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It is necessary to use an ultrasound imaging devices for Radio Frequency Ablation (RFA) of cancers or biopsy on liver, prostate, thyroid, and so on. However, it is very hard to discriminate a small tumor and its surrounding tissues when the tumor has less than a diameter of 1cm. Thus physician refers previously acquired diagnostic MR or CT images during those procedures in common. Furthermore, some medical devices manufacturing companies have released a ultrasound imaging device with fusion imaging technologies which can simultaneously display the same images together the live ultrasound image with the same patient's MR or CT. However, they needs many manual inputs. It has manually registered the coordinates of both MR and ultrasound as the physician needs to manually specify several corresponding points on both MR images and US images. In this paper, we introduced fusion imaging system via just one-click interaction for liver MR to ultrasound image which is the majority of the RFA or biopsy.

This paper suggests a novel technology that can clone a user's facial expression to his avatar in 3D virtual worlds realistically using only one color camera on a smart TV. To do this, the user's 3D head movement information and 3D position of facial feature points are needed in real time. We propose two novel approaches to achieve this. Firstly, we use a personalized 3D and 2D facial expression model to deal with head movement and various expressions. Secondly, we use a facial muscle model to generate natural motion of facial feature points located in cheeks and forehead which are difficult to be tracked using a camera. Experimental results demonstrate that the proposed method would be an efficient technique to perform realistic 3D facial expression cloning.

High Intensity Focused Ultrasound (HIFU) has gained considerable interest for clinical applications in recent decades due to its noninvasive characteristics. However current HIFU systems have limitations due to treatment times. Specifically it takes about two to three hours for the treatment of large tumors such as uterine fibroids. Optimal treatment with reduced time and costs as well as enhanced safety need fundamental innovation in HIFU technologies like transducer architecture and beam focusing to enable fast ablation of large tumors. The ultimate purpose of this research is the evaluation of the feasibility of rapid HIFU treatment of large-sized tumors. To enable this, new multi-element HIFU transducer architecture, which can generate multiple foci simultaneously with minimal grating lobes, has been designed and evaluated. Adopting the proposed transducer architecture provides a 70% reduction of RF driving channels but retains similar sonication effect with the full array. This might reduce the number of RF driving channels and cut down the cost.

In this study, the optimization framework to seamlessly integrate simulation process for computer simulations which perform from transducer array designs to ultrasound therapy is proposed. The optimization framework consists of three processes: a) design of a random transducer array, b) design of multi-focus patterns and a sparse array, and c) treatment planning of scanning time and path. In an attempt to tackling time-intensive computations, the 3D acoustic pressure expressed by the Rayleigh-Sommerfeld diffraction integral was numerically calculated with the aid of a distributed computing method. Genetic algorithms which were entirely used from the 1st process to the 3rd process also utilized the distributed computing method to speed up the calculation time. For the thermal dose which can describe the tissue damage, the tissue temperature evolution over time was obtained by solving Pennes' bioheat transfer equation on GPUs using NVIDIA's CUDA environment. The proposed optimization framework based on commercial software Isight 5.6 was implemented on the high performance computing facility. With this framework, a 1017-element spherical-section ultrasound phased array was developed and the high speed and safety of focused ultrasound thermal ablation was finally achieved.

Respiratory motion monitoring is important to deal with tumor tracking in radiotherapy and imaging. In this paper, we propose a system that measures directly the motion of organ itself only using ultrasound image. Our system locates robustly diaphragm even though diaphragm may be blurred in ultrasound images by a fitting technique. In addition, it can extract robustly respiratory signal by automatically selecting the best area to monitor respiratory area in all image sequences.

Image-guided therapy is the treatment technology which uses medical images to understand anatomic information on human body and make medical treatment plan. Such treatment includes radio frequency (RF) ablation, high intensity focused ultrasound (HIFU), cyber knife, surgical robot, etc. Especially, ultrasound (US) guided HIFU therapy has attracted much attention since it treats internal organs without giving any radiation dose to human body. However, it is still challengeable to track 3D position of tumors of moving organ from 2D us videos since respiration makes change on the position and shape of the internal organs such as liver, kidney or pancreas. The paper proposes a moving organ tracking method based on 3D organ model. The 3D organ model is made from MR or CT images captured before treatment. The 3D organ model is registered to US images, and the organ and its tumor's position are tracked in 2D US videos in real time. We apply the method on a liver phantom and get 5.3ms as the processing time and 1.3 mm as the accuracy.

