Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
The nature of statistical learning theory
The nature of statistical learning theory
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Cervical Cancer Detection Using Colposcopic Images: a Temporal Approach
ENC '05 Proceedings of the Sixth Mexican International Conference on Computer Science
Nucleus and cytoplast contour detector of cervical smear image
Pattern Recognition Letters
Classification Cervical Cancer Using Histology Images
ICCEA '10 Proceedings of the 2010 Second International Conference on Computer Engineering and Applications - Volume 01
Semi-automatic Cervical Cancer Segmentation Using Active Contours without Edges
SITIS '09 Proceedings of the 2009 Fifth International Conference on Signal Image Technology and Internet Based Systems
Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images
Pattern Recognition Letters
Nucleus and cytoplast contour detector from a cervical smear image
Expert Systems with Applications: An International Journal
White blood cell segmentation and classification in microscopic bone marrow images
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Computer Methods and Programs in Biomedicine
Histology image analysis for carcinoma detection and grading
Computer Methods and Programs in Biomedicine
Unsupervised segmentation and classification of cervical cell images
Pattern Recognition
A record linkage process of a cervical cancer screening database
Computer Methods and Programs in Biomedicine
Debris removal in Pap-smear images
Computer Methods and Programs in Biomedicine
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Cervical cancer is one of the leading causes of cancer death in females worldwide. The disease can be cured if the patient is diagnosed in the pre-cancerous lesion stage or earlier. A common physical examination technique widely used in the screening is Papanicolaou test or Pap test. In this research, a method for automatic cervical cancer cell segmentation and classification is proposed. A single-cell image is segmented into nucleus, cytoplasm, and background, using the fuzzy C-means (FCM) clustering technique. Four cell classes in the ERUDIT and LCH datasets, i.e., normal, low grade squamous intraepithelial lesion (LSIL), high grade squamous intraepithelial lesion (HSIL), and squamous cell carcinoma (SCC), are considered. The 2-class problem can be achieved by grouping the last 3 classes as one abnormal class. Whereas, the Herlev dataset consists of 7 cell classes, i.e., superficial squamous, intermediate squamous, columnar, mild dysplasia, moderate dysplasia, severe dysplasia, and carcinoma in situ. These 7 classes can also be grouped to form a 2-class problem. These 3 datasets were tested on 5 classifiers including Bayesian classifier, linear discriminant analysis (LDA), K-nearest neighbor (KNN), artificial neural networks (ANN), and support vector machine (SVM). For the ERUDIT dataset, ANN with 5 nucleus-based features yielded the accuracies of 96.20% and 97.83% on the 4-class and 2-class problems, respectively. For the Herlev dataset, ANN with 9 cell-based features yielded the accuracies of 93.78% and 99.27% for the 7-class and 2-class problems, respectively. For the LCH dataset, ANN with 9 cell-based features yielded the accuracies of 95.00% and 97.00% for the 4-class and 2-class problems, respectively. The segmentation and classification performances of the proposed method were compared with that of the hard C-means clustering and watershed technique. The results show that the proposed automatic approach yields very good performance and is better than its counterparts.