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Today, medical models can be made by the use of medical imaging systems through modern image processing methods and rapid prototyping (RP) technology. In ultrasound imaging systems, as images are not layered and are of lower quality as compared to those of computerized tomography (CT) and magnetic resonance imaging (MRI), the process for making physical models requires a series of intermediate processes and it is a challenge to fabricate a model using ultrasound images due to the inherent limitations of the ultrasound imaging process. The purpose of this paper is to make high quality, physical models from medical ultrasound images by combining modern image processing methods and RP technology.

A novel and effective semi‐automatic method was developed to improve the quality of 2D image segmentation process. In this new method, a partial histogram of 2D images was used and ideal boundaries were obtained. A 3D model was achieved using the exact boundaries and then the 3D model was converted into the stereolithography (STL) format, suitable for RP fabrication. As a case study, the foetus was chosen for this application since ultrasonic imaging is commonly used for foetus imaging so as not to harm the baby. Finally, the 3D Printing (3DP) and PolyJet processes, two types of RP technique, were used to fabricate the 3D physical models.

The physical models made in this way proved to have sufficient quality and shortened the process time considerably.

It is still a challenge to fabricate an exact physical model using ultrasound images. Current commercial histogram‐based segmentation method is time‐consuming and results in a less than optimum 3D model quality. In this research work, a novel and effective semi‐automatic method was developed to select the threshold optimum value easily.

The purpose of this study is to develop a hybrid algorithm for segmenting tumor from ultrasound images of the liver.

After collecting the ultrasound images, contrast-limited adaptive histogram equalization approach (CLAHE) is applied as preprocessing, in order to enhance the visual quality of the images that helps in better segmentation. Then, adaptively regularized kernel-based fuzzy C means (ARKFCM) is used to segment tumor from the enhanced image along with local ternary pattern combined with selective level set approaches.

The proposed segmentation algorithm precisely segments the tumor portions from the enhanced images with lower computation cost. The proposed segmentation algorithm is compared with the existing algorithms and ground truth values in terms of Jaccard coefficient, dice coefficient, precision, Matthews correlation coefficient, f-score and accuracy. The experimental analysis shows that the proposed algorithm achieved 99.18% of accuracy and 92.17% of f-score value, which is better than the existing algorithms.

From the experimental analysis, the proposed ARKFCM with enhanced level set algorithm obtained better performance in ultrasound liver tumor segmentation related to graph-based algorithm. However, the proposed algorithm showed 3.11% improvement in dice coefficient compared to graph-based algorithm.

The image preprocessing is carried out using CLAHE algorithm. The preprocessed image is segmented by employing selective level set model and Local Ternary Pattern in ARKFCM algorithm. In this research, the proposed algorithm has advantages such as independence of clustering parameters, robustness in preserving the image details and optimal in finding the threshold value that effectively reduces the computational cost.

Speckle noise removal in ultrasound images is one of the important tasks in biomedical-imaging applications. Many filtering -based despeckling methods are discussed in many existing works. Two-dimensional (2-D) transforms are also used enormously for the reduction of speckle noise in ultrasound medical images. In recent years, many soft computing-based intelligent techniques have been applied to noise removal and segmentation techniques. However, there is a requirement to improve the accuracy of despeckling using hybrid approaches.

The work focuses on double-bank anatomy with framelet transform combined with Gaussian filter (GF) and also consists of a fuzzy kind of clustering approach for despeckling ultrasound medical images. The presented transform efficiently rejects the speckle noise based on the gray scale relative thresholding where the directional filter group (DFB) preserves the edge information.

The proposed approach is evaluated by different performance indicators such as the mean square error (MSE), peak signal to noise ratio (PSNR) speckle suppression index (SSI), mean structural similarity and the edge preservation index (EPI) accordingly. It is found that the proposed methodology is superior in terms of all the above performance indicators.

Fuzzy kind clustering methods have been proved to be better than the conventional threshold methods for noise dismissal. The algorithm gives a reconcilable development as compared to other modern speckle reduction procedures, as it preserves the geometric features even after the noise dismissal.

The performance of three frequently used level set-based segmentation methods is examined for the purpose of defining features and boundary conditions for image-based Eulerian fluid and solid mechanics models. The focus of the evaluation is to identify an approach that produces the best geometric representation from a computational fluid/solid modeling point of view. In particular, extraction of geometries from a wide variety of imaging modalities and noise intensities, to supply to an immersed boundary approach, is targeted.

Two- and three-dimensional images, acquired from optical, X-ray CT, and ultrasound imaging modalities, are segmented with active contours, k-means, and adaptive clustering methods. Segmentation contours are converted to level sets and smoothed as necessary for use in fluid/solid simulations. Results produced by the three approaches are compared visually and with contrast ratio, signal-to-noise ratio, and contrast-to-noise ratio measures.

While the active contours method possesses built-in smoothing and regularization and produces continuous contours, the clustering methods (k-means and adaptive clustering) produce discrete (pixelated) contours that require smoothing using speckle-reducing anisotropic diffusion (SRAD). Thus, for images with high contrast and low to moderate noise, active contours are generally preferable. However, adaptive clustering is found to be far superior to the other two methods for images possessing high levels of noise and global intensity variations, due to its more sophisticated use of local pixel/voxel intensity statistics.

It is often difficult to know a priori which segmentation will perform best for a given image type, particularly when geometric modeling is the ultimate goal. This work offers insight to the algorithm selection process, as well as outlining a practical framework for generating useful geometric surfaces in an Eulerian setting.

This paper aims to develop a new robotic ultrasound system for spine imaging with more anthropomorphic scanning manipulation in comparison with previously reported techniques.

