Image analysis of kidney using wavelet transform

Abstract

Ultrasonography is often preferred over other medical imaging modalities because it is noninvasive, portable, and versatile, it does not use ionizing radiations, and it is relatively low-cost. However, the main disadvantage of medical ultrasonography is the poor quality of im ages, which are a ected by multiplicative speckle noise. Speckle occurs especially in images of the liver and kidney whose underlying structures are too small to be resolved by large wavelength ultrasound. The presence of speckle is undesirable since it degrades image quality and it a ects the tasks of human interpretation and diagnosis. As a result, speckle ltering is a critical pre-processing step for feature extraction, analysis, and recognition from medical imagery measurements. For 2-dimensional B- mode ultrasound images, we use an image enhancement algorithm based on ltering and noise reducing procedures from the coarse to ne resolution images that are obtained from the wavelet-transformed data. A comparative study with other de-speckling techniques (median and Wiener ltering), employing quantitative indices and visual evaluation, demonstrated that our method achieved superior speckle reduction per formance