This article reviews the role of mimicking materials used in focused ultrasound surgery (FUS) under magnetic resonance imaging. FUS is a noninvasive surgery that has many applications in oncology and neurology. Phantoms (mimicking materials) are mostly based in agar or gelatin phantoms.

Background and Objective: Oral cancer is a major public health problem which carries significant morbidity and mortality. A shift from treatment to prevention by screening is the key to reduce oral cancer lesion among population. Searching for an affordable and viable alternative to face-to-face screening that can expedite diagnosis of oral diseases among rural population with good accuracy is mandatory. One of the most realistic solutions to acknowledge this hurdle and the unavailability of dental professionals, is mobile teledentistry. Materials and Methods: Secondary data analysis was conducted, in which the data were derived from the project of “Oral Cancer Screening Program in Rural population” conducted by Thirumalai Mission Hospital, Ranipet. Ninety-six biopsies were taken for the patients who had visible oral lesions which had been provisionally diagnosed on clinical examination. Oral screening was carried out by unaided face-to-face screening method by a trained and calibrated dentist. In a separate subsequent visit, a trained teledental assistant took photographs of each participant's mouth by using a smartphone camera; the charting of the photographs was conducted independently by two dentists. Results: Intra-examiner reliability of Examiner 1 and Examiner 2 was 0.943 and 0.921, respectively. Inter examiner reliability score of 0.879 was obtained between both the examiners by the photographic method of diagnosis. Intraclass correlation coefficient between two methods of examination was 0.812. Agreement between the photographic examination (Examiner 1, Examiner 2) with the gold standard biopsy report was 0.791 and 0.855, respectively. Conclusion: Smartphone camera use offers a valid and reliable means of remote screening for oral lesions. Photographs of the oral lesions taken from the smart-phone camera with an acceptable diagnostic validity and reliability.

Background and Objectives: Stroke is a leading cause of long-term disability. Rehabilitation involving repetitive, high-intensity, and task-specific exercise is the pathway to restore motor skills. Robotic assistive devices such as Gloreha are increasingly being used in upper limb rehabilitation. The aim of this study is to explore the efficacy of robotic therapy for upper limb rehabilitation using robotic glove (Gloreha) in patients with stroke. Materials and Methods: The patients affected by stroke who were admitted to our rehabilitation unit were studied. Patients were exposed to Gloreha device rehabilitation (30 min/die), physiotherapy (1,5 hours/die), and occupational therapy (30 min/die). We measured the impairment in motor function and muscle tone using the modified Ashworth scale (MAS), the activities of daily living functional independence measure (FIM), and the finger dexterity Nine-Hole Peg test (NHPT). Results: Twelve patients (mean age = 64.5 years; male/female: 8:4) were admitted at the rehabilitation training. We found statistically significant differences between admission and discharged in terms of functional recovery using the FIM scale (pre/M = 88.33; post/M = 117.25,P = 0.01); hand training showed a better outcome using the NHPT (pre/M = 51.8; post/M = 36.33, P = 0.01). No significant changes were observed in terms of spasticity with the MAS (pre/M = 1.25; post/M = 1.08;P > 0.05). Conclusions: Rehabilitation with robotic glove (Gloreha) can positively promote functional recovery of arm function in a patient with stroke.

Background and Objectives: In magnetic resonance (MR) images' quantitative analysis, there are often considerable difficulties due to factors, such as intensity inhomogeneities and low contrast. Here, we construct a new image segmentation method to solve the MR image segmentation problem caused by internal and external factors. Materials and Methods: We downloaded a series of MR images as research objects through the BrainWeb (http://www.bic.mni.mcgill.ca/brainweb/). There is low contrast information between different components in these images. In addition, we randomly added a certain degree of bias field information to the images. We proposed a model that can simultaneously perform bias field estimation and image segmentation. Our idea is to make use of the property that observed image can be decomposed into multiplicative components. First, the bias field representation is given by a series of smooth basic functions; the required true image is represented as the function of observed image and bias field. Then, the segmentation model of Gaussian probability distribution with different means and variances is constructed by local information. Results: Qualitative experiments (intensity inhomogeneity images) show that our model achieves satisfactory segmentation results with very few (<10) iterations for severe intensity inhomogeneities image segmentation, while quantitative experiments (20 brain MR images) show that the proposed model can achieve higher accuracy in segmentation. Conclusions: Different from the existing model, our model is constructed based on the local information of the true image, and the influence of above-mentioned factors is better avoided and obtain satisfactory results.

Background and Objectives: Point set registration is a very fundamental problem in computer vision. The registration problem can be divided into rigid and nonrigid registration. The transformation function modeling of rigid registration is simpler, whereas the nonrigid registration is better to solve the practical problems. Materials and Methods: We proposed a robust point set registration method using both global and local structures. Here, we use a popular probability model, Gaussian mixture model, to preserve the global structure of point set. Then, we designed a local constraint provided by some neighboring points to maintain the local structure of the point set. Finally, expectation–maximization algorithm is used to update model parameters in our method. Results: First of all, we carried out experiments on the synthesized data, which included four degradation cases: deformation, noise, outlier, and rotation. By comparing the mean and standard deviation of registration errors with the several state-of-the-art methods, our method was proved to have stronger robustness. Then, we conducted experiments on real retinal fundus images, aiming to establish reliable feature point correspondence between the two images. The experimental results show that we perform better when the two images have larger shooting angles and more noises. Conclusions: The Gaussian mixture protects the global structure of the point set, and the local constraints make full use of the local structure, which makes our method more robust. Experiments on synthetic data prove that our method obtains superior results to those of the state-of-the-art methods. Experiments on retinal image data show that our method also performs very well in practical applications.

Background and Objectives: Image segmentation is the basic problem in computer vision and pattern recognition. This study mainly focuses on the segmentation of medical sequence images. Materials and Methods: In this article, we considered the spatial similarity of the medical sequence image in active contour model (ACM) for segmentation. First, by utilizing the similarity of object contour between adjacent slices of medical images, and then using the segment result of the former slice as the initial contour of the next image to segmentation. The proposed model can automatically obtain a better initial contour location and reduce the computing cost for segment processing. Second, to improve the accuracy of image segmentation, we considered the similarity of the object contour between adjacent slices, and introduce a punishment term in localized ACM. Results: We compared our model and other methods for segmenting medical brain magnetic resonance slices, and the experimental results on synthetic medical sequence images validate the effectiveness of the proposed method. Conclusions: By utilizing the similarity of object contour between adjacent slices of medical images, and using the segment result of former slice as the initial contour of the next image to segment, the proposed model can obtain better initial contour location for segmentation sequence images and reduce the computing cost for whole medical sequence image segmentation process.