Intravascular ultrasound (IVUS) imaging is a diagnostic imaging technique for tomographic visualization of coronary arteries and studying atherosclerotic diseases. These medical images are generally corrupted by multiplicative speckle noise due to the interference of the signal with the backscattered echoes. Speckle noise is an inherent property of medical ultrasound imaging, and it generally tends to reduce the image quality; thus, removing noise from the original medical image is a challenging problem for diagnosis applications. Trying to reduce speckle assists experts for better understanding of some pathologies and diagnosis purposes. Recently, several techniques have been proposed for effective suppression of speckle noise in ultrasound B-Scan images. In this paper, we overview the denoising techniques in both spatial and transform domain, and propose a new despeckling method based on Shearlet transform and ant colony optimization. To quantify the performance improvements of the speckle noise reduction methods, various evaluation criteria in addition to the visual quality of the denoised images are used. Our results showed that in general Shearlet transform with three different types of threshold selection is faster and more efficient than other techniques. In the case of accurate estimation of the variance of noise, Shearlet transform based on ACO yields acceptable results in comparison with others methods. This technique can obtain a favorable signal-to-noise ratio and successfully improve the quality of images and preserve edges and curves as well. Manuscript profile

The data-driven empirical mode decomposition (EMD) method is designed to analyze the non-stationary signals like biomedical signals originating from nonlinear biological systems. EMD analysis produces a local complete separation of the input signal in fast and slow oscillations along with the time-frequency localization. EMD extracts the amplitude and frequency modulated (AM–FM) functions, i.e. the intrinsic mode functions (IMFs), that have been widely used for biomedical signal de-noising, de-trending, feature extraction, compression, and identification. To overcome the problems of EMD, like mode mixing, new generations of EMD have been proposed and applied for biomedical signal analysis. Besides, the bidimensional EMD (BEMD) was introduced and improved for image processing. BEMD and its modified versions have been widely used for medical image de-noising, de-speckling, segmentation, registration, fusion, compression, and classification. In this paper, a review of notable studies in the biomedical signal and image processing based on EMD or BEMD method and their modified versions were considered. The studies on using EMD and its modified versions for mono-dimensional and bidimensional(image) signal processing showed the capabilities of the improved EMD and BEMD methods on biomedical signal and image processing. Manuscript profile