Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering

Fouad, Shereen; Randell, David A.; Galton, Antony; Mehanna, Hisham; Landini, Gabriel

doi:10.1007/978-3-319-60964-5_67

Cited by 13 publications

(10 citation statements)

References 14 publications

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“…Each segment is described through a combination of characteristics based on statistics of the seismic signals [ 42 , 43 , 44 ], shape [ 45 , 46 , 47 , 48 ], texture including Haralick features (GLCM) and Local Binary Patterns (LBP) [ 49 , 50 , 51 ], histogram of oriented gradients (HOG) [ 52 , 53 ], and Neighborhood information. These characteristics are represented in Figure 6 and are used in the clustering process employing any clustering algorithm.…”

Section: Methodsmentioning

confidence: 99%

Unsupervised Machine Learning Applied to Seismic Interpretation: Towards an Unsupervised Automated Interpretation Tool

Ramos

Figueiredo

Rodríguez

et al. 2021

Sensors

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Seismic interpretation is a fundamental process for hydrocarbon exploration. This activity comprises identifying geological information through the processing and analysis of seismic data represented by different attributes. The interpretation process presents limitations related to its high data volume, own complexity, time consumption, and uncertainties incorporated by the experts’ work. Unsupervised machine learning models, by discovering underlying patterns in the data, can represent a novel approach to provide an accurate interpretation without any reference or label, eliminating the human bias. Therefore, in this work, we propose exploring multiple methodologies based on unsupervised learning algorithms to interpret seismic data. Specifically, two strategies considering classical clustering algorithms and image segmentation methods, combined with feature selection, were evaluated to select the best possible approach. Additionally, the resultant groups of the seismic data were associated with groups obtained from well logs of the same area, producing an interpretation with aggregated lithologic information. The resultant seismic groups correctly represented the main seismic facies and correlated adequately with the groups obtained from the well logs data.

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Section: Methodsmentioning

confidence: 99%

Unsupervised Machine Learning Applied to Seismic Interpretation: Towards an Unsupervised Automated Interpretation Tool

Ramos

Figueiredo

Rodríguez

et al. 2021

Sensors

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“…Super-pixels divide the image into non-overlapping regions wherein similar pixels are grouped together [8]. The boundary of each irregular-shaped super-pixel is according to the edge information in the original image.…”

Section: Introductionmentioning

confidence: 99%

An optimal nuclei segmentation method based on enhanced multi-objective GWO

Sharma

2021

Complex Intell. Syst.

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In breast cancer image analysis, reliable segmentation of the nuclei is still an open-ended research problem. In this paper, a new clustering-based nuclei segmentation method is presented. First, the proposed method pre-processes the histopathology image through SLIC method. Then, a novel variant of multi-objective grey wolf optimizer is employed to group the obtained super-pixels into optimal clusters. Lastly, the optimal cluster with minimum value is segmented as the nuclei region. The experimental results demonstrates that the proposed variant of multi-objective grey wolf algorithm surpasses the existing multi-objective algorithms over ten standard multi-objective benchmark functions belonging to different categories. Particularly, the proposed variant has achieved best fitness value of more than 0.90 on 90% of the considered functions. Further, the nuclei segmentation accuracy of the proposed method is validated on H&E-stained estrogen receptor positive (ER+) breast cancer images. Experimental results illustrates that the proposed method has attained dice-coefficient value of more than 0.52 on 80% of the images. This illustrates that the proposed method is efficient in producing efficacious segmenting over histology images of Breast cancer.

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“…A Random Forest Classifier was used to perform classification. Fouad et al [18] proposed an unsupervised superpixel-based segmentation by using the adaptive consensus clustering method. In [18], a multi-stage segmentation processing with the Simple Linear Iterative Clustering (SLIC) superpixel framework was used to segment the histopathology image into different regions.…”

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

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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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Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering

Cited by 13 publications

References 14 publications

Unsupervised Machine Learning Applied to Seismic Interpretation: Towards an Unsupervised Automated Interpretation Tool

Unsupervised Machine Learning Applied to Seismic Interpretation: Towards an Unsupervised Automated Interpretation Tool

An optimal nuclei segmentation method based on enhanced multi-objective GWO

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

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