Browsing by Author "Basaza-Ejiri, Annabella Habinka"

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    Automated Segmentation of Nucleus and Cytoplasm of Cervical Cells from Pap-smear Images using A Quadtree Decomposition Approach.
    (Research square, 2021) Wasswa, William; Ware, Andrew; Basaza-Ejiri, Annabella Habinka; Obungoloch, Johnes
    Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology especially for cervical cancer screening from pap-smears. Manual assessment of pap-smears is labour intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. A critical prerequisite in automated analysis of pap-smears is nucleus and cytoplasm segmentation, which is the basis of cervical cancer screening. This paper articulates a potent approach to the segmentation of cervical cells into nucleus and cytoplasm using a quadtree decomposition approach with statistical measures.
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    Automated Segmentation of Nucleus, Cytoplasm and Background of Cervical Cells from Pap-smear Images using a Trainable Pixel Level Classifier
    (IEEE., 2019) Wasswa, William; Obungoloch, Johnes; Basaza-Ejiri, Annabella Habinka; Ware, Andrew
    Cervical cancer ranks as the fourth most prevalent cancer affecting women worldwide and its early detection provides the opportunity to help save life. Automated diagnosis of cervical cancer from pap-smear images enables accurate, reliable and timely analysis of the condition's progress. Cell segmentation is a fundamental aspect of successful automated pap-smear analysis. In this paper, a potent approach for segmentation of cervical cells from a pap-smear image into the nucleus, cytoplasm and background using pixel level information is proposed. A number of pixels from the nuclei, cytoplasm and background are extracted from 100 images to form a feature vector which is trained using noise reduction, edge detection and texture filters to produce a pixel level classifier. Comparison of the segmented images' nucleus and cytoplasm parameters (nucleus area, longest diameter, roundness, perimeter and cytoplasm area, longest diameter, roundness, perimeter) with the ground truth image features yielded average percentage errors of 0.14, 0.28, 0.03, 0.30, 0.15, 0.25, 0.05 and 0.39 respectively. Validation of the pixel classifier with 10fold cross-validation yielded pixel classification accuracy of 98.50%, 97.70% and 98.30% with Fast Random Forest, Naïve Bayes and J48 classification methods respectively. Comparison of the segmented nucleus and cytoplasm with the ground truth nucleus and cytoplasm segmentations resulted into a Zijdenbos similarity index greater than 0.9321 and 0.9639 for nucleus and cytoplasm segmentation respectively. The results indicated that the proposed pixel level segmentation classifier was able to extract the nucleus and cytoplasm regions accurately and worked well even though there was no significant contrast between the components in the image. The results from cross-validation and test set evaluation imply that the classifier can segment cells outside the training dataset with high precision.
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    Cervical Cancer Classification from Pap-smears using an Enhanced Fuzzy C-Means Algorithm
    (Informatics in Medicine Unlocked, 2019) Wasswa, William; Ware, Andrew; Basaza-Ejiri, Annabella Habinka; Obungoloch, Johnes
    Globally, cervical cancer ranks as the fourth most prevalent cancer affecting women. However, it can be successfully treated if detected at an early stage. The Pap smear is a good tool for initial screening of cervical cancer, but there is the possibility of error due to human mistake. Moreover, the process is tedious and time-consuming. The objective of this study was to mitigate the risk of mistake by automating the process of cervical cancer classification from Pap smear images. In this research, contrast local adaptive histogram equalization was used for image enhancement. Cell segmentation was achieved through a Trainable Weka Segmentation classifier, and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy c-means algorithm. The evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and Pap smear slide images from a pathology unit). An overall classification accuracy, sensitivity and specificity of ‘98.88%, 99.28% and 97.47%‘, ‘97.64%, 98.08% and 97.16%’ and ‘96.80%, 98.40% and 95.20%’ were obtained for each dataset respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that was utilized to select cell features that would improve the classification performance, and the number of clusters used during defuzzification and classification. The evaluation and testing conducted confirmed the rationale of the approach taken, which is based on the premise that the selection of salient features embeds sufficient discriminatory information that leads to an increase in the accuracy of cervical cancer classification. Results show that the method outperforms many of the existing algorithms in terms of the false negative rate (0.72%), false positive rate (2.53%), and classification error (1.12%), when applied to the DTU/Herlev benchmark Pap smear dataset. The approach articulated in this paper is applicable to many Pap smear analysis systems, but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies.
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    A Review of Applications of Image Analysis and Machine Learning Techniques in Automated Diagnosis and Classification of Cervical Cancer from Pap-smear Images
    (IEEE., 2018) Wasswa, William; Basaza-Ejiri, Annabella Habinka; Obungoloch, Johnes; Ware, Andrew
    Cervical cancer ranks as the fourth most prevalent form of cancer affecting women worldwide and its early detection provides the opportunity to help save life. Automated diagnosis and classification of cervical cancer has become a necessity as it enables accurate, reliable and timely analysis of the condition's progress. This survey paper presents an overview of the state of the art as articulated in a number of prominent recent publications focusing on automated diagnosis and classification of cervical cancer from pap-smear images. It reviews thirty journal papers obtained electronically through four scientific databases searched using three sets of keywords: (1) Segmentation, Classification, Cervical Cancer; (2) Medical Imaging, Machine Learning, pap-smear Images; (3) Automated, Segmentation, Pap-smear Images. The review found that some techniques are used more frequently than others are: for example, filtering, thresholding and KNN are the most used techniques for preprocessing, segmentation and classification of pap-smear images. It has also been observed that the superiority of the results of a classification algorithm over the other greatly depends on a number of factors which include: the set of features selected, the accuracy of the segmentation, the type of pre-processing techniques used and the type of datasets used. Most of the existing algorithms result in an accuracy of nearly 93.78% on open pap-smear data set segmented using commercial digital image segmentation softwares. K-Nearest-Neighbours has been reported to be an excellent classifier for cervical images giving an accuracy of over 99.27% for a 2-class classification problem. The reviewed papers indicate that there are still weaknessess in the available techniques that result in low accuracy of classification in some classes of cells. This accuracy can be improved by extracting more features, improvement in noise removal, and using hybrid segmentation and classification techniques.
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    A review of Image Analysis and Machine Learning Techniques for Automated Cervical Cancer Screening from pap-smear image
    (Computer methods and programs in biomedicine, 2018) Wasswa, William; Ware, Andrew; Basaza-Ejiri, Annabella Habinka; Obungoloch, Johnes
    Early diagnosis and classification of a cancer type can help facilitate the subsequent clinical management of the patient. Cervical cancer ranks as the fourth most prevalent cancer affecting women worldwide and its early detection provides the opportunity to help save life. To that end, automated diagnosis and classification of cervical cancer from pap-smear images has become a necessity as it enables accurate, reliable and timely analysis of the condition's progress. This paper presents an overview of the state of the art as articulated in prominent recent publications focusing on automated detection of cervical cancer from pap-smear images.