A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new approach to combining region growing and edge detection
Pattern Recognition Letters
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watershed-based segmentation and region merging
Computer Vision and Image Understanding
Digital Image Processing: PIKS Inside
Digital Image Processing: PIKS Inside
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing marbling in dry-cured Iberian ham by multiscale analysis
Pattern Recognition Letters
Strategies for image segmentation combining region and boundary information
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hybrid boundary detection algorithm based on watershed and snake
Pattern Recognition Letters
Seeded region growing: an extensive and comparative study
Pattern Recognition Letters
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Adaptive dual-point Hough transform for object recognition
Computer Vision and Image Understanding
Region growing: a new approach
IEEE Transactions on Image Processing
Lip image segmentation using fuzzy clustering incorporating an elliptic shape function
IEEE Transactions on Image Processing
Combining region and edge information to extract fish oocytes in histological images
VIIP '07 The Seventh IASTED International Conference on Visualization, Imaging and Image Processing
Statistical and wavelet based texture features for fish oocytes classification
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
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The study of biology and population dynamics of fish species requires the estimation of fecundity in individual fish in a routine way in many fisheries laboratories. The traditional procedure used by fisheries research is to count the oocytes manually on a subsample of known weight of the ovary, and to measure few oocytes under a binocular microscope. This process could be done on a computer using an interactive tool to count and measure oocytes. In both cases, the task is very time consuming, which implies that fecundity studies are rarely conducted routinely. This work represents the first attempt to design an automatic algorithm to recognize the oocytes in histological images. Two approaches based on region and edge information are described to segment the image and extract the oocytes. An statistical analysis reveals that higher than 74% of oocytes are recognized for both approaches, when an overlapping area between machine detection and true oocyte demanded is greater than 75%.