Unsupervised Segmentation of Cervical Cell Images Using Gaussian Mixture Model

Ragothaman, Srikanth; Narasimhan, Sridharakumar; Basavaraj, Madivala G.; Dewar, Rajan

doi:10.1109/cvprw.2016.173

Cited by 38 publications

(14 citation statements)

References 16 publications

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“…After obtaining clustering configuration via PRM with different cluster numbers, one can calculate the corresponding AIC values, where the cluster number k * corresponding to the smallest AIC value is selected for each driver. AIC is usually adopted to determine "optimal" cluster number for clustering problems where data have the same dimension [25]. In this paper, vehicle trajectories in various clustering configurations are generally with variable lengths and therefore AIC value for each clustering configuration cannot be directly calculated.…”

Section: B Optimal Cluster Numbermentioning

confidence: 99%

Trajectory Clustering Aided Personalized Driver Intention Prediction for Intelligent Vehicles

Liu

2019

IEEE Trans. Ind. Inf.

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Early driver intention prediction plays a significant role in intelligent vehicles. Drivers exhibit various driving characteristics impairing the performance of conventional algorithms using all drivers' data indiscriminatingly. This paper develops a personalized driver intention prediction system at unsignalized T intersections by seamlessly integrating clustering and classification. Polynomial regression mixture (PRM) clustering and Akaike's information criterion are applied to individual drivers trajectories for learning in-depth driving behaviors. Then, various classifiers are evaluated to link low-level vehicle states to high-level driving behaviors. CART classifier with Bayesian optimization excels others in accuracy and computation. The proposed system is validated by a real-world driving dataset. Comparative experimental results indicate that PRM clustering can discover more in-depth driving behaviors than manually defined maneuver due to its fine ability in accounting for both spatial and temporal information; the proposed framework integrating PRM clustering and CART classification provides promising intention prediction performance and is adaptive to different drivers.

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Section: B Optimal Cluster Numbermentioning

confidence: 99%

Trajectory Clustering Aided Personalized Driver Intention Prediction for Intelligent Vehicles

Liu

2019

IEEE Trans. Ind. Inf.

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“…In case of cervical cancer cell images, a study by Zhang et al [26] used various machine learning algorithms and matched their segmentation refinement with an artifact-nucleus classifier, for which random forest has revealed the best output. Along with other robust refinement methods, supervised and unsupervised methods were used to distinguish image patches or superpixels from extracted elements, such as Adaboost detectors [27], support vector machine (SVM) [28] or Gauussian mixture models [29]. In a study by Zhao et al [30] a novel superpixel-based Markov random field (MRF) segmentation was also implemented for non-overlapping cells.…”

Section: Related Workmentioning

confidence: 99%

Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods

Ijaz

Attique

Son

2020

Sensors

183

124

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Globally, cervical cancer remains as the foremost prevailing cancer in females. Hence, it is necessary to distinguish the importance of risk factors of cervical cancer to classify potential patients. The present work proposes a cervical cancer prediction model (CCPM) that offers early prediction of cervical cancer using risk factors as inputs. The CCPM first removes outliers by using outlier detection methods such as density-based spatial clustering of applications with noise (DBSCAN) and isolation forest (iForest) and by increasing the number of cases in the dataset in a balanced way, for example, through synthetic minority over-sampling technique (SMOTE) and SMOTE with Tomek link (SMOTETomek). Finally, it employs random forest (RF) as a classifier. Thus, CCPM lies on four scenarios: (1) DBSCAN + SMOTETomek + RF, (2) DBSCAN + SMOTE+ RF, (3) iForest + SMOTETomek + RF, and (4) iForest + SMOTE + RF. A dataset of 858 potential patients was used to validate the performance of the proposed method. We found that combinations of iForest with SMOTE and iForest with SMOTETomek provided better performances than those of DBSCAN with SMOTE and DBSCAN with SMOTETomek. We also observed that RF performed the best among several popular machine learning classifiers. Furthermore, the proposed CCPM showed better accuracy than previously proposed methods for forecasting cervical cancer. In addition, a mobile application that can collect cervical cancer risk factors data and provides results from CCPM is developed for instant and proper action at the initial stage of cervical cancer.

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“…Supervised and unsupervised methods were jointly used with other robust refinement techniques to classify image patches or superpixels from extracted features. Examples include modifier Adaboost detectors [115], SVM [88] or Gaussian mixture models [116]. Additionally, a novel superpixel-based Markov random field (MRF) segmentation for non-overlapping cells was introduced in a study by Zhao et al [87].…”

Section: Literature Review On Computational Approaches For Cervicamentioning

confidence: 99%

A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification

Conceição

Braga

Rosado

et al. 2019

IJMS

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Cervical cancer is the one of the most common cancers in women worldwide, affecting around 570,000 new patients each year. Although there have been great improvements over the years, current screening procedures can still suffer from long and tedious workflows and ambiguities. The increasing interest in the development of computer-aided solutions for cervical cancer screening is to aid with these common practical difficulties, which are especially frequent in the low-income countries where most deaths caused by cervical cancer occur. In this review, an overview of the disease and its current screening procedures is firstly introduced. Furthermore, an in-depth analysis of the most relevant computational methods available on the literature for cervical cells analysis is presented. Particularly, this work focuses on topics related to automated quality assessment, segmentation and classification, including an extensive literature review and respective critical discussion. Since the major goal of this timely review is to support the development of new automated tools that can facilitate cervical screening procedures, this work also provides some considerations regarding the next generation of computer-aided diagnosis systems and future research directions.

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Unsupervised Segmentation of Cervical Cell Images Using Gaussian Mixture Model

Cited by 38 publications

References 16 publications

Trajectory Clustering Aided Personalized Driver Intention Prediction for Intelligent Vehicles

Trajectory Clustering Aided Personalized Driver Intention Prediction for Intelligent Vehicles

Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods

A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification

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