operators, using a new classi?cation of the 3D+t structuring elements. Several methods (?ltering, tracking, segmentation) dedicated to the analysis of 3D + t datasets of zebra?sh embryogenesis are introduced and validated through a synthetic dataset. Then, we illustrate the application of these methods to the analysis of datasets of zebra?sh early development acquired with various microscopy techniques. This processing paradigm produces spatio-temporal coherent results as it bene?ts from the intrinsic redundancy of the temporal dimension, and minimizes the needs for human intervention in semi-automatic algorithms
Automatic Detection of Optic Disc Based on PCA and Mathematical Morphology
The algorithm proposed in this paper allows to automatically segment the optic disc from a fundus image. The goal is to facilitate the early detection of certain pathologies and to fully automate the process so as to avoid specialist intervention. The method proposed for the extraction of the optic disc contour is mainly based on mathematical morphology along with principal component analysis (PCA). It makes use of different
operations such as generalized distance function (GDF), a variant of the watershed transformation, the stochastic watershed, and geodesic transformations. The input of the segmentation method is obtained through PCA. The purpose of using PCA is to achieve the grey-scale image that better represents the original RGB image. The implemented algorithm has been validated on five public databases obtaining promising results. The average values obtained (a Jaccard s and Dice s coefficients of 0.8200 and 0.8932, respectively, an accuracy of 0.9947, and a true positive and false positive fractions of 0.9275 and 0.0036) demonstrate that this method is a robust tool for the automatic segmentation of the optic disc. Moreover, it is fairly reliable since it works properly on databases with a large degree of variability and improves the
results of other state-of-the-art methods.This work was supported in part by the project IMIDTA/2010/47 and in part by projects Consolider-C (SEJ2006-14301/PSIC), "CIBER of Physiopathology of Obesity and Nutrition, an initiative of ISCIII" and Excellence Research Program PROMETEO (Generalitat Valenciana. Conselleria de Educacion, 2008-157).Morales Martínez, S.; Naranjo Ornedo, V.; Angulo Lopez, J.; Alcañiz Raya, ML. (2013). Automatic Detection of Optic Disc Based on PCA and Mathematical Morphology. IEEE Transactions on Medical Imaging. 32(4):786-796. https://doi.org/10.1109/TMI.2013.2238244S78679632
Waterpixels
International audience— Many approaches for image segmentation rely on a 1 first low-level segmentation step, where an image is partitioned 2 into homogeneous regions with enforced regularity and adherence 3 to object boundaries. Methods to generate these superpixels have 4 gained substantial interest in the last few years, but only a few 5 have made it into applications in practice, in particular because 6 the requirements on the processing time are essential but are not 7 met by most of them. Here, we propose waterpixels as a general 8 strategy for generating superpixels which relies on the marker 9 controlled watershed transformation. We introduce a spatially 10 regularized gradient to achieve a tunable tradeoff between the 11 superpixel regularity and the adherence to object boundaries. 12 The complexity of the resulting methods is linear with respect 13 to the number of image pixels. We quantitatively evaluate our 14 approach on the Berkeley segmentation database and compare 15 it against the state-of-the-art
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