Acetowhite Region Research Articles

Recognition of Cervical Precancerous Lesions Based on Probability Distribution Feature Guidance.

Cervical cancer is a high incidence of cancer in women and cervical precancerous screening plays an important role in reducing the mortality rate. In this study, we proposed a multichannel feature extraction method based on the probability distribution features of the Acetowhite (AW) region to identify cervical precancerous lesions, with the overarching goal to improve the accuracy of cervical precancerous screening. A k-means clustering algorithm was first used to extract the cervical region images from the original colposcopy images. We then used a deep learning model called DeepLab V3+ to segment the AW region of the cervical image after the acetic acid experiment, from which the probability distribution map of the AW region after segmentation was obtained. This probability distribution map was fed into a neural network classification model for multichannel feature extraction, which resulted in the final classification performance. Results of the experimental evaluation showed that the proposed method achieved an average accuracy of 87.7%, an average sensitivity of 89.3%, and an average specificity of 85.6%. Compared with the methods that did not add segmented probability features, the proposed method increased the average accuracy rate, sensitivity, and specificity by 8.3%, 8%, and 8.4%, respectively. Overall, the proposed method holds great promise for enhancing the screening of cervical precancerous lesions in the clinic by providing the physician with more reliable screening results that might reduce their workload.

Segmentation of acetowhite region in uterine cervical image based on deep learning

Acetowhite (AW) region is a critical physiological phenomenon of precancerous lesions of cervical cancer. An accurate segmentation of the AW region can provide a useful diagnostic tool for gynecologic oncologists in screening cervical cancers. Traditional approaches for the segmentation of AW regions relied heavily on manual or semi-automatic methods. To automatically segment the AW regions from colposcope images. First, the cervical region was extracted from the original colposcope images by k-means clustering algorithm. Second, a deep learning-based image semantic segmentation model named DeepLab V3+ was used to segment the AW region from the cervical image. The results showed that, compared to the fuzzy clustering segmentation algorithm and the level set segmentation algorithm, the new method proposed in this study achieved a mean Jaccard Index (JI) accuracy of 63.6% (improved by 27.9% and 27.5% respectively), a mean specificity of 94.9% (improved by 55.8% and 32.3% respectively) and a mean accuracy of 91.2% (improved by 38.6% and 26.4% respectively). A mean sensitivity of 78.2% was achieved by the proposed method, which was 17.4% and 10.1% lower respectively. Compared to the image semantic segmentation models U-Net and PSPNet, the proposed method yielded a higher mean JI accuracy, mean sensitivity and mean accuracy. The improved segmentation performance suggested that the proposed method may serve as a useful complimentary tool in screening cervical cancer.

A Fuzzy Reasoning Model for Cervical Intraepithelial Neoplasia Classification Using Temporal Grayscale Change and Textures of Cervical Images During Acetic Acid Tests

Objective: A novel fuzzy reasoning model using temporal grayscale change and texture information of acetic acid test cervical images was developed to classify the patients at risk for Cervical Intraepithelial Neoplasia (CIN). Methods: Pre- and post-acetic acid test images were obtained from 505 patients (383 CIN negative and 122 CIN positive). An automatic image segmentation algorithm was implemented to extract the acetowhite region, followed by feature extraction of the: 1) temporal grayscale change from pre- to post-images; 2) texture coarseness; and 3) texture complexity of post-test images. The index of CIN was derived based on fuzzy reasoning incorporating expert knowledge. Logistic regression was built with cross validation to compare the classification performance between different combinations of features. Results: The averaged sensitivity was 80.8%, 80.9%, and 82.8% and specificity was 82.0%, 87.4%, and 86.2% for three individual features, respectively. The combination of all three features significantly improved the sensitivity (84.7%) without a significant loss of specificity (86.5%). The fuzzy reasoning model further improved the sensitivity significantly (sensitivity 85.9% and specificity 86.6%). Conclusion: Image texture features outperformed temporal grayscale change in specificity. Combining grayscale- and texture-based features could improve the overall classification performance, creating a synergistic effect. The proposed fuzzy reasoning model could further increase the sensitivity without requiring additional features. Significance: The proposed automatic CIN classification algorithm could provide a useful screening tool in the prevention of cervical cancers.

