Classification of cervical cell nuclei using morphological segmentation and textural feature extraction

Walker, Ross F.; Jackway, Paul; Lovell, Brian C.; Longstaff, I.D.

doi:10.1109/anziis.1994.396977

Cited by 32 publications

(23 citation statements)

References 7 publications

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“…For example, Walker et al [21] used a quadratic Gaussian classifier with co-occurrence texture features extracted from the nucleus pixels. Chou and Shapiro [22] classified cells using more than 300 features with a hierarchical multiple classifier algorithm.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised segmentation and classification of cervical cell images

Genctav

Aksoy

Önder

2012

Pattern Recognition

232

176

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a b s t r a c tThe Pap smear test is a manual screening procedure that is used to detect precancerous changes in cervical cells based on color and shape properties of their nuclei and cytoplasms. Automating this procedure is still an open problem due to the complexities of cell structures. In this paper, we propose an unsupervised approach for the segmentation and classification of cervical cells. The segmentation process involves automatic thresholding to separate the cell regions from the background, a multi-scale hierarchical segmentation algorithm to partition these regions based on homogeneity and circularity, and a binary classifier to finalize the separation of nuclei from cytoplasm within the cell regions. Classification is posed as a grouping problem by ranking the cells based on their feature characteristics modeling abnormality degrees. The proposed procedure constructs a tree using hierarchical clustering, and then arranges the cells in a linear order by using an optimal leaf ordering algorithm that maximizes the similarity of adjacent leaves without any requirement for training examples or parameter adjustment. Performance evaluation using two data sets show the effectiveness of the proposed approach in images having inconsistent staining, poor contrast, and overlapping cells.

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Section: Introductionmentioning

confidence: 99%

Unsupervised segmentation and classification of cervical cell images

Genctav

Aksoy

Önder

2012

Pattern Recognition

232

176

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“…This data was gathered by Ross Walker as part of a study into the use of nuclear texture analysis for the diagnosis of cervical cancer (9) . The data set consisted of 117 segmented images of normal and abnormal cervical cell nuclei.…”

Section: Cervical Cell Nuclear Texturementioning

confidence: 99%

“…Cervical cell nuclear texture analysis (Texture) (9) 2. Post-operative bleeding after cardiopulomonary bypass surgery (Heart) (10) 3.…”

Section: The Datamentioning

confidence: 99%

The use of the area under the ROC curve in the evaluation of machine learning algorithms

Bradley

1997

Pattern Recognition

5,752

2,981

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SummaryIn this paper we i n vestigate the use of receiver operating characteristic (ROC) curve f o r the evaluation of machine learning algorithms. In particular, we i n vestigate the use of the area under the ROC curve ( A UC) as a measure of classi er performance. The machine learning algorithms used are chosen to be representative of those in common use: two decision trees (C4.5 and Multiscale Classi er) two n e u r a l n e t works (Perceptron and Multi-layer Perceptron) and two statistical methods (K-Nearest Neighbours and a Quadratic Discriminant F unction).The evaluation is done using six, \real world," medical diagnostics data sets that contain a varying numbers of inputs and samples, but are primarily continuous input, binary classi cation problems. We i d e n tify three forms of bias that can a ect comparisons of this type (estimation, selection, and expert bias) and detail the methods used to avoid them. We compare and discuss the use of AUC with the conventional measure of classi er performance, overall accuracy (the probability of a correct response). It is found that AUC exhibits a number of desirable properties when compared to overall accuracy: increased sensitivity in Analysis of Variance (ANOVA) tests a standard error that decreased as both AUC and the number of test samples increased decision threshold independent invariant t o a priori class probabilities and it gives an indication of the amount o f \ w ork done" by a classi cation scheme, giving low scores to both random and \one class only" classi ers.It has been known for some time that AUC actually represents the probability that a randomly chosen positive example is correctly rated (ranked) with greater suspicion than a randomly chosen negative example. Moreover, this probability of correct ranking is the same quantity estimated by the non-parametric Wilcoxon statistic. We use this equivalence to show that the standard deviation of AUC, estimated using 10 fold cross validation, is a reliable estimator of the standard error estimated using the Wilcoxon test. The paper concludes with the recommendation that AUC be used in preference to overall accuracy when \single number" evaluation of machine learning algorithms is required. Draft Only 3 AbstractIn this paper we i n vestigate the use of the area under the receiver operating characteristic (ROC) curve ( A UC) as a performance measure for machine learning algorithms.As a case study we e v aluate six machine learning algorithms (C4.5, Multiscale Classi er, Perceptron, Multi-layer Perceptron, K-Nearest Neighbours, and a Quadratic Discriminant F unction) on six \real world" medical diagnostics data sets. We compare and discuss the use of AUC to the more conventional overall accuracy and nd that AUC exhibits a number of desirable properties when compared to overall accuracy: increased sensitivity in Analysis of Variance (ANOVA) tests a standard error that decreased as both AUC and the number of test samples increased decision threshold independent and it is invariant t o a priori class proba...

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“…An expert system CYTOPATH is designed through meta model and based on the priori information squamous lesions are decided and severity is concluded [11], Squamous Intraepithelial Lesions (SIL) is identified to screen the pap smear true positives. [12] Preliminary results for the classification of Pap Smear cell nuclei with textural features, using Gray Level Co-occurrence Matrix (GLCM) is performed [13]. A composite classification scheme implemented by combining several classifiers with distinctly different design methodologies.…”

Section: Related Workmentioning

confidence: 99%