The interest for the registration between 2D ultrasound (US) and preoperative CT or MR images has been growing for US-guided diagnosis, intervention, and surgery. In our previous study, we proposed a real-time and automatic registration system between two images of the liver without any help of positioning sensors. We have confirmed that the system can provide an accurate and reliable registration performance, if sufficient features are included in a current US image. In this paper, we propose a robust position estimation system of a moving liver lesion based on the previously proposed registration system. The system indirectly but reliably estimates the lesion position by registering a US image including sufficient features to 4D MR images, even if the US image does not include the target lesion. The proposed system is evaluated on three clinical datasets, through both qualitative and quantitative evaluations. Experimental results show that the registration error for target lesions is less than 5 mm on average, which is considered acceptable for many clinical applications.

This paper presents a new 6 DOF motion tracking architecture which can be utilized in wide application area. Based on spatial IR intensity attenuation characteristics, 3D position and orientation of the device are estimated accurately in real-time, and its performance is more improved by inertial sensor fusion. The proposed system can be constructed with low-cost sensors, and it has advantageous architecture for downsizing without any deterioration of its performance.

A low-cost, absolute pointing device for the TV exploiting the directivity of LEDs is presented. The main technical issues to solve were 1) the irregularity of the directivity of LEDs, 2) susceptibility to external infrared noise, and 3) instability due to hand tremor. The efficiency of the prototype was evaluated in comparison with GyroPoint and Wii remote.

Motion sensing devices enable users to interact with large virtual workspaces with small screens since relative motion can be an effective navigation technique to access off-screen contents. This paper proposes a cost-effective orientation sensing technology for pan-tilt motion interfaces of a mobile device, utilizing only a tri-axis accelerometer and a dual-axis angular rate sensor.

The recent online networked virtual worlds such as SecondLife, World of Warcraft and Lineage have been increasingly popular. A life-scale virtual world presentation and the intuitive interaction between the users and the virtual worlds would provide more natural and immersive experience for users. The emergence of novel interaction technologies such as sensing the facial expression and the motion of the users and the real world environments could be used to provide a strong connection between them. For the wide acceptance and use of the virtual world, a various type of novel interaction devices should have a unified interaction formats between the real world and the virtual world and interoperability among virtual worlds. Thus, MPEG-V Media Context and Control (ISO/IEC 23005) standardizes such connecting information. The paper provides an overview and its usage example of MPEG-V from the real world to the virtual world (R2V) on interfaces for controlling avatars and virtual objects in the virtual world by the real world devices. In particular, we investigate how the MPEG-V framework can be applied for the facial animation of an avatar in various types of virtual worlds.

In this paper, we propose gesture-based interaction methods using a tri-axis accelerometer for mobile devices. The algorithms for the gesture recognition and the shaking detection are suggested for gesture interaction. They have been implemented in the commercialized mobile phone (model name: SCH-S310). The mobile phone recognizes digits from 1 to 9 and five symbols written in the air. It detects the shaking motion to initiate commands for the gesture interactive games and musical instrument applications. We used the signals from a tri-axis accelerometer to recognize the gestures directly without trajectory estimation. In the experimental study, we achieved 97.01% of average recognition rate for a set of eleven gestures. The rate is cross validated from a whole data set of 3082 gestures from 100 users. The performance of the shaking detection methods shows that gesture-based interaction can be used as input methods of games and musical instrument applications for entertainment

In this paper, we present a novel gesture-based input device by using inertial sensing technique. The trajectories of user's hand-drawn gestures in 3D space are captured and recognized by this device to fulfill user interaction task. The proposed device employs gyro-free inertial measurement unit (IMU) to track hand motions without requiring any external reference sensors or signals. Since the unbounded growing error of trajectory estimation, as a major drawback of IMU-based motion tracking technology, is carefully solved by using zero velocity compensation. Here, a deliberately-designed motion detection scheme is proposed to capture accurate hand motion period. Finally, the recognition algorithm based on Bayesian networks takes estimated trajectories and finds the corresponding gesture model with the maximum probability. Because the IMU provides outstanding capability of self-contained positioning, the proposed device is extraordinary simple and effective, comparing with the devices using other tracking technologies such as vision-based system. Experimental results also show its effectiveness and feasibility. Currently, after employing the trajectory estimation method provided in this paper, the recognition rate of 95.51% for 14 gestures has been achieved when this device is implemented as a TV remote controller. It can be used as a powerful, flexible interface for ubiquitous computing device.

