TY  - JOUR
Y1  - 2014///
SN  - 1475925X
PB  - BioMed Central Ltd.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84924421998&doi=10.1186%2f1475-925X-13-154&partnerID=40&md5=b0a52a8c2fda82f056ca86883ecc0701
JF  - BioMedical Engineering Online
A1  - Yahya, N.
A1  - Kamel, N.S.
A1  - Malik, A.S.
VL  - 13
AV  - none
N1  - cited By 11
N2  - Background and purpose: Ultrasound imaging is a very essential technique in medical diagnosis due to its being safe, economical and non-invasive nature. Despite its popularity, the US images, however, are corrupted with speckle noise, which reduces US images qualities, hampering image interpretation and processing stage. Hence, there are many efforts made by researches to formulate various despeckling methods for speckle reduction in US images. Methods: In this paper, a subspace-based speckle reduction technique in ultrasound images is proposed. The fundamental principle of subspace-based despeckling technique is to convert multiplicative speckle noise into additive via logarithmic transformation, then to decompose the vector space of the noisy image into signal and noise subspaces. Image enhancement is achieved by nulling the noise subspace and estimating the clean image from the remaining signal subspace. Linear estimation of the clean image is derived by minimizing image distortion while maintaining the residual noise energy below some given threshold. The real US data for validation purposes were acquired under the IRB protocol (200210851-7) at the University of California Davis, which is also consistent with NIH requirements. Results: Experiments are carried out using a synthetically generated B-mode ultrasound image, a computer generated cyst image and real ultrasound images. The performance of the proposed technique is compared with Lee, homomorphic wavelet and squeeze box filter (SBF) in terms of noise variance reduction, mean preservation, texture preservation and ultrasound despeckling assessment index (USDSAI). The results indicate better noise reduction capability with the simulated images by the SDC than Lee, Wavelet and SBF in addition to less blurry effect. With the real case scenario, the SDC, Lee, Wavelet and SBF are tested with images obtained from raw radio frequency (RF) data. Results generated using real US data indicate that, in addition to good contrast enhancement, the autocorrelation results shows better preservation of image texture by SDC than Lee, Wavelet and SBF. Conclusion: In general, the performance of the SDC filter is better than Lee, Wavelet and SBF in terms of noise reduction, improvement in image contrast and preservation of the autocorrelation profiles. Furthermore, the filter required less computational time compared to Lee, Wavelet and SBF, which indicates its suitability for real time application. © 2014 Yahya et al.
IS  - 1
KW  - Autocorrelation; Diagnosis; Image texture; Medical imaging; Signal analysis; Speckle; Speech enhancement; Textures; Ultrasonic applications; Ultrasonic imaging; Vector spaces; Vectors
KW  -  B-mode ultrasound images; De-noising; Logarithmic transformations; Multiplicative speckle noise; Signal sub-space; Speckle reduction; University of California; USDSAI
KW  -  Image denoising
KW  -  Article; B scan; contrast enhancement; controlled study; echography; human; image analysis; image display; image enhancement; intermethod comparison; mathematical analysis; noise reduction; priority journal; signal noise ratio; spatial autocorrelation analysis; spatial domain constraint; squeeze box filter; statistical analysis; wavelet analysis; cyst; diagnostic imaging; imaging phantom; procedures; signal noise ratio
KW  -  Cysts; Image Enhancement; Phantoms
KW  -  Imaging; Signal-To-Noise Ratio; Ultrasonography
TI  - Subspace-based technique for speckle noise reduction in ultrasound images
ID  - scholars4112
ER  -