William Hulbert

Speckle reduction of medical ultrasound using Compressive Re-Sampling and instantaneous SNR

Medical Ultrasonography is a valuable imaging technology for medical diagnostics and to guide interventional procedures. However, ultrasound imaging suffers from speckle noise, an inherent characteristic of all coherent imaging techniques due to the presence of sub-resolution scatterers. Speckle noise produces a reduction in contrast resolution which is responsible for the overall lower effective resolution of ultrasound compared to x-ray or MRI imaging. In the case of breast imaging, ultrasound speckle can mask small details such as low contrast tumors or microcalcifications, which may be an early indication of breast cancer. This limitation prevents ultrasound from displacing mammography as the gold standard for breast cancer screening. Traditional speckle reduction techniques attempt to remove speckle noise while preserving edges and other important features but there is always a tradeoff between removing the speckle noise and blurring tissue structure and details. We introduce a novel speckle reduction and contrast enhancement method for ultrasound imaging that is motivated by the fundamental ideas behind compressive sampling. We also introduce a way to estimate instantaneous SNR in order to identify the areas that are mostly signal from the areas that are mostly noise in order to preserve the signal while suppressing the noise. We have shown improvements in SNR on the order of 12dB in the lab and improved visualization of clinical data.

Speckle reduction using stepped-frequency continuous wave ultrasound

Medical Ultrasonography is a valuable imaging technology for medical diagnostics and to guide interventional procedures. Ultrasound imaging is particularly useful in breast cancer detection and diagnosis for women with dense breast tissue where traditional mammography may fail to detect suspicious areas. However, ultrasound imaging suffers from speckle noise, an inherent characteristic of all coherent imaging techniques due to the presence of sub-resolution scatterers. Speckle noise produces a reduction in contrast resolution which is responsible for the overall lower effective resolution of ultrasound compared to x-ray or MRI imaging. In the case of breast imaging, ultrasound speckle can mask small details such as low contrast tumors or micro-calcifications, which may be an early indication of breast cancer. This limitation prevents ultrasound from displacing mammography as the gold standard for breast cancer screening. In conventional pulsed ultrasound imaging systems, de-noising techniques are used to minimize the effect of speckle noise. However, research shows that there is a tradeoff between the effectiveness of speckle reduction techniques and image resolution. We introduce stepped-frequency continuous wave ultrasound imaging which provides a framework where speckle reduction techniques are particularly effective, resulting in higher quality images with an improved SNR and significantly lower speckle noise while maintaining the spatial resolution of the original scan so that small lesions of interest are visible to the radiologist.

Decision quality support in diagnostic breast ultrasound through artificial Intelligence

Medical Ultrasonography is a valuable imaging technology for medical diagnostics and, more recently, as a screening alternative to mammography for women with dense breasts. However, ultrasound imaging within the contexts of both diagnostic and screening mammography suffers from inter-operator and intra-operator variability. Consequently, there is a broad distribution of performance profiles, even for radiologists of similar training. Typically, these profiles tend to err on the side of caution, preferring false positive errors to false negative errors. While this approach may lead to a higher Cancer Detection Rate (CDR), it also lowers the Positive Predictive Value (PPV3) of performed biopsies. A lower PPV3 translates to an increase in benign biopsies, the annual cost of which are estimated to be on the order of

Face recognition in a tactical environment

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Face recognition using a pictorial-edit distance

3 billion USD (not including pathological workups). And, of course, there is the immeasurable cost of pain, worry, and suffering borne by women undergoing these potentially unnecessary procedures. In this paper, we evaluate the ability of the ClearView cCAD algorithms to increase overall performance and reduce the inter-operator variance on a set of imaged lesions. The cCAD system provides an automated assessment of some ACR BI-RADs criteria and calculates a preliminary BI-RADs assessment, given as BI-RADS categorical bucket (1–3) or (4–5). Through the evaluation of 1300 breast lesion images, 3 MQSA certified radiologists were asked to determine both a Likelihood of Malignancy (LoM) and a BI-RADs assessment, from which their ROC curve AUC as well as PPV3 could be calculated. The cCAD system was also evaluated, on the same set of lesions, by a similar set of metrics. From this analysis we have been able to show that the cCAD system outperforms radiologists at all operating points within the scope of this study design. Furthermore, weve shown that through simple fusion schemes we are able to increase performance beyond that of either the cCAD system or the radiologist alone by all typically tracked quality metrics, and significantly reduce inter-operator variance.

