An Unsupervised Approach for Overlapping Cervical Cell Cytoplasm Segmentation

The poor contrast and the overlapping of cervical cell cytoplasm are the major issues in the accurate segmentation of cervical cell cytoplasm. This paper presents an automated unsupervised cytoplasm segmentation approach which can effectively find the cytoplasm boundaries in overlapping cells. The proposed approach first segments the cell clumps from the cervical smear image and detects the nuclei in each cell clump. A modified Otsu method with prior class probability is proposed for accurate segmentation of nuclei from the cell clumps. Using distance regularized level set evolution, the contour around each nucleus is evolved until it reaches the cytoplasm boundaries. Promising results were obtained by experimenting on ISBI 2015 challenge dataset.Comment: 4 pages, 4 figures, Biomedical Engineering and Sciences (IECBES), 2016 IEEE EMBS Conference on. IEEE, 201

Ekstraksi Dan Seleksi Fitur Untuk Klasifikasi Sel Epitel Dengan Sel Radang Pada Citra Pap Smear

Penelitian ini dilakukan seleksi fitur menggunakan Fisher Criterion, sedangkan pada proses klasifikasi data menggunakan algoritma Backpropagation terhadap 16 fitur yang terlebih dahulu diekstrak dari citra Pap smear. Adapun ke-16 fitur yang digunakan dibagi menjadi 3 kategori, yaitu: Fitur bentuk, Fitur tekstur, dan Fitur intensitas warna. Pada naskah ini terdapat 2 tahap utama, yaitu: 1) Ekstraksi Fitur; dan 2) Seleksi Fitur. Penelitian ini bertujuan menganalisis kinerja seleksi fitur pada klasifikasi data dan mencari fitur yang secara signifikan mempengaruhi klasifikasi sel epitel dengan sel radang. Sebagai pembanding, penelitian ini juga membandingkan hasil seleksi fitur antara Fisher Criterion dangan Feature Subset Selection. Hasil yang diperoleh dari proses perbandingan tersebut menunjukkan kesamaan fitur yang secara signifikan mempengaruhi proses klasifikasi sel radang dengan sel epitel. Tingkat akurasi klasifikasi pada penelitian ini adalah 92.5%

Inflammatory Cell Extraction in Pap smear Images: A Combination of Distance Criterion and Image Transformation Approach

In order to obtain a diagnosis of cervical cancer information, the characteristics of each cell nucleus must be identified and evaluated properly through a Pap smear test. The presence of inflammatory cells in Pap smear images can complicate the process of identification of cell nuclei in the early detection of cervical cancer. Inflammatory cells need to be eliminated to assist pathologists in reading Pap smear slides. In this work, we developed a novel method to extract the inflammatory cells that allow detection of cell nuclei more accuracy. The proposed algorithm consists of two stages: extraction of inflammatory cells using the distance criterion and image transformation. This experiment applied to the 1358 cells comprising 378 nuclei cells and 980 inflammatory cells from 25 Pap smear images. The results showed that our method can significantly reduce the amount of inflammation that can disrupt the cell nuclei in the detection process. The proposed method has promising results with a sensitivity level of 97% and a specificity of 84.38%

Overlapping Cervical Nuclei Separation using Watershed Transformation and Elliptical Approach in Pap Smear Images

In this study, a robust method is proposed for accurately separating overlapping cell nuclei in cervical microscopic images. This method is based on watershed transformation and an elliptical approach. Since the watershed transformation process of taking the initial seed is done manually, the method was developed to obtain the initial seed automatically. Total initial seeds at this stage represents the number of nuclei that exist in the image of a pap smear, either overlapping or not. Furthermore, a method was developed based on an elliptical approach to obtain the area of each of the nuclei automatically. This method can successfully separate several (more than two) clustered cell nuclei. In addition, the proposed method was evaluated by experts and was proven to have better results than methods from previous studies in terms of accuracy and execution time. The proposed method can determine overlapping and non-overlapping boundaries of nuclei fast and accurately. The proposed method provides better decision-making on areas with overlapping nuclei and can help to improve the accuracy of image analysis and avoid information loss during the process of image segmentation

Detection and Segmentation of Cervical Cell Cytoplast and Nucleus

This article alms to develop a method for the detection and segmentation of a cytoplast and nucleus from a cervix smear image. First, the technique of equalization method with Gaussian filter is adopted to eliminate noise in the image. Second. a new edge enhancement technique is proposed to work out the coarseness of each pixel, which is later used as a determining characteristic of reinforced object images. A two-group object enhancement technique is then used to reinforce this object according to rough pixels. Third, the proposed detector enhances the gradients of the edges of the cytoplast and nucleus while suppressing the noise gradients, and then specifies the pixels with higher gradients as possible edge pixels. Finally, it picks out the two longest closed curves constructed by part of the edge pixels. Detection and segmentation performance of the proposed method is later compared with seed region growing feature extraction and level set method using 10 cervix smear images as example. Besides comparing the contour segment of the cytoplast and nucleus obtained by using different methods, we also compare the quality of the segmentation results. Experimental results show that the proposed detector demonstrates an impressive performance. (C) 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 260-270, 2009 Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.2019