Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks

Cardiovascular disease (CVD) is the leading cause of mortality yet largely preventable, but the key to prevention is to identify at-risk individuals before adverse events. For predicting individual CVD risk, carotid intima-media thickness (CIMT), a noninvasive ultrasound method, has proven to be valuable, offering several advantages over CT coronary artery calcium score. However, each CIMT examination includes several ultrasound videos, and interpreting each of these CIMT videos involves three operations: (1) select three end-diastolic ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI) in each selected frame, and (3) trace the lumen-intima interface and the media-adventitia interface in each ROI to measure CIMT. These operations are tedious, laborious, and time consuming, a serious limitation that hinders the widespread utilization of CIMT in clinical practice. To overcome this limitation, this paper presents a new system to automate CIMT video interpretation. Our extensive experiments demonstrate that the suggested system significantly outperforms the state-of-the-art methods. The superior performance is attributable to our unified framework based on convolutional neural networks (CNNs) coupled with our informative image representation and effective post-processing of the CNN outputs, which are uniquely designed for each of the above three operations.Comment: J. Y. Shin, N. Tajbakhsh, R. T. Hurst, C. B. Kendall, and J. Liang. Automating carotid intima-media thickness video interpretation with convolutional neural networks. CVPR 2016, pp 2526-2535; N. Tajbakhsh, J. Y. Shin, R. T. Hurst, C. B. Kendall, and J. Liang. Automatic interpretation of CIMT videos using convolutional neural networks. Deep Learning for Medical Image Analysis, Academic Press, 201

An approach to a pseudo real-time image processing engine for hyperspectral imaging

Hyperspectral imaging provides an alternative way of increasing the accuracy by adding another dimension: the wavelength. Recently, hyperspectral imaging is also finding its way into many more applications, ranging from medical imaging in endoscopy for cancer detection to quality control in the sorting of fruit and vegetables. But effective use of hyperspectral imaging requires an understanding of the nature and limitations of the data and of various strategies for processing and interpreting it. Also, the breakthrough of this technology is limited by its cost, speed and complicated image interpretation. We have therefore initiated work on designing real-time hyperspectral image processing to tackle these problems by using a combination of smart system design, and pseudo-real time image processing software. The main focus of this paper is the development of a camera-based hyperspectral imaging system for stationary remote sensing applications. The system consists of a high performance digital CCD camera, an intelligent processing unit, an imaging spectrograph, an optional focal plane scanner and a laptop computer equipped with a frame grabbing card. In addition, special software has been developed to synchronize between the frame grabber (video capture card), and the digital camera with different image processing techniques for both digital and hyperspectral data

Spike Processing on an Embedded Multi-task Computer: Image Reconstruction

There is an emerging philosophy, called Neuro-informatics, contained in the Artificial Intelligence field, that aims to emulate how living beings do tasks such as taking a decision based on the interpretation of an image by emulating spiking neurons into VLSI designs and, therefore, trying to re-create the human brain at its highest level. Address-Event-Representation (AER) is a communication protocol that has embedded part of the processing. It is intended to transfer spikes between bioinspired chips. An AER based system may consist of a hierarchical structure with several chips that transmit spikes among them in real-time, while performing some processing. There are several AER tools to help to develop and test AER based systems. These tools require the use of a computer to allow the higher level processing of the event information, reaching very high bandwidth at the AER communication level. We propose the use of an embedded platform based on a multi-task operating system to allow both, the AER communication and processing without the requirement of either a laptop or a computer. In this paper, we present and study the performance of a new philosophy of a frame-grabber AER tool based on a multi-task environment. This embedded platform is based on the Intel XScale processor which is governed by an embedded GNU/Linux system. We have connected and programmed it for processing Address-Event information from a spiking generator.Ministerio de Educación y Ciencia TEC2006-11730-C03-0

Multi-frame image restoration method for novel rotating synthetic aperture imaging system

Abstract The novel rotating synthetic aperture (RSA) optical imaging system is an important development direction for future high-resolution optical remote sensing satellites in geostationary orbit. However, owing to the rotating rectangular pupil, the point spread function of the RSA system has an asymmetric spatial distribution, and the images obtained using the primary mirror from different rotation angles have nonuniform blur degradation. Moreover, platform vibration and pupil rotation have coupling effects on the RSA imaging, resulting in further radiometric and geometric quality degradation. To address these problems, the image degradation characteristics are first analyzed according to the imaging mechanism. Then, combined with the theory of mutual information, an image registration method is suggested by introducing the orientation gradient information. From this, a multi-frame image restoration model is proposed based on the directional gradient prior of the RSA system image. From the perspective of interpretation and application, when the aspect ratio is less than 3, the proposed inversion restoration method can achieve a satisfactory processing performance. This work can provide engineering application reference for the future space application of RSA imaging technology

Computer Forensics Field Triage Process Model

With the proliferation of digital based evidence, the need for the timely identification, analysis and interpretation of digital evidence is becoming more crucial. In many investigations critical information is required while at the scene or within a short period of time - measured in hours as opposed to days. The traditional cyber forensics approach of seizing a system(s)/media, transporting it to the lab, making a forensic image(s), and then searching the entire system for potential evidence, is no longer appropriate in some circumstances. In cases such as child abductions, pedophiles, missing or exploited persons, time is of the essence. In these types of cases, investigators dealing with the suspect or crime scene need investigative leads quickly; in some cases it is the difference between life and death for the victim(s). The Cyber Forensic Field Triage Process Model (CFFTPM) proposes an onsite or field approach for providing the identification, analysis and interpretation of digital evidence in a short time frame, without the requirement of having to take the system(s)/media back to the lab for an in-depth examination or acquiring a complete forensic image(s). The proposed model adheres to commonly held forensic principles, and does not negate the ability that once the initial field triage is concluded, the system(s)/storage media be transported back to a lab environment for a more thorough examination and analysis. The CFFTPM has been successfully used in various real world cases, and its investigative importance and pragmatic approach has been amply demonstrated. Furthermore, the derived evidence from these cases has not been challenged in the court proceedings where it has been introduced. The current article describes the CFFTPM in detail, discusses the model’s forensic soundness, investigative support capabilities and practical considerations

