Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a standardized diagnostic viewing plane with minimal artifacts and can provide additional information for other automatic image analysis algorithms. However, automatically detecting shadow regions is challenging because pixel-wise annotation of acoustic shadows is subjective and time consuming. In this paper we propose a weakly supervised method for automatic confidence estimation of acoustic shadow regions, which is able to generate a dense shadow-focused confidence map. During training, a multi-task module for shadow segmentation is built to learn general shadow features according based image-level annotations as well as a small number of coarse pixel-wise shadow annotations. A transfer function is then established to extend the binary shadow segmentation to a reference confidence map. In addition, a confidence estimation network is proposed to learn the mapping between input images and the reference confidence maps. This confidence estimation network is able to predict shadow confidence maps directly from input images during inference. We evaluate DICE, soft DICE, recall, precision, mean squared error and inter-class correlation to verify the effectiveness of our method. Our method outperforms the state-of-the-art qualitatively and quantitatively. We further demonstrate the applicability of our method by integrating shadow confidence maps into tasks such as ultrasound image classification, multi-view image fusion and automated biometric measurements.
* Automatic US shadow detection Acoustic shadows have a significant impact on US image quality, and thus a serious effect on robustness and accuracy of image processing methods. In clinical literature, US artifacts including shadows have been well studied and reviewed @cite_17 @cite_24 @cite_33 . However, the shadow problem is not well covered in automated US image analysis literature. Anatomically estimating acoustic shadow has rarely been the focus within the medical image analysis community as it is a challenging task.
@cite_24 @cite_33 @cite_17
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Automatically detecting acoustic shadows is of great importance for automatic 2D ultrasound analysis ranging from anatomy segmentation to landmark detection. However, variation in shape and similarity in intensity to other structures make shadow detection a very challenging task. In this paper, we propose an automatic shadow detection method to generate a pixel-wise, shadow-focused confidence map from weakly labelled, anatomically-focused images. Our method: (1) initializes potential shadow areas based on a classification task. (2) extends potential shadow areas using a GAN model. (3) adds intensity information to generate the final confidence map using a distance matrix. The proposed method accurately highlights the shadow areas in 2D ultrasound datasets comprising standard view planes as acquired during fet al screening. Moreover, the proposed method outperforms the state-of-the-art quantitatively and improves failure cases for automatic biometric measurement. In ultrasound images, acoustic shadows appear as regions of low signal intensity linked to boundaries with very high acoustic impedance differences. Acoustic shadows can be viewed either as informative features to detect lesions or calcifications, or as damageable artifacts for image processing tasks such as segmentation, registration or 3D reconstruction. In both cases, the detection of these acoustic shadows is useful. This paper proposes a new method to detect these shadows that combines a geometrical approach to estimate the B-scans shape, followed by a statistical test based on a dedicated modeling of ultrasound image statistics. Results demonstrate that the combined geometrical-statistical technique is more robust and yields better results than the previous statistical technique. Integration of regularization over time further improves robustness. Application of the procedure results in (1) improved 3D reconstructions with fewer artifacts, and (2) reduced mean registration error of tracked intraoperative brain ultrasound images. We present a survey of techniques for the reduction of streaking artefacts caused by met allic objects in X-ray Computed Tomography (CT) images. A comprehensive review of the existing state-of-theart Met al Artefact Reduction (MAR) techniques, drawn predominantly from the medical CT literature, is supported by an experimental comparison of twelve MAR techniques. The experimentation is grounded in an evaluation based on a standard scientific comparison protocol for MAR methods, using a software generated medical phantom image as well as a clinical CT scan. The experimentation is extended by considering novel applications of CT imagery consisting of met al objects in non-tissue surroundings acquired from the aviation security screening domain. We address the shortage of thorough performance analyses in the existing MAR literature by conducting a qualitative as well as quantitative comparative evaluation of the selected techniques. We find that the difficulty in generating accurate priors to be the predominant factor limiting the effectiveness of the state-of-the-art medical MAR techniques when applied to non-medical CT imagery. This study thus extends previous works by: comparing several state-of-the-art MAR techniques; considering both medical and non-medical applications and performing a thorough performance analysis, considering both image quality as well as computational demands. Abstract. Shadow detection and removal in real scene images is always a challenging but yet intriguing problem. In contrast with the rapidly expanding and continuous interests on this area, the authors are unaware of any comprehensive surveys on this topic. This paper aimed to give a comprehensive and critical survey of current shadow detection and removal methods. Algorithms are categorized into there sets by their different functions and assumptions about the scenes. A discussion of reasonable evaluation is given at the end of this survey. 1 Introduction Shadows and shadings in images have long been disruptive to computer vision algorithms. They appear as surface features, when in fact they are caused by the interaction between light and objects. This may lead to problems in scene understanding, object segmentation, tracking, recognition, etc. Because of the undesirable effects of shadows on image analysis, much attention was paid to the area of shadow detection and removal over the past decades and covered many specific applications such as traffic surveillance [1, 2], face recognition [3, 4, 5] and image segmentation [6]. In spite of these extensive studies, more researches focus on providing a general method for arbitrary scene images and thereby obtaining “visually pleasing” shadow free images. In contrast with the rapidly expanding interests on shadow removal, no comprehensive survey is reported on this particular topic. A list of recent work on this area is reported in [7] but algorithm details are missed. A survey was conducted by A. Prati [8, 9] on the moving cast shadow detection, which is the part of current interests. This paper aims to give a relatively comprehensive study on the current methods of detecting and removing shadows in both still and moving images. Before going into the detailed algorithms, we first review some different kinds of shadows in natural scenes.