An Improved Initialization Based Histogram of K-Mean Clustering Algorithm for Hyperchromatic Nucleus Segmentation in Breast Carcinoma Histopathological Images

Tan, Xiao Jian; Mustafa, Nazahah; Mashor, Mohd Yusoff; Rahman, Khairul Shakir Ab

doi:10.1007/978-981-13-6447-1_67

Cited by 9 publications

(4 citation statements)

References 12 publications

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“…Stage 2 (segmentation and removal of nucleus cells): K-Mean clustering algorithm is implemented to segment the nucleus cell in the Cyan channel. The segmentation method herein is justified on previous work [20], where detection of nucleus cells in histopathology images proven can be done effectively using K-Mean in Cyan channel. Next, the segmented nucleus cells obtained from the K-Mean were used as mask to remove the pixels of the nucleus cells in the RGB input images.…”

Section: Methodsmentioning

confidence: 99%

“…This method is termed as knowledge-based initial centroids selection in the subsequent section. This method can effectively reduce the search space (reflected with a lower number of iteration) and eliminate limitations in the conventional FCM (with random initial centroids generation), such as dead center, center redundancy, and possibility of initial centroid to trap in local minima [19,20]. SNIC is a new method that aims to eliminate nucleus cells while preserve image information and reduce fuzziness of the input image.…”

Section: Introductionmentioning

confidence: 99%

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Spatial neighborhood intensity constraint (SNIC) clustering framework for tumor region in breast histopathology images

Tan¹,

Mustafa²,

Mashor³

et al. 2021

Preprint

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Precise segmentation of tumor regions plays prominent role in the grading of breast carcinoma using the Nottingham Histological Grading (NHG) system. A robust segmentation framework is expected to produce cost-effective, repeatable, and reproducible quantitative outputs. In this study, a spatial neighborhood intensity constraint (SNIC) clustering framework for tumor region in breast histopathology images is presented. The proposed framework consists of five main stages: (1) color normalization, (2) segmentation and removal of nucleus cells, (3) SNIC, (4) FCM with knowledge-based initial centroids selection, and (5) post-processing. The novelty of the proposed framework lies within its simple but powerful in clustering tumor regions precisely in a heterogenous environment. The SNIC is implemented to remove and replace the intensity of the nucleus cells based on the spatial constraints. Also, a knowledge-based initial centroids selection method is implemented to ease the FCM clustering algorithm. Both of these methods are posited to facilitate the clustering stage producing complementary results. To validate the hypothesis, careful justifications are performed to evaluate the role of SNIC and knowledge-based initial centroids selection. These methods are found plausible by achieving positive results in \(Acc\), \(F1\), \(AOM\), and \(CEI\) of 91.2%, 92.1%, 85.7%, and 90.1%, respectively. To further demonstrate the applicability of the proposed framework, four recent works are included for benchmarking purposes. The proposed framework found outperformed these methods with the lowest percentages in over-segmentation and under-segmentation: 8.7% and 6.6%, respectively.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial neighborhood intensity constraint (SNIC) clustering framework for tumor region in breast histopathology images

Tan¹,

Mustafa²,

Mashor³

et al. 2021

Preprint

Self Cite

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“…Stage 1 (learn phase) starts by selecting a non-defective unit as the input image. The die and background are then characterized into two distinct clusters using an improved K-Mean clustering method, namely K-Mean with histogram-based initialization [24]. This method can effectively reduce the computation time of the conventional K-Mean by providing the initial values based on the histogram analysis of the input image.…”

Section: Stage 1: Learn Phasementioning

confidence: 99%

A Simple, Precise, and High-Speed Die Edge Detection Framework for the Semiconductor Industry

Tan

Leow

Cheor

2021

Preprint

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This paper presents an automated high-speed die edge detection method that is meant to be implemented in non-destructive die-level defects inspection in the semiconductor industry. This paper specifically aims to achieve high accuracy (or yield) of greater than 99.95% with stable performance within a short computation time (i.e., less than 30 ms), provided the targeted unit-per-hour (UPH) is at 30,000. To demonstrate the applicability of the proposed method, the proposed method is validated using three production units (i.e., Production units A: 6000 units; Production unit B: 3500 units; Production unit C: 4000 units) and is benchmarked to two common edge detection methods that are widely implemented in the semiconductor industry nowadays (i.e., cross-correlation and normalized cross-correlation methods). The results obtained show that the proposed method is capable to perform die edge detection with promising accuracy and stable performance by achieving 100.0% yield in all three production units and outperformed the benchmarking methods. Also, the overall computation time (considering die edge detection and die rotation) of the proposed method is short, which is approximately 30 ms.

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“…, thresholding ( Jeong et al, 2005 ) and watershed Sharif et al, 2012 ; Ji et al, 2015 ), machine learning (ML) ( e.g. , support vector machine (SVM) ( Marcuzzo et al, 2009 ; Mohammed et al, 2013 ), random forest classifier ( Essa et al, 2015 ), and k-means Mariena & J. Sathiaseelan, 2019 ; Tan et al, 2019 ), and deep learning (DL) to segment the cells with universal measurement indicators ( Coccia, 2020 ). These algorithms, however, are inapplicable to non-experts, particularly biologists, where some stages necessitate difficult parameter tuning for individual cases and complex scripting and user interface for varying pipelines to achieve efficient segmentation results ( Gole et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

A review of open-source image analysis tools for mammalian cell culture: algorithms, features and implementations

Malik

Idris

Toha

et al. 2023

PeerJ Computer Science

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Cell culture is undeniably important for multiple scientific applications, including pharmaceuticals, transplants, and cosmetics. However, cell culture involves multiple manual steps, such as regularly analyzing cell images for their health and morphology. Computer scientists have developed algorithms to automate cell imaging analysis, but they are not widely adopted by biologists, especially those lacking an interactive platform. To address the issue, we compile and review existing open-source cell image processing tools that provide interactive interfaces for management and prediction tasks. We highlight the prediction tools that can detect, segment, and track different mammalian cell morphologies across various image modalities and present a comparison of algorithms and unique features of these tools, whether they work locally or in the cloud. This would guide non-experts to determine which is best suited for their purposes and, developers to acknowledge what is worth further expansion. In addition, we provide a general discussion on potential implementations of the tools for a more extensive scope, which guides the reader to not restrict them to prediction tasks only. Finally, we conclude the article by stating new considerations for the development of interactive cell imaging tools and suggesting new directions for future research.

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An Improved Initialization Based Histogram of K-Mean Clustering Algorithm for Hyperchromatic Nucleus Segmentation in Breast Carcinoma Histopathological Images

Cited by 9 publications

References 12 publications

Spatial neighborhood intensity constraint (SNIC) clustering framework for tumor region in breast histopathology images

Spatial neighborhood intensity constraint (SNIC) clustering framework for tumor region in breast histopathology images

A Simple, Precise, and High-Speed Die Edge Detection Framework for the Semiconductor Industry

A review of open-source image analysis tools for mammalian cell culture: algorithms, features and implementations

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