Segmentation and Classification of Cervical Cytology Images Using Morphological and Statistical Operations

Sivaprakasam, S. Anantha; Naganathan, E. R.

doi:10.21917/ijivp.2017.0208

Cited by 9 publications

(2 citation statements)

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“…Early methods proposed to achieve the automatic segmentation and classification of abnormal cervical cells based on isolated cell without overlapping images. The most of them used one or multiple techniques including thresholding [13], morphology operation [26,27], k-means [28], Hough transform [29] and watershed [30]. For the better ways, Li et al [ [42] used graph cut approach to segment cervical cells in images with healthy and abnormal cells, and then segmented the nuclei especially abnormal nuclei by using group cut adaptively and locally.…”

Section: Cervical Cell Recognitionmentioning

confidence: 99%

“…Early methods proposed to achieve the automatic segmentation and classification of abnormal cervical cells based on isolated cell without overlapping images. The most of them used one or multiple techniques including thresholding [13], morphology operation [26,27], k-means [28], Hough transform [29] and watershed [30]. For the better ways, Li et al [31] utilize a Radiating Gradient Vector Flow (RGVF) Snake to extract both the nucleus and sytoplasm from a single-cell cervical cell image.…”

Section: Cervical Cell Recognitionmentioning

confidence: 99%

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A novel automation-assisted cervical cancer reading method based on convolutional neural network

Xiang

Sun

Pan

et al. 2020

Biocybernetics and Biomedical Engineering

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Cervical cytology screening using Pap smear or liquid-based cytology is one of the most widely followed and accepted method. Automation-assisted screening based on cervical cytology has become a necessity due to the manual screening method operated by a visual analysis for cervical cell specimen under the microscope of the glass slide is usually labor-intensive and time-consuming. While automation-assisted reading system can improve efficiency, their performance often relies on the success of accurate cell segmentation and hand-craft feature extraction. This paper presents an efficient and totally segmentation-free method for automated cervical cell screening that utilizes modern object detector to directly detect cervical cells or clumps, without the design of specific hand-crafted feature. Specifically, we use the state-of-the-art CNN-based object detection methods, YOLOv3, as our baseline model. In order to improve the classification performance of hard examples which are four highly similar categories, we cascade an additional task-specific classifier. We also investigate the presence of unreliable annotations and coped with them by smoothing the distribution of noisy labels.We comprehensively evaluate our methods on our test set which is consisted of 1,014 annotated cervical cell images with size of 4000×3000 and complex cellular situation corresponding to 10 categories.Our model achieves 97.5% sensitivity (Sens) and 67.8% specificity (Spec) on cervical cell image-level screening. Moreover, we obtain a best mean Average Precision (mAP) of 63.4% on cervical cell-level diagnosis, and improve the Average Precision (AP) of hard examples which are the most valuable but most difficult to distinguish. Our automation-assisted cervical cell reading system not only achieves cervical cell image-level classification but also provides more detailed location and category reference information of abnormal cells. The results indicate feasible performance of our method, together with the efficiency and robustness, providing a new idea for future development of computer-assisted reading systems in clinical cervical screening.

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Section: Cervical Cell Recognitionmentioning

confidence: 99%

“…Early methods proposed to achieve the automatic segmentation and classification of abnormal cervical cells based on isolated cell without overlapping images. The most of them used one or multiple techniques including thresholding [13], morphology operation [26,27], k-means [28], Hough transform [29] and watershed [30]. For the better ways, Li et al [31] utilize a Radiating Gradient Vector Flow (RGVF) Snake to extract both the nucleus and sytoplasm from a single-cell cervical cell image.…”

Section: Cervical Cell Recognitionmentioning

confidence: 99%