Morphological Characteristics of Cervical Cells for Cervical Cancer Diagnosis

Rahmadwati,; Naghdy, Golshah; Ros, Montserrat; Todd, Catherine

doi:10.1007/978-3-642-28308-6_32

Cited by 9 publications

(8 citation statements)

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“…The images are then segments into their constituent elements using color based K-means clustering method. There are four dominant colours: white for the background, dark purple for the nuclei, dark pink for the cytoplasm, light pink for stroma [1,13,7]. The colours are the outcome of the Hematoxylin and Eosin (H&E) staining in the cervical histology slide for ease of analysis.…”

Section: Methods 1; K-means Clustering Based On the Colourmentioning

confidence: 99%

“…A number of studies apply the median filter method in the pre-processing process to medical images [13,[27][28]. The median filter sorts the values (according to the brightness or intensity) of each neighbouring pixel in ascending order [1,13]. Subsequently, the median value of this ordered sequence is selected.…”

Section: Image Pre-processingmentioning

confidence: 99%

“…The distances between the sample and the cluster centroids are calculated to determine each sample's closest cluster and to generate new partition. c. Based on the distance (commonly Euclidian distance) of the pixel similarity , the pixel is clustered by application of Equation 1.…”

Section: Image Segmentationmentioning

confidence: 99%

“…An expert pathologist views a glass slide under the microscope and analyses the whole image in order to identify the region of interest (ROI). Pathologists examine the slide based on their experiences, and individual pathologists can interpret the slide differently [1,2]. The examination of the slide is therefore influenced by subjectivity factors [2].…”

Section: Introductionmentioning

confidence: 99%

“…The purpose of segmentation is to partition the ROI. The segmentation methods applied in the research for ROI identification within the histopathology image are edge detection [15][16], active contours [17][18][19], threshold-based approaches [11,20], K-Means clustering [1,13], Local Gaussian (LG) and Gaussian Mixture Model (GMM) [21], and application of the co-occurrence matrix [2]. This paper introduces a systematic, quantitative, accurate, repeatable and objective CADSS to help practitioners in increasing the sensitivity and specificity of pathology examinations for cervical cancer.…”

Section: Introductionmentioning

confidence: 99%

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Computer aided decision support system for cervical cancer classification

et al. 2012

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Conventional analysis of a cervical histology image, such a pap smear or a biopsy sample, is performed by an expert pathologist manually. This involves inspecting the sample for cellular level abnormalities and determining the spread of the abnormalities. Cancer is graded based on the spread of the abnormal cells. This is a tedious, subjective and timeconsuming process with considerable variations in diagnosis between the experts. This paper presents a computer aided decision support system (CADSS) tool to help the pathologists in their examination of the cervical cancer biopsies. The main aim of the proposed CADSS system is to identify abnormalities and quantify cancer grading in a systematic and repeatable manner. The paper proposes three different methods which presents and compares the results using 475 images of cervical biopsies which include normal, three stages of pre cancer, and malignant cases. This paper will explore various components of an effective CADSS; image acquisition, pre-processing, segmentation, feature extraction, classification, grading and disease identification. Cervical histological images are captured using a digital microscope. The images are captured in sufficient resolution to retain enough information for effective classification. Histology images of cervical biopsies consist of three major sections; background, stroma and squamous epithelium. Most diagnostic information are contained within the epithelium region. This paper will present two levels of segmentations; global (macro) and local (micro). At the global level the squamous epithelium is separated from the background and stroma. At the local or cellular level, the nuclei and cytoplasm are segmented for further analysis. Image features that influence the pathologists' decision during the analysis and classification of a cervical biopsy are the nuclei's shape and spread; the ratio of the areas of nuclei and cytoplasm as well as the texture and spread of the abnormalities. Similar features are extracted towards the automated classification process. This paper will present various feature extraction methods including colour, shape and texture using Gabor wavelet as well as various quantative metrics. Generated features are used to classify cells or regions into normal and abnormal categories. Following the classification process, the cancer is graded based on the spread of the abnormal cells. This paper will present the results of the grading process with five stages of the cancer spectrum. ABSTRACTConventional analysis of a cervical histology image, such a pap smear or a biopsy sample, is performed by an expert pathologist manually. This involves inspecting the sample for cellular level abnormalities and determining the spread of the abnormalities. Cancer is graded based on the spread of the abnormal cells. This is a tedious, subjective and timeconsuming process with considerable variations in diagnosis between the experts. This paper presents a computer aided decision support system (CADSS) tool to help the patholo...

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Section: Methods 1; K-means Clustering Based On the Colourmentioning

confidence: 99%