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Aja-Fern′andez, S., Martin-Fern′andez, M. and Alberola-L′opez, C. (2007) Tissue identification in ultrasound images using Rayleigh local parameter estimation. Proceedings of Bioinformatics and Bioengineering, Boston.

has been cited by the following article:

• TITLE: Medical ultrasound image segmentation by modified local histogram range image method

  AUTHORS: Ali Kermani, Ahmad Ayatollahi, Ahmad Mirzaei, Mohammad Barekatain

  KEYWORDS: Segmentation; Local Histogram; Ultrasound Image; Morphological Image Processing; Discrete Wavelet Transform

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.3 No.11, November 18, 2010

  ABSTRACT: Fast and satisfied medical ultrasound segmentation is known to be difficult due to speckle noises and other artificial effects. Since speckle noise is formed from random signals which are emitted by an ultrasound system, we can’t encounter the same way as other image noises. Lack of information in ultrasound images is another problem. Thus, segmentation results may not be accurate enough by means of customary image segmentation methods. Those methods that can specify undesirable effects and segment them by eliminating artificial effects, should be chosen. It seems to be a complicated work with high computational load. The current study presents a different approach to ultrasound image segmentation that relies mainly on local evaluation, named as local histogram range image method which is modified by means of discrete wavelet transform. Thus, a significant decrease in computational load is then achieved. The results show that it is possible for tissues to be segmented correctly.