A novel fuzzy classification entropy approach to image thresholding
Pattern Recognition Letters
An incremental neural network for tissue segmentation in ultrasound images
Computer Methods and Programs in Biomedicine
A unified framework for image compression and segmentation by using an incremental neural network
Expert Systems with Applications: An International Journal
Image histogram thresholding based on multiobjective optimization
Signal Processing
A novel image thresholding method based on Parzen window estimate
Pattern Recognition
Computer Vision and Image Understanding
Fractional differentiation and non-Pareto multiobjective optimization for image thresholding
Engineering Applications of Artificial Intelligence
Image thresholding using type II fuzzy sets
Pattern Recognition
Median-based image thresholding
Image and Vision Computing
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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Medical images are used mainly in the diagnosing process and as an aid in determining correct treatment. Therefore, the process of segmenting different regions of interests (ROIs) within the medical images is considered a critical one. When provided with a segment with high segmentation accuracy, the physician can easily detect the problem and determine the best treatment. In this paper, a neural network retrained on-line is proposed to automatically segment medical images using a global threshold. The network is initially trained off-line using a set of features extracted from a set of randomly selected training images, along with their best thresholds, as targets for the neural network. The features are extracted using Seeded Up Robust Feature (SURF) technique from a rectangle around the ROI. This network continues training on-line as new images arrive, based on a feedback correction done by the clinician to the segmented image. This process is repeated multiple times to verify the generalization ability of the network.