Contrast limited adaptive histogram equalization
Graphics gems IV
Attribute openings, thinnings, and granulometries
Computer Vision and Image Understanding
Unsupervised cell nucleus segmentation with active contours
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Detection and segmentation of cervical cell cytoplast and nucleus
International Journal of Imaging Systems and Technology
Active contours with selective local or global segmentation: A new formulation and level set method
Image and Vision Computing
Local binary patterns variants as texture descriptors for medical image analysis
Artificial Intelligence in Medicine
Edge Enhancement Nucleus and Cytoplast Contour Detector of Cervical Smear Images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Gradient watersheds in morphological scale-space
IEEE Transactions on Image Processing
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Morphological grayscale reconstruction in image analysis: applications and efficient algorithms
IEEE Transactions on Image Processing
Unsupervised segmentation and classification of cervical cell images
Pattern Recognition
Cervical cell classification based exclusively on nucleus features
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Classifier ensemble for an effective cytological image analysis
Pattern Recognition Letters
Automatic cervical cell segmentation and classification in Pap smears
Computer Methods and Programs in Biomedicine
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In this work, we present an automated method for the detection and boundary determination of cells nuclei in conventional Pap stained cervical smear images. The detection of the candidate nuclei areas is based on a morphological image reconstruction process and the segmentation of the nuclei boundaries is accomplished with the application of the watershed transform in the morphological color gradient image, using the nuclei markers extracted in the detection step. For the elimination of false positive findings, salient features characterizing the shape, the texture and the image intensity are extracted from the candidate nuclei regions and a classification step is performed to determine the true nuclei. We have examined the performance of two unsupervised (K-means, spectral clustering) and a supervised (Support Vector Machines, SVM) classification technique, employing discriminative features which were selected with a feature selection scheme based on the minimal-Redundancy-Maximal-Relevance criterion. The proposed method was evaluated on a data set of 90 Pap smear images containing 10,248 recognized cell nuclei. Comparisons with the segmentation results of a gradient vector flow deformable (GVF) model and a region based active contour model (ACM) are performed, which indicate that the proposed method produces more accurate nuclei boundaries that are closer to the ground truth.