An Adaptive Approach for Noise Reduction in Sequences of CT Images

Georgieva, Veska; Kountchev, Roumen; Draganov, Ivo

doi:10.1007/978-3-319-00467-9_4

Cited by 1 publication

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“…The criterion is a minimum of three different entropy criteria: the energy of the transformed in wavelet domain image, Shannon entropy and the logarithm of energy [29,31].…”

Section: Noise Reduction Based On Wavelet Packet Decomposition and Admentioning

confidence: 99%

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Multistage Approach for Simple Kidney Cysts Segmentation in CT Images

Georgieva

Draganov

2016

New Approaches in Intelligent Image Analysis

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In the chapter is presented a multistage approach for segmentation of medical objects in Computed Tomography (CT) images. Noise reduction with consecutive applied median filter and wavelet shrinkage packet decomposition, and contrast enhancement based on Contrast limited adaptive histogram equalization (CLAHE) are applied in preprocessing stage. As a next step is used a combination of 2 basic methods for image segmentation such as split and merge algorithm, following by color based K-mean clustering. For refining the boundaries of the detected objects additional texture analysis is introduced based on limited Haralick's feature set and morphological filters. Due to the diminished number of components for the feature vectors the speed of the segmentation stage is higher in comparison with the full feature set. Some experimental results are presented, obtained by computer simulation in the MATLAB environment. The experimental results give detailed information about detected simple renal cysts and their boundaries in axial plane of CT images which are presented in native, arterial and venous phases. The proposed approach can be used in real time for precise diagnosis or in monitoring the disease progression.

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“…The criterion is a minimum of three different entropy criteria: the energy of the transformed in wavelet domain image, Shannon entropy and the logarithm of energy [29,31].…”

Section: Noise Reduction Based On Wavelet Packet Decomposition and Admentioning

confidence: 99%

“…The algorithm for noise reduction based on wavelet packet transform contains the following basic stages [29]:…”

Section: Noise Reduction Based On Wavelet Packet Decomposition and Admentioning

confidence: 99%