arXiv subjects

Saba Adabi

Publications and source records attributed to Saba Adabi.

6 recordsLinked to original sources

arXiv2020

Non contrast Doppler Microvessel Image Reconstruction by a semi Nonrigid Motion Compensation and Localized Clutter Filtering; a Qualitative and Quantitative Evaluation

Vascular networks can provide invaluable information about tumor angiogenesis. Ultrafast Doppler imaging enables ultrasound to image micro-vessels by applying tissue clutter filtering methods on the Spatio-temporal data obtained from plane-wave imaging. However, motion is an intrinsic part of microvasculature imaging due to various reasons e.g breathing and vessel pulsation. Part of such a motion is taken care of by using Spatio-temporal cluttering filtering. Nonetheless, the remaining part of the motion who manifests itself as blurring or generating ghost vessels should be corrected using another level of motion compensation. We proposed a robust and computationally efficient motion compensation algorithm for Ultrasound micro-vessel imaging. We successfully evaluated the performance of the algorithm by a simulation study. Finally, we tested the proposed motion compensation method on the in vivo data of microvasculature in different organs including breast and thyroid. Results show blurring and ghost vessel problems are significantly reduced using the proposed algorithm. Moreover, our quantitative assessment demonstrated that image correlation among different frames in Ultrafast Doppler imaging is significantly improved utilizing the proposed motion correction method.

physics.med-ph↗

arXiv2018

An Adaptive Cluster-based Filtering Framework for Speckle Reduction of OCT Skin Images

Optical coherence tomography (OCT) has become a favorable device in the Dermatology discipline due to its moderate resolution and penetration depth. OCT images however contain a grainy pattern, called speckle, due to the use of a broadband source in the configuration of OCT. So far, a variety of filtering (de-speckling) techniques is introduced to reduce speckle in OCT images. Most of these methods are generic and can be applied to OCT images of different tissues. The ambition of this work is to provide a de-speckling framework specialized for filtering skin tissues for the community to utilize, adapt or build upon. In this paper, we present an adaptive cluster-based filtering framework, optimized for speckle reduction of OCT skin images. In this framework, by considering the layered structure of skin, first the OCT skin images are segmented into differentiable layers utilizing clustering algorithms, and then each cluster is de-speckled individually using adaptive filtering techniques. In this study, hierarchical clustering algorithm and adaptive Wiener filtering technique are utilized to develop the framework. The proposed method is tested on optical solid phantoms with predetermined optical properties. The method is also tested on healthy human skin images. The results show that the proposed cluster-based filtering method can effectively reduce the speckle and increase the signal-to-noise ratio and contrast while preserving the edges in the image. The proposed cluster-based filtering framework enables researchers to develop unsupervised learning solutions for de-speckling OCT skin images using adaptive filtering methods, or extend the framework to new applications.

cs.CV↗

arXiv2018

Linear-Array Photoacoustic Imaging Using Minimum Variance-Based Delay Multiply and Sum Adaptive Beamforming Algorithm

In Photoacoustic imaging (PA), Delay-and-Sum (DAS) beamformer is a common beamforming algorithm having a simple implementation. However, it results in a poor resolution and high sidelobes. To address these challenges, a new algorithm namely Delay-Multiply-and-Sum (DMAS) was introduced having lower sidelobes compared to DAS. To improve the resolution of DMAS, a novel beamformer is introduced using Minimum Variance (MV) adaptive beamforming combined with DMAS, so-called Minimum Variance-Based DMAS (MVB-DMAS). It is shown that expanding the DMAS equation results in multiple terms representing a DAS algebra. It is proposed to use the MV adaptive beamformer instead of the existing DAS. MVB-DMAS is evaluated numerically and experimentally. In particular, at the depth of 45 mm MVB-DMAS results in about 31 dB, 18 dB and 8 dB sidelobes reduction compared to DAS, MV and DMAS, respectively. The quantitative results of the simulations show that MVB-DMAS leads to improvement in full-width-half-maximum about 96 %, 94 % and 45 % and signal-to-noise ratio about 89 %, 15 % and 35 % compared to DAS, DMAS, MV, respectively. In particular, at the depth of 33 mm of the experimental images, MVB-DMAS results in about 20 dB sidelobes reduction in comparison with other beamformers.

