Segmentation of overlapping/aggregating nuclei cells in biological images

Cloppet, Florence; Boucher, Arnaud

doi:10.1109/icpr.2008.4761451

Cited by 48 publications

(34 citation statements)

References 10 publications

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“…Therefore Watershed transform [17] [20] is used in our work, which uses the connectivity in the given image pixel. The distance transform calculates the difference between the pixel and the nearest non-zero pixel.…”

Section: Watershed Thresholdmentioning

confidence: 99%

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A Novel Method for Automatic Detection of Malaria Parasite Stage in Microscopic Blood Image

Charpe¹,

Bairagi²,

Desarda³

et al. 2015

IJCA

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Malaria is caused due to the mosquito bite hence the parasite enter into blood through the saliva of the mosquito. The malaria parasite directly infects the red blood cells, therefore to design an automatic detection system, the red blood cells should be segmented from the artifacts and background in a microscopic image. Here in this paper watershed transform is used with the distance transform which separates even the overlapped red blood cells more efficiently, which are useful for the diagnosis of parasite and for the parasitemia too. The result shows improvement in diagnostic accuracy of detection of the parasite in Red Blood Cells and also describing the life cycle stage of the parasite. The accuracy, sensitivity and specificity achieved were as 97.7%, 97.4% and 97.7% respectively.

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Section: Watershed Thresholdmentioning

confidence: 99%

“…Statistical values of the image on the basis of the histogram are also called as the histogram second order statistical features [17] [18] are calculated.…”

Section: 331statistical Featuresmentioning

confidence: 99%

A Novel Method for Automatic Detection of Malaria Parasite Stage in Microscopic Blood Image

Charpe¹,

Bairagi²,

Desarda³

et al. 2015

IJCA

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show abstract

“…The literature [11] use fungi spore images and use a combination of graph segmentation technique and thresholding algorithm for cell segmentation and use corner detection algorithm to identify the touching cells. The literature [12] uses the watershed algorithm to segment overlapping and aggregating cells and uses significant concavity points to identify the overlapping/aggregating cells of normal/pathological nuclei cells. In case of overlapping nuclei, the grey level intensity is far higher in the area of overlapping than the mean intensity of the connected component [12].…”

Section: Introductionmentioning

confidence: 99%

Overlapping White Blood Cell Segmentation And Counting On Microscopic Blood Cell Images

Fatichah

Purwitasari

Hariadi

et al. 2014

International Journal on Smart Sensing and Intelligent Systems

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“…For isolated nuclei this is rather straightforward, but for touching nuclei this requires an extra step to detect each individual nucleus. This is mainly done by assuming that the detected segments should have a convex shape [2,[9][10][11]. These methods are non optimal since both steps are independent: the second step can only use the result of the first step, instead of all information available, e.g.…”

Section: Introductionmentioning

confidence: 99%

Cell Nuclei Detection Using Globally Optimal Active Contours with Shape Prior

Vylder

Aelterman

Vandewoestyne

et al. 2012

Advances in Visual Computing

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Abstract. Cell nuclei detection in fluorescent microscopic images is an important and time consuming task for a wide range of biological applications. Blur, clutter, bleed through and partial occlusion of nuclei make this a challenging task for automated detection of individual nuclei using image analysis. This paper proposes a novel and robust detection method based on the active contour framework. The method exploits prior knowledge of the nucleus shape in order to better detect individual nuclei. The method is formulated as the optimization of a convex energy function. The proposed method shows accurate detection results even for clusters of nuclei where state of the art methods fail.

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Segmentation of overlapping/aggregating nuclei cells in biological images

Abstract: Abstract

Cited by 48 publications

References 10 publications

A Novel Method for Automatic Detection of Malaria Parasite Stage in Microscopic Blood Image

A Novel Method for Automatic Detection of Malaria Parasite Stage in Microscopic Blood Image

Overlapping White Blood Cell Segmentation And Counting On Microscopic Blood Cell Images

Cell Nuclei Detection Using Globally Optimal Active Contours with Shape Prior

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