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

Automatic segmentation of overlapping cervical smear cells based on local distinctive features and guided shape deformation

Automated segmentation of cells from cervical smears poses great challenge to biomedical image analysis because of the noisy and complex background, poor cytoplasmic contrast and the presence of fuzzy and overlapping cells. In this paper, we propose an automated segmentation method for the nucleus and cytoplasm in a cluster of cervical cells based on distinctive local features and guided sparse shape deformation. Our proposed approach is performed in two stages: segmentation of nuclei and cellular clusters, and segmentation of overlapping cytoplasm. In the rst stage, a set of local discriminative shape and appearance cues of image superpixels is incorporated and classi ed by the Support Vector Machine (SVM) to segment the image into nuclei, cellular clusters, and background. In the second stage, a robust shape deformation framework is proposed, based on Sparse Coding (SC) theory and guided by representative shape features, to construct the cytoplasmic shape of each overlapping cell. Then, the obtained shape is re ned by the Distance Regularized Level Set Evolution (DRLSE) model. We evaluated our approach using the ISBI 2014 challenge dataset, which has 135 synthetic cell images for a total of 810 cells. Our results show that our approach outperformed existing approaches in segmenting overlapping cells and obtaining accurate nuclear boundaries. Keywords: overlapping cervical smear cells, feature extraction, sparse coding, shape deformation, distance regularized level set

Deep Learning Techniques for Cervical Cancer Diagnosis based on Pathology and Colposcopy Images

Cervical cancer is a prevalent disease affecting millions of women worldwide every year. It requires significant attention, as early detection during the precancerous stage provides an opportunity for a cure. The screening and diagnosis of cervical cancer rely on cytology and colposcopy methods. Deep learning, a promising technology in computer vision, has emerged as a potential solution to improve the accuracy and efficiency of cervical cancer screening compared to traditional clinical inspection methods that are prone to human error. This review article discusses cervical cancer and its screening processes, followed by the Deep Learning training process and the classification, segmentation, and detection tasks for cervical cancer diagnosis. Additionally, we explored the most common public datasets used in both cytology and colposcopy and highlighted the popular and most utilized architectures that researchers have applied to both cytology and colposcopy. We reviewed 24 selected practical papers in this study and summarized them. This article highlights the remarkable efficiency in enhancing the precision and speed of cervical cancer analysis by Deep Learning, bringing us closer to early diagnosis and saving lives

Design Of An Automated Slide Capturing System With A DSP-Based Automatic Features Extraction Of Thinprep Images For Cervical Cancer [RC280.U8 R627 2008 f rb].

Kaedah konvensional untuk mengesan kanser pangkal rahim melibatkan pakar patologi dan sitologis memeriksa slaid palitan ThinPrep® di bawah mikroskop cahaya di bawah pembesaran 100X dan 400X. Conventional method of cervical cancer screening involves of pathologist or cytologist examining the ThinPrep® cervical smear slide under normal light microscope with 100X and 400X magnification

Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images

Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. This becomes particularly challenging for extremely large images, since manual intervention and processing time can make segmentation intractable. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional (3D) contour evolution that extends previous work on fast two-dimensional active contours. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The proposed software takes advantage of Monte-Carlo sampling schemes in order to speed up convergence and reduce thread divergence. Experimental results show that this method provides superior performance for large 2D and 3D cell segmentation tasks when compared to existing methods on large 3D brain images

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%