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.
Highlights
Fast and reliable ultrasound image segmentation is a complicated process with particular difficulties
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
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
Summary
Fast and reliable ultrasound image segmentation is a complicated process with particular difficulties. Local evaluation methods can be a solution for ultrasound images, since speckle pixels will be recognized precisely. In [7], researchers defined “local histogram range image” (LHRI) based on histogram distribution which reduced statistic complexity. They applied a classification method to recognize edge or border of organ while, speckle noises remained unchanged. LHRI was proposed for segmentation in [8]; its computational load was too high. The combined edge-based and continuitybased method are presented by modified LHRI. We expect to have proper segmented images and to decrease computational load by means of reducing input image size.
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