Nucleus Classification and Recognition of Uterine Cervical Pap-Smears Using FCM Clustering Algorithm

Kim, Kwang-Baek; Kim, Sungshin; Kim, Gwang-Ha

doi:10.1007/978-3-540-71629-7_33

Cited by 3 publications

(4 citation statements)

References 3 publications

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“…This method showed fast optimization process but the accuracy depends greatly on closeness of initially detected nucleus center point to the actual one. Clustering based: Kim et al [79] suggested a Fuzzy C-Means (FCM) based segmentation method on uterine cervical images in HSI colour space. A patch based FCM clustering technique was proposed by Chankong et al [23] for segmentation of nucleus, cytoplasm and background.…”

Section: Differential Characteristics Associated With Cytomorphology ...mentioning

confidence: 99%

“…Malignant or cancerous are further classified into different categories based on their stage.A single classifier or combination or ensemble of different classifiers are used. Among different classifiers binary classification method [57], Artificial Neural Network (ANN) and its modifications [103,102,23] Support vector machine (SVM) [201,121], Fuzzy C Means (FCM) [79,127] etc. are popularly used.…”

Section: Classificationmentioning

confidence: 99%

“…They obtained better performance after using SVM based screening with smaller number of features.Monteagudo et al [121] proposed a combination of SVM and waterfall algorithm also classify cancerous and normal specimens. Fuzzy C-Means(FCM): Kim et al [79] suggested FCM based nucleus recognition techniques to distinguish normal and abnormal cells in HSI images. Different nucleus specific information such as circumference of nucleus, different ratios from nucleus and cytoplasm, degree of roundness, etc.…”

Section: Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Cytology Image Analysis Techniques Towards Automation: Systematically Revisited

Mitra,

Das,

Dey

et al. 2020

Preprint

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Cytology is the branch of pathology which deals with the microscopic examination of cells for diagnosis of carcinoma or inflammatory conditions. Automation in cytology started in the early 1950s with the aim to reduce manual efforts in diagnosis of cancer. The inflush of intelligent technological units with high computational power and improved specimen collection techniques helped to achieve its technological heights. In the present survey, we focus on such image processing techniques which put steps forward towards the automation of cytology. We take a short tour to 17 types of cytology and explore various segmentation and/or classification techniques which evolved during last three decades boosting the concept of automation in cytology. It is observed, that most of the works are aligned towards three types of cytology: Cervical, Breast and Lung, which are discussed elaborately in this paper. The user-end systems developed during that period are summarized to comprehend the overall growth in the respective domains. To be precise, we discuss the diversity of the state-of-the-art methodologies, their challenges to provide prolific and competent future research directions inbringing the cytology-based commercial systems into the mainstream.

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Section: Differential Characteristics Associated With Cytomorphology ...mentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Cytology Image Analysis Techniques Towards Automation: Systematically Revisited

Mitra,

Das,

Dey

et al. 2020

Preprint

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show abstract

“…In order to improve the segmentation, the identified areas are classified under the Fisher algorithm (González and Goods, 2002). In (Kim et al, 2007) the HSI model is used for the nucleus region extraction from an image of uterine cervical cytodiagnosis. Firstly, a preprocessing step to eliminate noise in the image is made.…”

Section: Introductionmentioning

confidence: 99%

A multiscale algorithm for nuclei extraction in pap smear images

Gonzalez

Aguirre

2016

Expert Systems with Applications

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Cytology Image Analysis Techniques Toward Automation

et al. 2021

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Cytology is a branch of pathology that deals with the microscopic examination of cells for diagnosis of carcinoma or inflammatory conditions. In the present work, the term cytology is used to indicate solid organ cytology. Automation in cytology started in the early 1950s with an aim to reduce manual efforts in the diagnosis of cancer. The influx of intelligent systems with high computational power and improved specimen collection techniques helped to achieve technological heights in the cytology automation process. In the present survey, we focus on image analysis techniques paving the way to automation in cytology. We take a short tour of 17 types of solid organ cytology to explore various segmentation and/or classification techniques that evolved during the past three decades to automate cytology image analysis. It is observed that most of the works are aligned toward three types of cytology: Cervical, Breast, and Respiratory tract cytology. These are discussed elaborately in the article. Commercial systems developed during the period are also summarized to comprehend the overall growth in respective domains. Finally, we discuss different state-of-the-art methods and related challenges to provide prolific and competent future research directions in bringing cytology-based commercial systems into the mainstream.

show abstract

Nucleus Classification and Recognition of Uterine Cervical Pap-Smears Using FCM Clustering Algorithm

Cited by 3 publications

References 3 publications

Cytology Image Analysis Techniques Towards Automation: Systematically Revisited

Cytology Image Analysis Techniques Towards Automation: Systematically Revisited

A multiscale algorithm for nuclei extraction in pap smear images

Cytology Image Analysis Techniques Toward Automation

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