A rule-based approach for robust clump splitting

Kumar, Sameer; Ong, Sim Heng; Ranganath, S.; Ong, Tan Ching; Chew, Fook Tim

doi:10.1016/j.patcog.2005.11.014

Cited by 114 publications

(95 citation statements)

References 17 publications

(40 reference statements)

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“…Using our implementation of the concavity-based segmentation method of Kumar et al [13], we find very poor performance (0.03 ± 0.08). Referring to the example segmentation shown in Fig.…”

Section: Concavity-based Segmentation Of Nucleimentioning

confidence: 84%

“…Motivated by observations that shape is a large factor in humans' ability to properly discriminate individual nuclei, we use the method in [13] which uses concavities as the basis for segmentation lines.…”

Section: Concavity-based Segmentation Of Nucleimentioning

confidence: 99%

“…The concavity-based (CB) segmentation method of Kumar et al [13] uses a rule-based approach for the segmentation of binary objects, beginning with a measure of concavity depth. In this method, the segmentation of one binary blob into multiple constituent objects is accomplished with the use of a "split line."…”

Section: Concavity-based Segmentation Of Nucleimentioning

confidence: 99%

See 2 more Smart Citations

Use of imperfectly segmented nuclei in the classification of histopathology images of breast cancer

Boucheron

Manjunath

Harvey

2010

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

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Many features used in the analysis of pathology imagery are inspired by grading features defined by clinical pathologists as important for diagnosis and characterization. A large majority of these features are features of cell nuclei; as such, there is often the desire to segment the imagery into individual nuclei prior to feature extraction and further analysis. In this paper we present an analysis of the utility of imperfectly segmented cell nuclei for classification of H&E stained histopathology imagery of breast tissue. We show the object-and image-level classification performance using these imperfectly segmented nuclei in a benign versus malignant decision. Results indicate that very good classification accuracies can be achieved with imperfectly segmented nuclei and further that perfect nuclei segmentation does not necessarily guarantee better classification accuracy.

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“…Using our implementation of the concavity-based segmentation method of Kumar et al [13], we find very poor performance (0.03 ± 0.08). Referring to the example segmentation shown in Fig.…”

Section: Concavity-based Segmentation Of Nucleimentioning

confidence: 84%

Section: Concavity-based Segmentation Of Nucleimentioning

confidence: 99%

See 1 more Smart Citation

Use of imperfectly segmented nuclei in the classification of histopathology images of breast cancer

Boucheron

Manjunath

Harvey

2010

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

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show abstract

“…8, it arises a problem that even the overlap of two RBC recognized as one RBC. Then, the clump split method [13,14,15] is used to split clumps of two or more RBCs into constituent cells. For the contents of this process, the process is shown through the following in Fig.…”

Section: Clump Splitmentioning

confidence: 99%

The Development of Evaluation Algorithm for Blood Infection Degree

Kim

Kwac

Kim

2017

Wireless Pers Commun

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In this paper, a series of processes are performed to assess the degree of infection in a subject's blood infected with malaria. This process improves the existing testing method's inefficiencies to assess the results obtained of the infected blood. Image processing algorithm was constructed to establish a standardized test procedure as a way to process the information. The algorithm is built to process the images obtained from the microscopic image of the infected blood and detect the extent of Plasmodium infection which is the cause of Malaria Disease. The images obtained from the infected red blood cells and uninfected red blood cells are processed using the image processing algorithm to distinguish the number of cells infected by malaria in the infected blood. A quantitative analysis is carried out using the results to evaluate the extent of the infection in the blood cells. To establish the relationship between the number of infected and uninfected cells, the algorithm was constructed based on color image processing through HSV color space and morphological image processing techniques. The key configuration for the proposed algorithm is the morphological image processing technique which clearly divide the red blood cells in the micrograph based on the extent of penetration of plasmodium in the blood cells using the clamp splitting method and categorize the infected blood cells.

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“…A group of these algorithms have used ellipse fitting (2), Gaussian mixtures (6), and physical deformable models (7) to decompose clustered cell nuclei based on their roundness. Another group have proposed to find concave points on cluster boundaries and split the cluster from these points (8,9). As they mainly model morphological properties of nuclei, these algorithms are susceptible to undersegmentations when cells form big clusters.…”

mentioning

confidence: 99%

Iterative h‐minima‐based marker‐controlled watershed for cell nucleus segmentation

et al. 2016

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Automated microscopy imaging systems facilitate high-throughput screening in molecular cellular biology research. The first step of these systems is cell nucleus segmentation, which has a great impact on the success of the overall system. The markercontrolled watershed is a technique commonly used by the previous studies for nucleus segmentation. These studies define their markers finding regional minima on the intensity/gradient and/or distance transform maps. They typically use the h-minima transform beforehand to suppress noise on these maps. The selection of the h value is critical; unnecessarily small values do not sufficiently suppress the noise, resulting in false and oversegmented markers, and unnecessarily large ones suppress too many pixels, causing missing and undersegmented markers. Because cell nuclei show different characteristics within an image, the same h value may not work to define correct markers for all the nuclei. To address this issue, in this work, we propose a new watershed algorithm that iteratively identifies its markers, considering a set of different h values. In each iteration, the proposed algorithm defines a set of candidates using a particular h value and selects the markers from those candidates provided that they fulfill the size requirement. Working with widefield fluorescence microscopy images, our experiments reveal that the use of multiple h values in our iterative algorithm leads to better segmentation results, compared to its counterparts. V C 2016 International Society for Advancement of Cytometry

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A rule-based approach for robust clump splitting

Cited by 114 publications

References 17 publications

Use of imperfectly segmented nuclei in the classification of histopathology images of breast cancer

Use of imperfectly segmented nuclei in the classification of histopathology images of breast cancer

The Development of Evaluation Algorithm for Blood Infection Degree

Iterative h‐minima‐based marker‐controlled watershed for cell nucleus segmentation

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