Iterative thresholding for segmentation of cells from noisy images

Wu, Haishan; Berba, J.; Gil, Joan

doi:10.1111/j.1365-2818.2000.00653.x

Cited by 32 publications

(25 citation statements)

References 25 publications

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“…Those methods can be broadly classified as edge-based [15], region-based [16], threshold-based [17], and watershed-based [18,19] segmentation schemes. Wu et al [15] stated that cell boundaries are not sharp enough to perform edge-based segmentation in leukocyte images.…”

Section: Literature Surveymentioning

confidence: 99%

Unsupervised Leukocyte Image Segmentation Using Rough Fuzzy Clustering

Mohapatra

Patra

Kumar

2012

ISRN Artificial Intelligence

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The segmentation of leukocytes and their components acts as the foundation for all automated image-based hematological disease recognition systems. Perfection in image segmentation is a necessary condition for improving the diagnostic accuracy in automated cytology. Since the diagnostic information content of the segmented images is plentiful, suitable segmentation routines need to be developed for better disease recognition. Clustering is an essential image segmentation procedure which segments an image into desired regions. A judicious integration of rough sets and fuzzy sets is suitably employed towards leukocyte segmentation in a clustering framework. In this study, the goodness of fuzzy sets and rough sets is suitably integrated to achieve improved segmentation performance. The membership concept of fuzzy sets endow is efficient handling of overlapping partitions, and the rough sets provide a reasonable solution to deal with uncertainty, vagueness, and incompleteness in data. Such synergistic combination gives the proposed scheme an edge over standard cluster-based segmentation techniques, that is, K-means, K-medoid, fuzzy c-means, and rough c-means. Comparative analysis reveals that the hybrid rough fuzzy c-means algorithm is robust in segmenting stained blood microscopic images. The accomplished segmented nucleus and cytoplasm of a leukocyte can be used for feature extraction which leads to automated leukemia detection.

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Section: Literature Surveymentioning

confidence: 99%

Unsupervised Leukocyte Image Segmentation Using Rough Fuzzy Clustering

Mohapatra

Patra

Kumar

2012

ISRN Artificial Intelligence

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“…However, cell segmentation is still a problem due to the complexities of cell structures resulting from inconsistent staining, poor contrast, and overlapping cells [1]. Global approaches such as thresholding, clustering, and histogram-based methods lead to unsatisfactory results due to the variable staining existing even within a single cell [2]. Such problems can be partially avoided using local information by exploiting local relative changes to find the boundaries between different cell structures.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Cervical Cell Images

Kale

Aksoy

2010

2010 20th International Conference on Pattern Recognition

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Abstract-The key step of a computer-assisted screening system that aims early diagnosis of cervical cancer is the accurate segmentation of cells. In this paper, we propose a twophase approach to cell segmentation in Pap smear test images with the challenges of inconsistent staining, poor contrast, and overlapping cells. The first phase consists of segmenting an image by a non-parametric hierarchical segmentation algorithm that uses spectral and shape information as well as the gradient information. The second phase aims to obtain nucleus regions and cytoplasm areas by classifying the segments resulting from the first phase based on their spectral and shape features. Experiments using two data sets show that our method performs well for images containing both a single cell and many overlapping cells.

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“…They include watershed [5], [6], [7], region-based [8] and threshold-based methods [9]. Application of active contour has been widely investigated for cell segmentation [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Cell Microscopic Segmentation with Spiking Neuron Networks

Meftah¹,

Lézoray²,

Lecluse³

et al. 2010

Artificial Neural Networks – ICANN 2010

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Abstract. Spiking Neuron Networks (SNNs) overcome the computational power of neural networks made of thresholds or sigmoidal units. Indeed, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. In this paper, we present how SNN can be applied with efficacy for cell microscopic image segmentation. Results obtained confirm the validity of the approach. The strategy is performed on cytological color images. Quantitative measures are used to evaluate the resulting segmentations.

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Iterative thresholding for segmentation of cells from noisy images

Cited by 32 publications

References 25 publications

Unsupervised Leukocyte Image Segmentation Using Rough Fuzzy Clustering

Unsupervised Leukocyte Image Segmentation Using Rough Fuzzy Clustering

Segmentation of Cervical Cell Images

Cell Microscopic Segmentation with Spiking Neuron Networks

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