eess.SP↗

arXiv2018

Double-Stage Delay Multiply and Sum Beamforming Algorithm: Application to Linear-Array Photoacoustic Imaging

Photoacoustic imaging (PAI) is an emerging medical imaging modality capable of providing high spatial resolution of Ultrasound (US) imaging and high contrast of optical imaging. Delay-and-Sum (DAS) is the most common beamforming algorithm in PAI. However, using DAS beamformer leads to low resolution images and considerable contribution of off-axis signals. A new paradigm namely Delay-Multiply-and-Sum (DMAS), which was originally used as a reconstruction algorithm in confocal microwave imaging, was introduced to overcome the challenges in DAS. DMAS was used in PAI systems and it was shown that this algorithm results in resolution improvement and sidelobe degrading. However, DMAS is still sensitive to high levels of noise, and resolution improvement is not satisfying. Here, we propose a novel algorithm based on DAS algebra inside DMAS formula expansion, Double Stage DMAS (DS-DMAS), which improves the image resolution and levels of sidelobe, and is much less sensitive to high level of noise compared to DMAS. The performance of DS-DMAS algorithm is evaluated numerically and experimentally. The resulted images are evaluated qualitatively and quantitatively using established quality metrics including signal-to-noise ratio (SNR), full-width-half-maximum (FWHM) and contrast ratio (CR). It is shown that DS-DMAS outperforms DAS and DMAS at the expense of higher computational load. DS-DMAS reduces the lateral valley for about 15 dB and improves the SNR and FWHM better than 13% and 30%, respectively. Moreover, the levels of sidelobe are reduced for about 10 dB in comparison with those in DMAS.

eess.SP↗

arXiv2017

A Learnable Despeckling Framework for Optical Coherence Tomography Images

Optical coherence tomography (OCT) is a prevalent, interferometric, high-resolution imaging method with broad biomedical applications. Nonetheless, OCT images suffer from an artifact, called speckle which degrades the image quality. Digital filters offer an opportunity for image improvement in clinical OCT devices where hardware modification to enhance images is expensive. To reduce speckle, a wide variety of digital filters have been proposed, selecting the most appropriate filter for each OCT image/image set is a challenging decision. To tackle this challenge, we propose an expandable learnable despeckling framework, we called LDF. LDF decides which speckle reduction algorithm is most effective on a given image by learning a figure of merit (FOM) as a single quantitative image assessment measure. The architecture of LDF includes two main parts: (i) an autoencoder neural network, (ii) filter classifier. The autoencoder learns the figure of merit based on the quality assessment measures obtained from the OCT image including.Subsequently, the filter classifier identifies the most efficient filter from the following categories: (a) sliding window filters including median, mean, and symmetric nearest neighborhood, (b) adaptive statistical based filters including Wiener, homomorphic Lee, and Kuwahara, and, (c) edge preserved patch or pixel correlation based filters including non-local mean, total variation, and block matching 3D filtering.

physics.med-ph↗

arXiv2017

Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms

Currently, diagnosis of skin diseases is based primarily on visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment. Tools for automated pattern recognition can provide objective information to support clinical decision-making. Noninvasive skin imaging techniques provide complementary information to the clinician. In recent years, optical coherence tomography has become a powerful skin imaging technique. According to specific functional needs, skin architecture varies across different parts of the body, as do the textural characteristics in OCT images. There is, therefore, a critical need to systematically analyze OCT images from different body sites, to identify their significant qualitative and quantitative differences. Sixty-three optical and textural features extracted from OCT images of healthy and diseased skin are analyzed and in conjunction with decision-theoretic approaches used to create computational models of the diseases. We demonstrate that these models provide objective information to the clinician to assist in the diagnosis of abnormalities of cutaneous microstructure, and hence, aid in the determination of treatment. Specifically, we demonstrate the performance of this methodology on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) from healthy tissue.

q-bio.TO↗