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

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

Peer reviewedPublisher PD

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 analysis of ultrasound imaging of muscle and tendon in the upper limb using artificial intelligence methods

Accurate estimation of geometric musculoskeletal parameters from medical imaging has a number of applications in healthcare analysis and modelling. In vivo measurement of key morphological parameters of an individual’s upper limb opens up a new era for the construction of subject-specific models of the shoulder and arm. These models could be used to aid diagnosis of musculoskeletal problems, predict the effects of interventions and assist in the design and development of medical devices. However, these parameters are difficult to evaluate in vivo due to the complicated and inaccessible nature of structures such as muscles and tendons. Ultrasound, as a non-invasive and low-cost imaging technique, has been used in the manual evaluation of parameters such as muscle fibre length, cross sectional area and tendon length. However, the evaluation of ultrasound images depends heavily on the expertise of the operator and is time-consuming. Basing parameter estimation on the properties of the image itself and reducing the reliance on the skill of the operator would allow for automation of the process, speeding up parameter estimation and reducing bias in the final outcome. Key barriers to automation are the presence of speckle noise in the images and low image contrast. This hinders the effectiveness of traditional edge detection and segmentation methods necessary for parameter estimation. Therefore, addressing these limitations is considered pivotal to progress in this area.The aims of this thesis were therefore to develop new methods for the automatic evaluation of these geometric parameters of the upper extremity, and to compare these with manual evaluations. This was done by addressing all stages of the image processing pipeline, and introducing new methods based on artificial intelligence.Speckle noise of musculoskeletal ultrasound images was reduced by successfully applying local adaptive median filtering and anisotropic diffusion filtering. Furthermore, low contrast of the ultrasound image and video was enhanced by developing a new method based on local fuzzy contrast enhancement. Both steps contributed to improving the quality of musculoskeletal ultrasound images to improve the effectiveness of edge detection methods.Subsequently, a new edge detection method based on the fuzzy inference system was developed to outline the necessary details of the musculoskeletal ultrasound images after image enhancement. This step allowed automated segmentation to be used to estimate the morphological parameters of muscles and tendons in the upper extremity.Finally, the automatically estimated geometric parameters, including the thickness and pennation angle of triceps muscle and the cross-sectional area and circumference of the flexor pollicis longus tendon were compared with manually taken measurements from the same ultrasound images.The results show successful performance of the novel methods in the sample population for the muscles and tendons chosen. A larger dataset would help to make the developed methods more robust and more widely applicable.Future work should concentrate on using the developed methods of this thesis to evaluate other geometric parameters of the upper and lower extremities such as automatic evaluation of the muscle fascicle length