Acetowhite region segmentation in uterine cervix images using a registered ratio image

Visual inspection with acetic acid (VIA) remains a main cervical cancer screening tool in developing countries. However, it depends on the operator's experience, and its utility is often limited by the lack of trained doctors. Smart colposcope devices to automatically detect the cervical intraepithelial neoplasia (CIN, the early stage of cervical cancer) may provide a promising alternative. As the acetowhite (AW) region is the most important feature of CIN during VIA, its segmentation is considered an important procedure in the automatic detection of CIN. In this study, an automatic AW region segmentation algorithm based on the pre-acetic-acid and post-acetic-acid test images was developed. The cervix region was extracted according to a clustering algorithm from the pre-acetic acid test image. A ratio image was then obtained after registering the pre- and post-acetic-acid test images to facilitate the segmentation of the AW region using a modified level set algorithm. The results showed that although the developed algorithm yielded a mean sensitivity of 71.86%, which was lower than that of the fuzzy C-means (FCM) algorithm by 12.08% and the classical CV model-based level set algorithm (CV-LSA) by 4.04%, a high mean specificity (92.76%) was achieved that was greater than those of FCM and CV-LSA by 46.61% and 31.34%, respectively. Additionally, a high Jaccard index (JI) mean accuracy of 61.51% was achieved, which was greater than those of FCM and CV-LSA by 18.74% and 17.14%, respectively. This new algorithm, with an improved segmentation performance over traditional algorithms, may serve as a promising tool to advance the clinical prognosis of cervical cancer.

Effective Classification of Cervical Cancer Using Contourlet Transform

Cervical cancer is the second most leading cancer among women. Source of cervical cancer is Human Papilloma Virus (HPV). Tests to confirm a diagnosis of cervical cancer are colposcopy and cervical biopsy (pap smear). This paper includes the usage of colposcopy images and tests to find where cancer cells are on the surface of the cervix. Contourlet transform (CT) is proposed to extract the features of the colposcopy images and then for classification of Aceto white Region (Abnormal) and Non Aceto white Region (Normal), K-Nearest Neighbourhood (KNN) classifier is used. In the existing system, wavelet transforms are used to extract the features in which minimum quantity of information and features are obtained (Only 3 directions are focused). In CT, for higher resolution, additional directional is employed. The greater performance is achieved by extracting and choosing the simplest features from contourlet coefficients of the colposcopy images and these outputs are fed into the KNN classifier for classification.

WAVELET TRANSFORM BASED AUTOMATIC LESION DETECTION IN CERVIX IMAGES USING ACTIVE CONTOUR

Colposcopy is a medical diagnostic procedure to examine an illuminated, magnified view of the cervix by a colposcope. Most cases of cervical cancer can be prevented through screening programs aimed at detecting precancerous lesions. Colposcopy cervical images are acquired in raw form which contains major cervix lesions, regions outside the cervix and parts of the imaging devices such as speculum. In this study, a fully automated lesion detection method based on active contour is proposed. To detect the lesion, the active contour method requires an initial mask in the acetowhite region. In the proposed method, the initial contour is automatically obtained based on Discrete Wavelet Transform (DWT). Before feature extraction, a preprocessing method is applied to remove the irrelevant information and specular reflection from the colposcopy cervical images based on Mathematical morphology, Gaussian Mixture Modeling. Then the wavelet features are extracted and the features are used as an input to the K Nearest Neighbour (KNN) to obtain the initial mask. Segmentation results are evaluated on 240 images of colposcopy.

Segmentation and classification of cervix lesions by pattern and texture analysis

This work aims at automated segmentation of major lesions observed in early stages of uterine cervical cancer. Automated segmentation reduces subjective variability and cost in current manual evaluation methods used to determine the biopsy locations for diagnosis. Two different methods, a non-convex optimisation approach and mathematical morphological approach, are used to segment the aceto-white region. Within this region other abnormalities, such as mosaic patterns, are classified by fuzzy c-means using a textural feature obtained from skeletonised vascular structures. These vascular structures are extracted by a series of morphological operations. Minimisation of uncertainties for degraded images is also discussed.