This paper presents a gesture input device, magic wand, with which a user can input gestures in 3-D space, inertial sensors embedded in it generate acceleration and angular velocity signals according to a user's hand movement. A trajectory estimation algorithm is employed to convert them into a trajectory on 2-D plane. The recognition algorithm based on Bayesian networks finds the gesture model with the maximum likelihood from it. The recognition performance of the proposed system is quite promising; the writer-independent recognition rate was 99.2% on average for the database of 15 writers and 13 gesture classes.

We present a 3-D input medium based on inertial sensors for on-line character recognition and an ensemble classification scheme for the recognition task. The system allows user to write a character in the air as a gesture, with a sensor-embedded device held in hand. The kinds of sensors used are 3-axis accelerometer and 3-axis gyroscope generating acceleration and angular velocity signals respectively. For character recognition, we used the technique of FDA (Fisher discriminant analysis). We tried different combinations of sensor signals to test the recognition performance. It is also possible to estimate a 2-D handwriting trajectory from the sensor signals. The best recognition rate of 93.23%, in case we use only raw sensor signals, was attained when all 6 sensor signals were combined. The recognition rate of 92.22% was reached if the estimated trajectory was used as input. Finally we tested the ensemble method and the generalization rate of 95.04% was attained on the ensemble recognizer consisting of 3 FDA recognizers based on acceleration-only, angular-velocity-only and handwriting trajectory respectively.

In this paper, we present a pen-shaped input device forwearable computers which reproduce and recognizethree-dimensional (3D) hand motions with no externalreferences. The input device is equipped with inertialsensors. The inertial sensors measure accelerations andangular velocities produced by a user's handwritingmotion in 2D/3D spaces. The measurements fromgyroscopes are integrated once to produce the attitude ofthe system and are consequently used to remove theeffects of the gravity and the inclination of the system.The compensated acceleration measurements are doublyintegrated to yield the position of the system. Due to theintegration processes involved in reproducing thehandwriting trajectory, the accuracy of the positionmeasurement significantly deteriorates along with time.To reproduce handwriting trajectory of a stroke on asurface and even in the air, we propose algorithms tosegment a stroke, to compensate the integration errors,and to reconstruct 2D trajectory on the x-y plane from 3Dtrajectory in the air. Real experimental results areincluded to show the effectiveness and feasibility of theproposed algorithms. The proposed methods provide anew approach for implementing a small, user-friendlykeyboard alternative input device for wearable computers.

Here, a pen-type input device is proposed to track trajectories in 3D space by using accelerometers and gyroscopes. Inertial navigation system (INS) theory, as an autonomous positioning technology widely adopted, is employed to track the users' handwriting motion without external reference sensors or signals. The INS-based positioning system, however, suffers from unbounded error, which grows with time due to the integration process involved. In order to solve this problem, the system error model is built to analyze the influence of error sources. Then efficient error compensation algorithm based on zero velocity compensation (ZVC) is developed to reduce the effect of identified errors. The experiments show the effectiveness and feasibility of the proposed method.

Reports some early results of our study on continuous Korean sign language (KSL) recognition using color vision. In recognizing gesture words such as sign language, it is very difficult to segment a continuous sign into individual sign words since the patterns are very complicated and diverse. To solve this problem, we disassemble the KSL into 18 hand motion classes according to their patterns and represent the sign words as some combination of hand motions. Observing the speed and the change of speed of hand motion and using fuzzy partitioning and state automata, we reject unintentional gesture motions such as preparatory motion and meaningless movement between sign words. To recognize 18 hand motion classes we adopt the hidden Markov model. Using these methods, we recognize 15 KSL sentences and obtain 94% recognition ratio.

A high quality deflection yoke (DY) is an essential factor for a high quality monitor. Currently, the DY adjustment process is done manually by attaching ferrite sheets on the inside surface of the DY. In this paper, we deal with the convergence adjustment algorithms employed in the guidance system. An inference engine that is based on soft computing techniques is proposed to systematically deal with several resources including the expert's knowledge in the convergence adjustment process of the DY. In our approach, the rough set theory is used to handle the input/output data collections of the DY adjustment, and the fuzzy logic and evolutionary programming are used to model the plant and to tune the model. With the given initial convergence, a rough set driven coarse search part finds a coarse area and then more precise positions are searched and decided in the fuzzy inference engine driven fine search part. Initial experimental results show that the proposed algorithm works well in the real convergence adjustment process of the DY.