On the Decomposition of Cell Clusters

Schmitt, Oliver; Reetz, Stephan

doi:10.1007/s10851-008-0110-5

Cited by 19 publications

(25 citation statements)

References 102 publications

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“…Watershed-based techniques include all forms of watershed-based algorithm, etc., (Tulsani, 2013;Ferro et al, 2013;Hodneland et al, 2013;Schmitt & Reetz, 2009;Schmitt & Hasse, 2009;Špringl, 2009;Abbas & Mohamad, 2015a). Watershed-based approaches have a certain degree of success, but in dense clumps they resulted in over-segmentation, while in some cases, they also suffered from the problem of under-segmentation.…”

Section: Introductionmentioning

confidence: 99%

Clustered Red Blood Cells Splitting via Boundary Analysis in Microscopic Thin Blood Smear Digital Images

Abbas

Abdullah²,

Mohamad

et al. 2015

IJTech

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Clustered Red Blood Cells are observed very frequently in the thin blood smear digital images. Separating clustered Red Blood Cells from the single Red Blood Cells and splitting of clustered Red Blood Cells into single Red Blood Cells is a challenging job in the computer-assisted diagnosis of blood for any disorder in many diseases like Complete Blood Count Test, Anemia, Leukemia and Malaria etc. The mentioned problems are highly laborious in manual microscopy for the hematologists. Many techniques currently existing for the solution suffer from both under-and over-splitting problems when highly complex clusters of Red Blood Cells occur. In addition, the existing techniques are not computationally efficient. In this paper, we address the aforementioned problems, firstly by considering the boundaries of the convex hulls of clustered Red Blood Cells and secondly, by splitting the boundaries according to the number of Red Blood Cells in relation to distance measures. Furthermore, we draw circles using a mid-point circle algorithm at each boundary cleavage to give an illusion of the Red Blood Cells. The test results of the proposed technique on a standard online dataset are presented in two ways. Statistically first of all by achieving an average recall of 0.964 and precision of 0.970 while their F-measure achieved is 0.962 as well as secondly through ground truth data with visual inspections.

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Section: Introductionmentioning

confidence: 99%

Clustered Red Blood Cells Splitting via Boundary Analysis in Microscopic Thin Blood Smear Digital Images

Abbas

Abdullah²,

Mohamad

et al. 2015

IJTech

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show abstract

“…This method initially determines the dominant points on the contour, i.e. its concavities and convexities, based on which an optimal cut or split path is defined between concave points to minimize a cost function (Bai et al, 2009;Schmitt & Reetz, 2009;Yu et al, 2009;Indhumathi et al, 2011). A short review on these methods is given in the following.…”

Section: Introductionmentioning

confidence: 99%

“…A concavity scale space based decomposition method (Schmitt & Reetz, 2009) is proposed for cell cluster decomposition. First of all, concavity scale space based decomposition still uses the simple thresholding method of Otsu (Otsu, 1978) to segment inputting greyscale images and obtains binary images.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to other methods the concavity scale space based decomposition approach has certain advantages regarding the partitioning of clustered regions with emerging complexity. A disadvantage seems to be the huge space of parameters that need to be adjusted (more than 10 parameters) (Schmitt & Reetz, 2009). It is rather elaborate tuning so many parameters for each new image.…”

Section: Introductionmentioning

confidence: 99%

“…For the clump splitting problem, the majority of current methods (Bai et al, 2009;Schmitt & Reetz, 2009;Yu et al, 2009;Indhumathi et al, 2011) are based on analysis of concavities of clumps in the segmented binary images. All of them detect all possible dominant concave points in binary images at one time and then search all possible splitting paths between detected concave points based on some criteria.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dense nuclei segmentation based on graph cut and convexity–concavity analysis

2013

Journal of Microscopy

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SummaryWith the rapid advancement of 3D confocal imaging technology, more and more 3D cellular images will be available. However, robust and automatic extraction of nuclei shape may be hindered by a highly cluttered environment, as for example, in fly eye tissues. In this paper, we present a novel and efficient nuclei segmentation algorithm based on the combination of graph cut and convex shape assumption. The main characteristic of the algorithm is that it segments nuclei foreground using a graph-cut algorithm with our proposed new initialization method and splits overlapping or touching cell nuclei by simple convexity and concavity analysis. Experimental results show that the proposed algorithm can segment complicated nuclei clumps effectively in our fluorescent fruit fly eye images. Evaluation on a public hand-labelled 2D benchmark demonstrates substantial quantitative improvement over other methods. For example, the proposed method achieves a 3.2 Hausdorff distance decrease and a 1.8 decrease in the merged nuclei error per slice.

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Rouleaux red blood cells splitting in microscopic thin blood smear images via local maxima, circles drawing, and mapping with original RBCs

Rehman

Abbas

Saba

et al. 2018

Microscopy Res & Technique

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Splitting the rouleaux RBCs from single RBCs and its further subdivision is a challenging area in computer-assisted diagnosis of blood. This phenomenon is applied in complete blood count, anemia, leukemia, and malaria tests. Several automated techniques are reported in the state of art for this task but face either under or over splitting problems. The current research presents a novel approach to split Rouleaux red blood cells (chains of RBCs) precisely, which are frequently observed in the thin blood smear images. Accordingly, this research address the rouleaux splitting problem in a realistic, efficient and automated way by considering the distance transform and local maxima of the rouleaux RBCs. Rouleaux RBCs are splitted by taking their local maxima as the centres to draw circles by mid-point circle algorithm. The resulting circles are further mapped with single RBC in Rouleaux to preserve its original shape. The results of the proposed approach on standard data set are presented and analyzed statistically by achieving an average recall of 0.059, an average precision of 0.067 and F-measure 0.063 are achieved through ground truth with visual inspection.

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On the Decomposition of Cell Clusters

Cited by 19 publications

References 102 publications

Clustered Red Blood Cells Splitting via Boundary Analysis in Microscopic Thin Blood Smear Digital Images

Clustered Red Blood Cells Splitting via Boundary Analysis in Microscopic Thin Blood Smear Digital Images

Dense nuclei segmentation based on graph cut and convexity–concavity analysis

Rouleaux red blood cells splitting in microscopic thin blood smear images via local maxima, circles drawing, and mapping with original RBCs

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