@inproceedings{1e38396190e440e0a42d6268e388402d,
title = "A neural approach to image thresholding",
abstract = "Image thresholding (as the simplest form of segmentation) is a very challenging task because of the differences in the characteristics of different images such that different thresholds may be tried to obtain maximum segmentation accuracy. In this paper, a supervised neural network is used to {"}dynamically{"} threshold images by assigning a suitable threshold to each image. The network is trained using a set of simple features extracted from medical images randomly selected form a sample set and then tested using the remaining medical images. The results are compared with the Otsu algorithm and the active shape models (ASM) approach.",
author = "Othman, {Ahmed A.} and Tizhoosh, {Hamid R.}",
year = "2010",
doi = "10.1007/978-3-642-15819-3_72",
language = "English (US)",
isbn = "3642158188",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 1",
pages = "561--564",
booktitle = "Artificial Neural Networks, ICANN 2010 - 20th International Conference, Proceedings",
edition = "PART 1",
note = "20th International Conference on Artificial Neural Networks, ICANN 2010 ; Conference date: 15-09-2010 Through 18-09-2010",
}