Cervical Cancer Classification Using Gabor Filters

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

doi:10.1109/hisb.2011.15

Cited by 19 publications

(15 citation statements)

References 7 publications

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“…Authors in [4][5] proposed methods for cervical cancer detection and grading by color texture features and content based image retrieval techniques. The same problem is addressed in [6][7] employing Gabor filters, marked watershed segmentation and Delaney triangulation. The method in [8] detects and grades oral cancer using higher order spectra features and fuzzy classifier.…”

Section: Related Workmentioning

confidence: 97%

Computer assisted diagnosis of basal cell carcinoma using Z-transform features

Noroozi

Zakerolhosseini

2016

Journal of Visual Communication and Image Representation

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Section: Related Workmentioning

confidence: 97%

Computer assisted diagnosis of basal cell carcinoma using Z-transform features

Noroozi

Zakerolhosseini

2016

Journal of Visual Communication and Image Representation

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“…Each filter produces one element of a feature vector in high dimensionality [35]. It is important to select the appropriate filter parameters in order to characterise textures embedded in the image for the segmentation process [14]. The finer detection of textures is obtained by applying the filters with higher frequencies [35].…”

Section: Gabor Waveletmentioning

confidence: 99%

“…The development of the proposed CADSS requires the integration of the research areas including mathematics, digital image processing, physics, computer vision and statistics. Studies have documented computer aided diagnosis (CAD) to detect and classify abnormalities of cells within histology images [2,[9][10][11][12][13][14]. One of the processes in CADSS is image segmentation.…”

Section: Introductionmentioning

confidence: 99%

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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“…Chen et al [ 6 ] proposed classifying the cells into superficial cells, intermediate cells, parabasal cells, low-grade squamous intraepithelial lesion, and high-grade squamous intraepithelial lesion (HSIL). Rahmadwati et al [ 10 , 11 ] classified all the cervical cells into normal, premalignant, and malignant categories. In another study [ 11 ], the premalignant stage was further divided into CIN1 (carcinoma in situ 1), CIN2, and CIN3.…”

Section: Introductionmentioning

confidence: 99%

“…Rahmadwati et al [ 10 , 11 ] classified all the cervical cells into normal, premalignant, and malignant categories. In another study [ 11 ], the premalignant stage was further divided into CIN1 (carcinoma in situ 1), CIN2, and CIN3. Rajesh Kumar et al [ 12 ] classified the cervical cells into two types of cells, normal and abnormal cervical cells.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Cervical Cancer Cells by a Two-Level Cascade Classification System

et al. 2016

Analytical Cellular Pathology

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We proposed a method for automatic detection of cervical cancer cells in images captured from thin liquid based cytology slides. We selected 20,000 cells in images derived from 120 different thin liquid based cytology slides, which include 5000 epithelial cells (normal 2500, abnormal 2500), lymphoid cells, neutrophils, and junk cells. We first proposed 28 features, including 20 morphologic features and 8 texture features, based on the characteristics of each cell type. We then used a two-level cascade integration system of two classifiers to classify the cervical cells into normal and abnormal epithelial cells. The results showed that the recognition rates for abnormal cervical epithelial cells were 92.7% and 93.2%, respectively, when C4.5 classifier or LR (LR: logical regression) classifier was used individually; while the recognition rate was significantly higher (95.642%) when our two-level cascade integrated classifier system was used. The false negative rate and false positive rate (both 1.44%) of the proposed automatic two-level cascade classification system are also much lower than those of traditional Pap smear review.

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Cervical Cancer Classification Using Gabor Filters

Cited by 19 publications

References 7 publications

Computer assisted diagnosis of basal cell carcinoma using Z-transform features

Computer assisted diagnosis of basal cell carcinoma using Z-transform features

Computer aided decision support system for cervical cancer classification

Automatic Detection of Cervical Cancer Cells by a Two-Level Cascade Classification System

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