Automatic cervical cell segmentation and classification in Pap smears

Chankong, Thanatip; Theera-Umpon, Nipon; Auephanwiriyakul, Sansanee

doi:10.1016/j.cmpb.2013.12.012

Cited by 238 publications

(187 citation statements)

References 24 publications

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“…Hal ini dapat dicegah jika diperiksa pada tahap prakanker [2]. Kanker serviks terjadi karena adanya perkembangan sel-sel abnormal secara cepat dan tidak terkendali pada leher rahim [3].…”

Section: Pendahuluanunclassified

“…Penelitian sebelumnya mengusulkan ekstraksi fitur bentuk berdasarkan deskriptor regional dan deskriptor boundary yang dikombinasikan dengan fitur tekstur berdasarkan homogenitas Gray Level Co-occurence Matrix (GLCM) [2]. Fitur bentuk yang diusulkan oleh [2] yakni area nukleus, compactness nukleus, major axis length nukleus, minor axis length nukleus, aspek rasio nukleus, area sel, compactness sel, dan rasio N/C (rasio nukleus dengan sitoplasma).…”

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Klasifikasi Sel Serviks Pada Citra Pap Smear berdasarkan Fitur Bentuk Deskriptor Regional dan Fitur Tekstur Uniform Rotated Local Binary Pattern

Sholik

Fatichah

2017

JUTI

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ABSTRAK Perubahan orientasi objek pada saat akuisisi memerlukan metode ekstraksi fitur yang invariant terhadap rotasi. Ekstraksi fitur tekstur yang telah digunakan dalam kombinasi fitur sebelumnya untuk klasifikasi sel serviks pada dataset Herlev antara lain homogenitas GLCM dan Local Binary Pattern Histogram Fourier (LBP-HF I. PENDAHULUANANKER serviks merupakan penyebab utama keempat kematian kanker pada wanita di dunia [1]. Hal ini dapat dicegah jika diperiksa pada tahap prakanker [2]. Kanker serviks terjadi karena adanya perkembangan sel-sel abnormal secara cepat dan tidak terkendali pada leher rahim [3]. Papanicolaou test adalah langkah pertama dalam pemeriksaan kanker serviks [3]. Hasil pemeriksaan akan menunjukkan kondisi sel serviks normal atau abnormal. Pemeriksaan terdiri dari pengumpulan sampel sel dari daerah tertentu pada serviks, kemudian sampel sel diamati melalui mikroskop dan didiagnosis oleh ahli sel secara manual [4]. Analisis hasil pemeriksaan secara manual memerlukan waktu yang lama dan rawan terhadap kesalahan sehingga dibutuhkan proses klasifikasi sel serviks pada citra pap smear berbasis komputer.Adanya perubahan orientasi sel pada saat akuisisi akan mempengaruhi nilai fitur yang dihasilkan [5] sehingga menjadi dasar pada penelitian ini. Tidak semua fitur dapat menghasilkan fitur yang sama jika citra sel yang sama mengalami perubahan orientasi sehingga dibutuhkan metode ekstraksi fitur yang invariant terhadap rotasi.

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“…Hal ini dapat dicegah jika diperiksa pada tahap prakanker [2]. Kanker serviks terjadi karena adanya perkembangan sel-sel abnormal secara cepat dan tidak terkendali pada leher rahim [3].…”

Section: Pendahuluanunclassified

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Klasifikasi Sel Serviks Pada Citra Pap Smear berdasarkan Fitur Bentuk Deskriptor Regional dan Fitur Tekstur Uniform Rotated Local Binary Pattern

Sholik

Fatichah

2017

JUTI

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“…Chankong et al [5] used patch-based fuzzy C-means clustering method for the segmentation of cytoplasm and nuclei regions from Herlev dataset only. The four step segmentation approach is proposed in which first input single cell image is changed into grayscale and then median filter of size 7×7 is used to eliminate the noise and smoothen the image.…”

