Segmentation is an important step in medical imaging to acquire qualitative measurements such as the location of the desired objects and also for quantitative measurements such as area, volume or the analysis of dynamic behaviour of anatomical structures over time. Among these images, ultrasound images play a crucial role, because they can be produced on video-rate and therefore allows a dynamic analysis of moving structures. In addition, the acquisition of these images is non-invasive, cheap, and does not require ionizing radiations compared to other medical imaging techniques. On the other hand, the automatic segmentation of anatomical structures in ultrasound imagery is a real challenge due to acoustic interferences (speckle noise) and artifacts which are inherent in these images. This paper surveys the literature often recent researches on echocardiography image segmentation methods, focusing on techniques developed for medical. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten recent papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the echocardiography segmentation problem. The contribution of the paper is in three ways: 1) to serve as a tutorial on the field for both clinicians and technologists, 2) to provide an extensive account of segmentation techniques in a comprehensive and systematic manner, and 3) to critically review recent approaches in terms of their performance and degree of clinical evaluation with respect to the final goal of cardiac functional analysis.

The concept of mobile Virtual Reality (VR) headset that utilize mobile smartphone as the main display and processing device has successfully achieved a low-cost but yet feasible method to implement VR for a mobile user. However, locomotion in mobile VR is still a challenge because the ways to interact with the smartphone are limited. Walking-in-place (WIP) can be used as a hands-free input method to control locomotion inside a mobile VR environment, at the same time, enhance user experience by increasing the sense of immersion. Commonly, WIP implementation in mobile VR uses the inertia sensors of smartphone to detect WIP, thus no additional apparatus is needed. However, common WIP implementation navigates based on user’s gaze direction, but in reality, we may walk and look into different direction at the same time. Besides that, fatigue caused by WIP is another important issue to be considered for prolongs usage of VR application. So, we present a less fatigue WIP gesture, swing-in-place (SIP) with a WIP implementation that allows users to travel and look in different directions during locomotion. Our implementation used only the accelerometer and gyroscope in single smartphone. Evaluation results show that SIP able to provide a similar feeling of immersion and it is less fatigue when compared with a common WIP method used in mobile VR. The side-viewing functionality is liked by the users.

Recent development in the field of human–computer interaction has led renewed interest in dynamic hand gesture segmentation based on gesture recognition system. Despite its long clinical success, dynamic hand gesture segmentation using webcam vision becomes technically challenging and suffers the problem of non-accurate and poor hand gesture segmentation where the hand region is not integral due to complicated environment, partial occlusion and light effects. Therefore, for segmenting complete hand gesture region and improving the segmentation accuracy, this study proposes a combination of four modified visual features segmentation procedures, which are skin, motion, skin moving as well as contour features and fast marching method. Quantitative measurement was performed for evaluating hand gesture segmentation algorithm. Besides, qualitative measurement was done to conduct a comparison based on segmentation accuracy with previous studies. Consequently, the experiment results showed a great enhancement in hand area segmentation with a high accuracy rate of 98%.

Medical image fusion is the procedure of combining several images from one or multiple imaging modalities. In spite of numerous attempts in direction of automation ventricle segmentation and tracking in echocardiography, due to low quality images with missing anatomical details or speckle noises and restricted field of view, this problem is a challenging task. This paper presents a fusion method which particularly intends to increase the segment-ability of echocardiography features such as endocardial and improving the image contrast. In addition, it tries to expand the field of view, decreasing impact of noise and artifacts and enhancing the signal to noise ratio of the echo images. The proposed algorithm weights the image information regarding an integration feature between all the overlapping images, by using a combination of principal component analysis and discrete wavelet transform. For evaluation, a comparison has been done between results of some well-known techniques and the proposed method. Also, different metrics are implemented to evaluate the performance of proposed algorithm. It has been concluded that the presented pixel-based method based on the integration of PCA and DWT has the best result for the segment-ability of cardiac ultrasound images and better performance in all metrics.

A FAST AND ACCURATE METHOD FOR AUTOMATIC CORONARY ARTERIAL TREE EXTRACTION IN ANGIOGRAMS