The system evaluates the imaging quality of ultrasound (US) B-scans by detecting vertebral landmarks and groups the images with relatively low quality into several sub-optimal types. By imitating the scanning skills of sonographers, the authors defined a set of adjustment strategies for certain sub-optimal types. In this way, the robot can recollect the US images with high quality by adaptively adjusting the pose of the probe like a sonographer.

The results from phantom experiments and in vivo experiments showed that the proposed method could improve the quality of B-scans during the scanning. The 3 D US volume reconstruction has also verified the feasibility of the proposed method.

This paper demonstrates how to adapt a robotic spinal ultrasound scanning using a preliminary anthropomorphic approach.

This study aims to solve the problems of ambiguous localization, large calculation, poor real-time and limited applicability of bolt thread defect detection.

First, the acquired ultrasound image is used to acquire the larger area of the image, which is set as the compliant threaded area. Second, based on the determined coordinates of the center point in each selected region, the set of coordinates on the left and right sides of the bolts is acquired by DBSCAN method with parameters eps and MinPts, which is determined by data set dimension D and the k-distance curve. Finally, the defect detection boundary line fitting is completed using the acquired coordinate set, and the relationship between the distance from each detection point to the curve and d, which is obtained from the measurement of the standard bolt sample with known thread defect, is used to locate the bolt thread defect simultaneously.

In this paper, the bolt thread defect detection method with ultrasonic image is proposed; meanwhile, the ultrasonic image acquisition system is designed to complete the real-time localization of bolt thread defects.

The detection results show that the method can effectively detect bolt thread defects and locate the bolt thread defect location with wide applicability, small calculation and good real-time performance.

The purpose of this study is to develop a reconstruction and measurement system for data analysis using ultrasonic transmission tomography. The problem of reconstruction from the projection is encountered in practical implementation, which consists in reconstructing an image that is an estimation of an unknown object from a finite set of projection data. Reconstructive algorithms used in transmission tomography are based on linear mathematical models, which makes it necessary to process non-linear data into estimates for a finite number of projections. The application of transformation methods requires building a mathematical model in which the projection data forming the known and unknown quantities are functions with arguments from a continuous set of real numbers, determining the function describing the unknown quantities sought in the form of inverse relation and adapting it to operate on discrete and noisy data. This was done by designing a tomographic device and proprietary algorithms capable of reconstructing two-dimensional images regardless of the size, shape, location or number of inclusions hidden in the examined object.

The application consists of a device and measuring sensors, as well as proprietary algorithms for image reconstruction. Ultrasonic transmission tomography makes it possible to analyse processes occurring in an object without interfering with the examined object. The proposed solution uses algorithms based on ray integration, the Fermat principle and deterministic methods. Two applications were developed, one based on C and implemented on the embedded device, while the other application was made in Matlab.

Research shows that ultrasonic transmission tomography provides an effective analysis of tested objects in closed tanks.

In the presented technique, the use of ultrasonic absorption wave has been limited. Nevertheless, the effectiveness of such a solution has been confirmed.

The presented solution can be used for research and monitoring of technological processes.

Author’s tomographic system consisting of a measuring system and image reconstruction algorithms.

Conventional machining methods for fabricating piezoelectric components such as ultrasound transducer arrays are time-consuming and limited to relatively simple geometries. The purpose of this paper is to develop an additive manufacturing process based on the projection-based stereolithography process for the fabrication of functional piezoelectric devices including ultrasound transducers.

To overcome the challenges in fabricating viscous and low-photosensitive piezocomposite slurry, the authors developed a projection-based stereolithography process by integrating slurry tape-casting and a sliding motion design. Both green-part fabrication and post-processing processes were studied. A prototype system based on the new manufacturing process was developed for the fabrication of green-parts with complex shapes and small features. The challenges in the sintering process to achieve desired functionality were also discussed.

The presented additive manufacturing process can achieve relatively dense piezoelectric components (approximately 95 per cent). The related property testing results, including X-ray diffraction, scanning electron microscope, dielectric and ferroelectric properties as well as pulse-echo testing, show that the fabricated piezo-components have good potentials to be used in ultrasound transducers and other sensors/actuators.

A novel bottom-up projection system integrated with tape casting is presented to address the challenges in the piezo-composite fabrication, including small curing depth and viscous ceramic slurry recoating. Compared with other additive manufacturing processes, this method can achieve a thin recoating layer (as small as 10 μm) of piezo-composite slurry and can fabricate green parts using slurries with significantly higher solid loadings. After post processing, the fabricated piezoelectric components become dense and functional.

Briefly discusses the impact of information technology on medicine and health care. Introduces the concept of an integrated health information system, and details its functions and its benefits, especially for developing countries. Defines telemedicine and describes its origin, scope and functioning. Details its advantages, namely the expansion and export of skills, and explains its use in fetal telemedicine, teleendoscopy, telemedical education and telepathology.

This paper aims to report the results of a study investigating the relevance criteria used by health care professionals when seeking medical images.

Data were collected from 29 participants using a think‐aloud protocol and face‐to‐face interviews and analysed using the Straussian version of grounded theory (GT).

The results show that participants made use of 15 relevance criteria, although they agreed on topicality being the most important. The findings suggest that users apply different criteria in different situations when evaluating the relevancy of medical images.

To the best of the authors' knowledge, there have been few studies that investigate relevance judgments for visually orientated documents. Thus, this study helps to contribute to the understanding of medical image resources and the information needs of health care professionals. A clear understanding of the medical image information needs of health care professionals is also vital to the design process and development of medical image retrieval systems.