1,362 research outputs found
Automated segmentation of tissue images for computerized IHC analysis
This paper presents two automated methods for the segmentation ofimmunohistochemical tissue images that overcome the limitations of themanual approach aswell as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologie
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
A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images
Cataloged from PDF version of article.Computer-based imaging systems are becoming important tools for quantitative assessment
of peripheral blood and bone marrow samples to help experts diagnose blood disorders
such as acute leukemia. These systems generally initiate a segmentation stage
where white blood cells are separated from the background and other nonsalient objects.
As the success of such imaging systems mainly depends on the accuracy of this stage,
studies attach great importance for developing accurate segmentation algorithms.
Although previous studies give promising results for segmentation of sparsely distributed
normal white blood cells, only a few of them focus on segmenting touching and overlapping
cell clusters, which is usually the case when leukemic cells are present. In this article,
we present a new algorithm for segmentation of both normal and leukemic cells in
peripheral blood and bone marrow images. In this algorithm, we propose to model color
and shape characteristics of white blood cells by defining two transformations and introduce
an efficient use of these transformations in a marker-controlled watershed algorithm.
Particularly, these domain specific characteristics are used to identify markers and
define the marking function of the watershed algorithm as well as to eliminate false white
blood cells in a postprocessing step. Working on 650 white blood cells in peripheral
blood and bone marrow images, our experiments reveal that the proposed algorithm
improves the segmentation performance compared with its counterparts, leading to high accuracies for both sparsely distributed normal white blood cells and dense leukemic cell clusters. (C) 2014 International Society for Advancement of Cytometr
Effects of Noninhibitory Serpin Maspin on the Actin Cytoskeleton: A Quantitative Image Modeling Approach
Recent developments in quantitative image analysis allow us to interrogate confocal microscopy images to answer biological questions. Clumped and layered cell nuclei and cytoplasm in confocal images challenges the ability to identify subcellular compartments. To date, there is no perfect image analysis method to identify cytoskeletal changes in confocal images. Here, we present a multidisciplinary study where an image analysis model was developed to allow quantitative measurements of changes in the cytoskeleton of cells with different maspin exposure. Maspin, a noninhibitory serpin influences cell migration, adhesion, invasion, proliferation, and apoptosis in ways that are consistent with its identification as a tumor metastasis suppressor. Using different cell types, we tested the hypothesis that reduction in cell migration by maspin would be reflected in the architecture of the actin cytoskeleton. A hybrid marker-controlled watershed segmentation technique was used to segment the nuclei, cytoplasm, and ruffling regions before measuring cytoskeletal changes. This was informed by immunohistochemical staining of cells transfected stably or transiently with maspin proteins, or with added bioactive peptides or protein. Image analysis results showed that the effects of maspin were mirrored by effects on cell architecture, in a way that could be described quantitatively
Image Processing Methods for Automatic Cell Counting In Vivo or In Situ Using 3D Confocal Microscopy
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