Detecting partially occluded ellipses using the Hough transform
Image and Vision Computing - 4th Alvey Vision Meeting
A new curve detection method: randomized Hough transform (RHT)
Pattern Recognition Letters
Unsupervised cell nucleus segmentation with active contours
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing: PIKS Inside
Digital Image Processing: PIKS Inside
Mean Shift Analysis and Applications
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Segmentation of heterogeneous blob objects through voting and level set formulation
Pattern Recognition Letters
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
White blood cell segmentation using morphological operators and scale-space analysis
SIBGRAPI '07 Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing
Ellipse detection using a randomized hough transform based on edge segment merging scheme
ISPRA'07 Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
Nucleus and cytoplast contour detector of cervical smear image
Pattern Recognition Letters
Particle swarm optimization for pap-smear diagnosis
Expert Systems with Applications: An International Journal
A new algorithm for ellipse detection by curve segments
Pattern Recognition Letters
Segmentation of Biological Cell Images for Sonification
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 2 - Volume 02
Graph-based tools for microscopic cellular image segmentation
Pattern Recognition
Splitting touching cells based on concave points and ellipse fitting
Pattern Recognition
Feature Extraction & Image Processing, Second Edition
Feature Extraction & Image Processing, Second Edition
Interactive segmentation framework of the Medical Imaging Interaction Toolkit
Computer Methods and Programs in Biomedicine
A multidimensional segmentation evaluation for medical image data
Computer Methods and Programs in Biomedicine
Detection and segmentation of cervical cell cytoplast and nucleus
International Journal of Imaging Systems and Technology
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Web-based interactive 2D/3D medical image processing and visualization software
Computer Methods and Programs in Biomedicine
ICMB'08 Proceedings of the 1st international conference on Medical biometrics
An Automated Method for Segmentation of Epithelial Cervical Cells in Images of ThinPrep
Journal of Medical Systems
Edge Enhancement Nucleus and Cytoplast Contour Detector of Cervical Smear Images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Debris removal in Pap-smear images
Computer Methods and Programs in Biomedicine
Automatic cervical cell segmentation and classification in Pap smears
Computer Methods and Programs in Biomedicine
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In order to automate cervical cancer screening tests, one of the most important and longstanding challenges is the segmentation of cell nuclei in the stained specimens. Though nuclei of isolated cells in high-quality acquisitions often are easy to segment, the problem lies in the segmentation of large numbers of nuclei with various characteristics under differing acquisition conditions in high-resolution scans of the complete microscope slides. We implemented a system that enables processing of full resolution images, and proposes a new algorithm for segmenting the nuclei under adequate control of the expert user. The system can work automatically or interactively guided, to allow for segmentation within the whole range of slide and image characteristics. It facilitates data storage and interaction of technical and medical experts, especially with its web-based architecture. The proposed algorithm localizes cell nuclei using a voting scheme and prior knowledge, before it determines the exact shape of the nuclei by means of an elastic segmentation algorithm. After noise removal with a mean-shift and a median filtering takes place, edges are extracted with a Canny edge detection algorithm. Motivated by the observation that cell nuclei are surrounded by cytoplasm and their shape is roughly elliptical, edges adjacent to the background are removed. A randomized Hough transform for ellipses finds candidate nuclei, which are then processed by a level set algorithm. The algorithm is tested and compared to other algorithms on a database containing 207 images acquired from two different microscope slides, with promising results.