Preprocessing for improved computer aided detection in medical ultrasound

Recently, a face recognition algorithm has been proposed that has been designed to work in uncontrolled settings with non-cooperative subjects. This technique is particularly useful for access control in a tactical setting due to ease of use. The method offers secure access control while reducing the cognitive load on the warfighter so that he can concentrate on the mission. The method works with standard webcams and can be used for logon and screen lock capabilities. This method can be incorporated into a multi -factor solution where smart card and other biometric modalities can be added for enhanced security. This technology can also be applied to stand-off applications or surveillance type scenarios where the warfighter can capture a video stream from a local village and process it against a watch list. The face recognition algorithm can recognize the individual under varying conditions that may be found in such an application such as changes in illumination, weather, facial position and expression and other degradations. One of the key features of this system is an automatic image quality and pose estimation scheme. This allows the capture of a video stream followed by automatic extraction of the key frame or frames that contain the best quality faces for recognition. This can be used to filter the video stream and transmit only the information necessary to identify the subject of interest to a central server that contains the watch list information in a secure environment. This is especially important for tactical network environments where bandwidth is at a premium and transmission of high bitrate video streams may not be feasible.

Face recognition for uncontrolled environments

A new face recognition algorithm is introduced that is based on finding an optimal mapping between two images and measuring the properties of the mapping to determine the similarity between the two images. This new method is shown to be extremely robust to variations in pose, camera position and lighting for the problem of face recognition especially in an uncontrolled environment such as video surveillance. The technique does not depend on locating or training on facial features and can be applied in general to any object recognition problem. We present recognition results on current databases available to the research community and will demonstrate a real-time version of the software for access control and surveillance applications at the conference.

System and method for noise reduction and signal enhancement of coherent imaging systems

Recently, a new speckle noise reduction and contrast enhancement technique has been introduced that is motivated by the research in compressive sampling or sensing. Compressive sampling is based on the principle that a sparse signal such as ultrasound can be fully recovered when sampled below the Nyquist rate. This allows for a new noise reduction technique that preserves the high frequency and fine details while reducing the effects of speckle noise. This method improves the overall perceptual quality of the image for visualization and diagnosis by the radiologist. This paper examines how the improvement in SNR makes the method suitable as a preprocessor to improve a computer aided detection (CAD) system for breast cancer detection. Classical performance metrics such as false positive rates, false negative rates and receiver operator curves will be used to show the benefits of this approach. Initial experiments look promising for microcalcification detection, where the new method yields a false negative rate of 20 percent at a false positive rate of 0.5 percent while the traditional speckle reduction techniques yield a false negative rate of 60 percent at a false positive rate of 0.5 percent.

METHOD AND SYSTEM OF COMPUTER-AIDED DETECTION USING MULTIPLE IMAGES FROM DIFFERENT VIEWS OF A REGION OF INTEREST TO IMPROVE DETECTION ACCURACY

A new face recognition algorithm has been proposed which is robust to variations in pose, expression, illumination and occlusions such as sunglasses. The algorithm is motivated by the Edit Distance used to determine the similarity between strings of one dimensional data such as DNA and text. The key to this approach is how to extend the concept of an Edit Distance on one-dimensional data to two-dimensional image data. The algorithm is based on mapping one image into another and using the characteristics of the mapping to determine a two-dimensional Pictorial-Edit Distance or P-Edit Distance. We show how the properties of the mapping are similar to insertion, deletion and substitution errors defined in an Edit Distance. This algorithm is particularly well suited for face recognition in uncontrolled environments such as stand-off and other surveillance applications. We will describe an entire system designed for face recognition at a distance including face detection, pose estimation, multi-sample fusion of video frames and identification. Here we describe how the algorithm is used for face recognition at a distance, present some initial results and describe future research directions.(