Analisa Fitur Tekstur Nukleus dan Deteksi Sitoplasma pada Citra Pap Smear

Currently the identification of Pap smear cells in the early detection process of cervical cancer is still an important stage of the process. The ease of detecting Pap smear cells will be very helpful in the introduction of cell abnormalities. Pap smear cell images consist of parts of the nucleus and cytoplasm. Proper analysis of parts of the nucleus and cytoplasm will facilitate the process of identifying cell abnormalities. This study presents Pap smear cell texture analysis on the pap smear cell nucleus and segmentation of the cytoplasmic area. Texture analysis was performed on 250 cell images of the nucleus. While cytoplasmic segmentation was performed for 887 cytoplasmic cell images. Senua cell image used has class categories categorized into seven classes. Three classes of them are normal cell image class categories that include: Normal Superficial, Normal Intermediate, and Normal Columnar, and the other four classes are abnormal cell image class categories which include: mild dysplasia, moderate dysplasia, severe dysplasia and carcinoma Di There. The method used for texture analysis using 8 bit grayscale. And using the second sequence of Gray Level Co-occurrence Matrix (GLCM) statistics, with contrast, correlation, energy, homogeneity and entropy features. Cytoplasmic detection uses edge detection and some morphological analyzes. The results showed that the numerical results of all the texture of the nucleus for each class of Pap smear image had slightly different properties. As for the results of cytoplasmic detection showed that the stage of the proposed detection process results in a clean area of the cytoplasm and can be detected wel

Semi-Automatic Features Extraction Of Cervical Cells

This project is entitled ‘Semi-automatic Features Extraction of Cervical Cells’. The project is aimed to create a user friendly software which can be able to analyze Pap smear images via image processing. Cytological screening using the Pap smear test is the most effective strategy for the detection of precancerous state and consequent control of cervical cancer. Cytological samples that are taken from Pap smear test will undergo further analysis to detect the degree of abnormality of the cervical cells. The results of the abnormality of the samples can be inaccurate since some types of the medical images are blurring and highly affected by unwanted noise. Those bottlenecks in the medical images are believed that can be reduced via implementations of an adaptive fuzzy c-means (AFCM) and moving k-means (MKM) clustering techniques. These clustering techniques were used to segment the Pap smear images and later the features of the cells were extracted using region growing based feature extraction (RGBFE) technique. The performance of AFCM and MKM were analyzed based on the segmentation results of 6 Pap smear images. In overall, MKM was produced much better images than AFCM. Although the results have revealed that AFCM was suffering from centre redundancy and poor final centres in most of the cases, but it has also shown an advantage over MKM where AFCM was not sensitive to initial centres

ANALISA FITUR TEKSTUR NUKLEUS DAN DETEKSI SITOPLASMA PADA CITRA PAP SMEAR

Currently, the identification of Pap smear cells in the early detection process of cervical cancer is still an important stage of the process. The ease of detecting Pap smear cells will be very helpful in the introduction of cell abnormalities. Pap smear cell images consist of parts of the nucleus and cytoplasm. Proper analysis of parts of the nucleus and cytoplasm will facilitate the process of identifying cell abnormalities. This study presents Pap smear cell texture analysis on the pap smear cell nucleus and segmentation of the cytoplasmic area. Texture analysis was performed on 250 cell images of the nucleus. While cytoplasmic segmentation was performed for 887 cytoplasmic cell images. Senua cell image used has class categories categorized into seven classes. Three classes of them are normal cell image class categories that include: Normal Superficial, Normal Intermediate, and Normal Columnar, and the other four classes are abnormal cell image class categories which include: mild dysplasia, moderate dysplasia, severe dysplasia, and carcinoma Di There. The method used for texture analysis using 8-bit grayscale. And using the second sequence of Gray Level Co-occurrence Matrix (GLCM) statistics, with contrast, correlation, energy, homogeneity, and entropy features. Cytoplasmic detection uses edge detection and some morphological analyzes. The results showed that the numerical results of all the texture of the nucleus for each class of Pap smear image had slightly different properties. As for the results of cytoplasmic detection showed that the stage of the proposed detection process results in a clean area of the cytoplasm and can be detected wel

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