Cervical Cancer Detection and Classification using Texture Analysis
نویسندگان
چکیده
منابع مشابه
Cervical Cancer Detection and Classification Using Texture Analysis
Cervical cancer is one of the deadliest cancer among women. The main problem with cervical cancer is that it cannot be identified in its early stages since it doesn’t show any symptoms until the final stages. Therefore the accurate staging will help to give the accurate treatment volume to the patient. Some diagnosing tools like X-ray, CT, MRI, etc. can be used with image processing techniques ...
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Accurate classification of Pap smear images becomes the challenging task in medical image processing. This can be improved in two ways. One way is by selecting suitable well defined specific features and the other is by selecting the best classifier. This paper presents a nominated texture based cervical cancer (NTCC) classification system which classifies the Pap smear images into any one of t...
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Cervical cancer is the deadly cancer caused in women which affects the cervix region of the uterus. The cervical tissues are categorized into three types the Squanomus Epithelium (SE), Columnar Epithelium(CE) and Aceto White (AW) Region. The AW region is used for diagnosing cervical cancer which is tested with acetic acid turns into white. A Biopsy of the tissue is taken where many computers as...
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We present a bottom-up approach for automatic cancer cell detection in multispectral microscopic thin Pap smear images. Around 4,000 multispectral texture features are explored for cancer cell detection. Using two feature screening measures, the initial feature set is effectively reduced to a computationally manageable size. Based on pixel-level screening results, cancerous regions can thus be ...
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For cervical cancer detection, the performance of multispectral texture (MST) features extracted from multispectral Pap smear images is evaluated. In this study we carried out pairwise comparisons between different image features, including MST versus average spectral texture features (AST, without spectral information), and MST versus multispectral intensity features (MSI, without texture info...
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ژورنال
عنوان ژورنال: Biomedical and Pharmacology Journal
سال: 2016
ISSN: 0974-6242
DOI: 10.13005/bpj/988