Section: Literature Surveymentioning

confidence: 99%

Various Techniques for Classification and Segmentation of Cervical Cell Images - A Review

Sharma¹,

Mangat²

2016

IJCA

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Pap smear test plays an important role for the early diagnosis of cervical cancer in which human cells taken from the cervix of patient are analysed for pre-cancerous changes. The manual analysis of these cells by expert cytologist is labor intensive and time consuming job. The automatic and accurate detection of cervical cells are two critical preprocessing steps for automatic Pap smear image analysis and also for diagnosis of pre-cancerous changes in the uterine cervix. Similarly, the reliable segmentation of abnormal nuclei in cervical cytology is of utmost importance in automation-assisted screening techniques. This paper presents, the existing automated methods for the detection, segmentation and boundary determination of cells nuclei in conventional Pap stained cervical smear images. The majority of cytoplasm segmentation uses K-means algorithm, edge detection method, thresholding approach, graph cut and active contours technique. Most of existing work is done on images of isolated cells, especially for those which are in the Herlev data set. For segmentation of images which contains multiple cells, level set and thresholding techniques have been used. The nucleus segmentation varies as: single-nucleus segmentation, touching-nuclei splitting and multiple-nuclei segmentation. However, many segmentation methods incorporates shape priors, usually enforcing elliptical shapes in order to overcome cell occlusion and noise. The main focus of this paper is comprehensive literature survey of various existing classification and segmentation techniques. The shortcomings and failures of the existing work are also provided for further enhancement and improvement of overall performance and accuracy.

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“…Segmented images using a dataset of ERUDIT, LCH, and harlev. The results showed that the nuclei segmentation in harlev dataset gave precision, recall, and ZSI by 85%, 83%, and 80% [7]. Nucleui segmentation method was also conducted by Tang et al using Intersection Cortical Model (ICM) and Particle Swarm Optimization (PSO) to optimize the parameters in the ICM.…”

Section: Introductionmentioning

confidence: 99%

“…Colour image segmentation has the ability to improve the analysis process because the colourful images are more accommodating information needed [7]. It underlies this research by using a colour-based segmentation.…”

Section: Introductionmentioning

confidence: 99%

A Colour Space Based Detection for Cervical Cancer Using Fuzzy C-Means Clustering

Indrabayu

Fatmasari

Nurtanio

2017

Proceedings of the 6th International Conference on Bioinformatics and Biomedical Science

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This research presents a colour segmentation method using Hue, Saturation, Value (HSV) colour space based on fuzzy c-means clustering (FCM) to segment nucleus from single cell Pap smear images. Nucleus is a structural part of cell which can indicate whether a cell is normal or abnormal. This research aims to analyze the performance of colour space in the segmentation process. Pap smear images were segmented in HSV colour space by using fuzzy c-means clustering technique. Compared with segmentation process directly on HSV channel, the segmentation of each channel in space H, S and V were proposed. The segmentation results on each channel that has been applied roundness detection subsequently merged as the final segmentation and labeled as a nucleus. This research used 70 single cell Pap smear images taken in harlev dataset to examine the proposed segmentation method. The calculation of segmentation performance used the measurement based on precision, recall, and Zijdenbox Similarity Index (ZSI). The result showed that the proposed method generated precision, recall, and ZSI by 93%, 94%, and 93%.

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Automatic cervical cell segmentation and classification in Pap smears

Cited by 238 publications

References 24 publications

Klasifikasi Sel Serviks Pada Citra Pap Smear berdasarkan Fitur Bentuk Deskriptor Regional dan Fitur Tekstur Uniform Rotated Local Binary Pattern

Klasifikasi Sel Serviks Pada Citra Pap Smear berdasarkan Fitur Bentuk Deskriptor Regional dan Fitur Tekstur Uniform Rotated Local Binary Pattern

Various Techniques for Classification and Segmentation of Cervical Cell Images - A Review

A Colour Space Based Detection for Cervical Cancer Using Fuzzy C-Means Clustering

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