Paper Session II: Computer Forensics Field Triage Process Model

With the proliferation of digital based evidence, the need for the timely identification, analysis and interpretation of digital evidence is becoming more crucial. In many investigations critical information is required while at the scene or within a short period of time - measured in hours as opposed to days. The traditional cyber forensics approach of seizing a system(s)/media, transporting it to the lab, making a forensic image(s), and then searching the entire system for potential evidence, is no longer appropriate in some circumstances. In cases such as child abductions, pedophiles, missing or exploited persons, time is of the essence. In these types of cases, investigators dealing with the suspect or crime scene need investigative leads quickly; in some cases it is the difference between life and death for the victim(s). The Cyber Forensic Field Triage Process Model (CFFTPM) proposes an onsite or field approach for providing the identification, analysis and interpretation of digital evidence in a short time frame, without the requirement of having to take the system(s)/media back to the lab for an in-depth examination or acquiring a complete forensic image(s). The proposed model adheres to commonly held forensic principles, and does not negate the ability that once the initial field triage is concluded, he system(s)/storage media be transported back to a lab environment for a more thorough examination d analysis. The CFFTPM has been successfully used in various real world cases, and its investigative importance and pragmatic approach has been amply demonstrated. Furthermore, the derived evidence from these cases has not been challenged in the court proceedings where it has been introduced. The current article describes the CFFTPM in detail, discusses the model’s forensic soundness, investigative support capabilities and practical considerations

Perancangan dan Implementasi Penerjemah Bahasa Isyarat dari Video Menjadi Teks Menggunakan Ekstraksi Ciri Histogram dan ART-2

ABSTRAKSI: Nowadays, communication has become a very important thing and cannot be separated from human life. The interaction among humans has become borderless caused by technology development. But, it doesn’t happen to the Deaf. Language constraint is the real border for them to communicate with society normally and freely. The SIBI dictionary which is used in Indonesia now cannot be accessed by everyone; it’s also expensive and thick. Different interpretation might also happen because the explanation and description about Sign Language gestures is not so clear.This research implemented a Sign Language translator system based on video processing, image processing, and neural network ART-2. The recognizing parameter used in this research was hand shape, because almost all words in SIBI dictionary are based on hand shape which shows certain letter. The hand shape was taken from the last moving frame. Then, it would be processed and the features were taken using Histogram feature extraction. Neural network ART-2 was used as the recognizer of each hand shape. Over all, there were some processes done in this research, they are : frame difference to show movement, capturing to get the hand shape, counting the pixel 1 appearance as the feature extraction, and the last was recognizing using ART-2.Output of this research was a system that was able to visualize SIBI dictionary and to recognize each hand shape in the end of movement. The testing result showed that this system was able to recognize Sign Language hand shape with highest accuracy was as much as 100% with total testing videos that were used was 90.Kata Kunci : Sign Language, hand shape, histogram, ART-2ABSTRACT: Nowadays, communication has become a very important thing and cannot be separated from human life. The interaction among humans has become borderless caused by technology development. But, it doesn’t happen to the Deaf. Language constraint is the real border for them to communicate with society normally and freely. The SIBI dictionary which is used in Indonesia now cannot be accessed by everyone; it’s also expensive and thick. Different interpretation might also happen because the explanation and description about Sign Language gestures is not so clear.This research implemented a Sign Language translator system based on video processing, image processing, and neural network ART-2. The recognizing parameter used in this research was hand shape, because almost all words in SIBI dictionary are based on hand shape which shows certain letter. The hand shape was taken from the last moving frame. Then, it would be processed and the features were taken using Histogram feature extraction. Neural network ART-2 was used as the recognizer of each hand shape. Over all, there were some processes done in this research, they are : frame difference to show movement, capturing to get the hand shape, counting the pixel 1 appearance as the feature extraction, and the last was recognizing using ART-2.Output of this research was a system that was able to visualize SIBI dictionary and to recognize each hand shape in the end of movement. The testing result showed that this system was able to recognize Sign Language hand shape with highest accuracy was as much as 100% with total testing videos that were used was 90.Keyword: Sign Language, hand shape, histogram, ART-

Intelligent pre-processing for fast-moving object detection

Detection and segmentation of objects of interest in image sequences is the first major processing step in visual surveillance applications. The outcome is used for further processing, such as object tracking, interpretation, and classification of objects and their trajectories. To speed up the algorithms for moving object detection, many applications use techniques such as frame rate reduction. However, temporal consistency is an important feature in the analysis of surveillance video, especially for tracking objects. Another technique is the downscaling of the images before analysis, after which the images are up-sampled to regain the original size. This method, however, increases the effect of false detections. We propose a different pre-processing step in which we use a checkerboard-like mask to decide which pixels to process. For each frame the mask is inverted to avoid that certain pixel positions are never analyzed. In a post-processing step we use spatial interpolation to predict the detection results for the pixels which were not analyzed. To evaluate our system we have combined it with a background subtraction technique based on a mixture of Gaussian models. Results show that the models do not get corrupted by using our mask and we can reduce the processing time with over 45% while achieving similar detection results as the conventional technique

Machine Analysis of Facial Expressions

No abstract