Analysis of Different Filters for Image Despeckling : A Review

Digital image acquisition and processing in clinical diagnosis plays a significant part. Medical images at the time of acquisition can be corrupted via noise. Removal of this noise from images is a challenging problem. The presence of signal dependent noise is referred as speckle which degrades the actual quality of an image. Considering, several techniques have been developed focused on speckle noise reduction. The primary purpose of these techniques was to improve visualization of an image followed by preprocessing step for segmentation, feature extraction and registration. The scope of this paper is to provide an overview of despeckling techniques

Speckle Noise Reduction in Medical Ultrasound Images Using Modelling of Shearlet Coefficients as a Nakagami Prior

The diagnosis of UltraSound (US) medical images is affected due to the presence of speckle noise. This noise degrades the diagnostic quality of US images by reducing small details and edges present in the image. This paper presents a novel method based on shearlet coefficients modeling of log-transformed US images. Noise-free log-transformed coefficients are modeled as Nakagami distribution and speckle noise coefficients are modeled as Gaussian distribution. Method of Log Cumulants (MoLC) and Method of Moments (MoM) are used for parameter estimation of Nakagami distribution and noise free shearlet coefficients respectively. Then noise free shearlet coefficients are obtained using Maximum a Posteriori (MaP) estimation of noisy coefficients. The experimental results were presented by performing various experiments on synthetic and real US images. Subjective and objective quality assessment of the proposed method is presented and is compared with six other existing methods. The effectiveness of the proposed method over other methods can be seen from the obtained results

Automated reduction the speckle noise of the panoramic ultrasound images of Muscles and Tendons

Peer reviewedPublisher PD

Computer-Assisted Algorithms for Ultrasound Imaging Systems

Ultrasound imaging works on the principle of transmitting ultrasound waves into the body and reconstructs the images of internal organs based on the strength of the echoes. Ultrasound imaging is considered to be safer, economical and can image the organs in real-time, which makes it widely used diagnostic imaging modality in health-care. Ultrasound imaging covers the broad spectrum of medical diagnostics; these include diagnosis of kidney, liver, pancreas, fetal monitoring, etc. Currently, the diagnosis through ultrasound scanning is clinic-centered, and the patients who are in need of ultrasound scanning has to visit the hospitals for getting the diagnosis. The services of an ultrasound system are constrained to hospitals and did not translate to its potential in remote health-care and point-of-care diagnostics due to its high form factor, shortage of sonographers, low signal to noise ratio, high diagnostic subjectivity, etc. In this thesis, we address these issues with an objective of making ultrasound imaging more reliable to use in point-of-care and remote health-care applications. To achieve the goal, we propose (i) computer-assisted algorithms to improve diagnostic accuracy and assist semi-skilled persons in scanning, (ii) speckle suppression algorithms to improve the diagnostic quality of ultrasound image, (iii) a reliable telesonography framework to address the shortage of sonographers, and (iv) a programmable portable ultrasound scanner to operate in point-of-care and remote health-care applications

Fusion of magnetic resonance and ultrasound images for endometriosis detection

Endometriosis is a gynecologic disorder that typically affects women in their reproductive age and is associated with chronic pelvic pain and infertility. In the context of pre-operative diagnosis and guided surgery, endometriosis is a typical example of pathology that requires the use of both magnetic resonance (MR) and ultrasound (US) modalities. These modalities are used side by sidebecause they contain complementary information. However, MRI and US images have different spatial resolutions, fields of view and contrasts and are corrupted by different kinds of noise, which results in important challenges related to their analysis by radiologists. The fusion of MR and US images is a way of facilitating the task of medical experts and improve the pre-operative diagnosis and the surgery mapping. The object of this PhD thesis is to propose a new automatic fusion method for MRI and US images. First, we assume that the MR and US images to be fused are aligned, i.e., there is no geometric distortion between these images. We propose a fusion method for MR and US images, which aims at combining the advantages of each modality, i.e., good contrast and signal to noise ratio for the MR image and good spatial resolution for the US image. The proposed algorithm is based on an inverse problem, performing a super-resolution of the MR image and a denoising of the US image. A polynomial function is introduced to modelthe relationships between the gray levels of the MR and US images. However, the proposed fusion method is very sensitive to registration errors. Thus, in a second step, we introduce a joint fusion and registration method for MR and US images. Registration is a complicated task in practical applications. The proposed MR/US image fusion performs jointly super-resolution of the MR image and despeckling of the US image, and is able to automatically account for registration errors. A polynomial function is used to link ultrasound and MR images in the fusion process while an appropriate similarity measure is introduced to handle the registration problem. The proposed registration is based on a non-rigid transformation containing a local elastic B-spline model and a global affine transformation. The fusion and registration operations are performed alternatively simplifying the underlying optimization problem. The interest of the joint fusion and registration is analyzed using synthetic and experimental phantom images