Detection of Brain Tumor in MRI Image through Fuzzy-Based Approach

The process of accurate detection of edges of MRI images of a brain is always a challenging but interesting problem. Accurate detection is very important and critical for the generation of correct diagnosis. The major problem that comes across while analyzing MRI images of a brain is inaccurate data. The process of segmentation of brain MRI image involves the problem of searching anatomical regions of interest, which can help radiologists to extract shapes, appearance, and other structural features for diagnosis of diseases or treatment evaluation. The brain image segmentation is composed of many stages. During the last few years, preprocessing algorithms, techniques, and operators have emerged as a powerful tool for efficient extraction of regions of interest, performing basic algebraic operations on images, enhancing specific image features, and reducing data on both resolution and brightness. Edge detection is one of the techniques of image segmentation. Here from image segmentation, tumor is located. Finally, we try to retrieve tumor from MRI image of a brain in the form of edge more accurately and efficiently, by enhancing the performance of diffe rent kinds of edge detectors using fuzzy approach

Online Covariate Shift Detection based Adaptive Brain-Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation

A major issue in electroencephalogram (EEG) based brain-computer interfaces (BCIs) is the intrinsic non-stationarities in the brain waves, which may degrade the performance of the classifier, while transitioning from calibration to feedback generation phase. The non-stationary nature of the EEG data may cause its input probability distribution to vary over time, which often appear as a covariate shift. To adapt to the covariate shift, we had proposed an adaptive learning method in our previous work and tested it on offline standard datasets. This paper presents an online BCI system using previously developed covariate shift detection (CSD)-based adaptive classifier to discriminate between mental tasks and generate neurofeedback in the form of visual and exoskeleton motion. The CSD test helps prevent unnecessary retraining of the classifier. The feasibility of the developed online-BCI system was first tested on 10 healthy individuals, and then on 10 stroke patients having hand disability. A comparison of the proposed online CSD-based adaptive classifier with conventional non-adaptive classifier has shown a significantly (p<0.01) higher classification accuracy in both the cases of healthy and patient groups. The results demonstrate that the online CSD-based adaptive BCI system is superior to the non-adaptive BCI system and it is feasible to be used for actuating hand exoskeleton for the stroke-rehabilitation applications

An EEG-EMG Correlation-based Brain-Computer Interface for Hand Orthosis Supported Neuro-Rehabilitation

Background Corticomuscular coupling has been investigated for long, to find out the underlying mechanisms behind cortical drives to produce different motor tasks. Although important in rehabilitation perspective, the use of corticomuscular coupling for driving brain-computer interface (BCI)-based neurorehabilitation is much ignored. This is primarily due to the fact that the EEG-EMG coherence popularly used to compute corticomuscular coupling, fails to produce sufficient accuracy in single-trial based prediction of motor tasks in a BCI system. New Method In this study, we have introduced a new corticomuscular feature extraction method based on the correlation between band-limited power time-courses (CBPT) associated with EEG and EMG. 16 healthy individuals and 8 hemiplegic patients participated in a BCI-based hand orthosis triggering task, to test the performance of the CBPT method. The healthy population was equally divided into two groups; one experimental group for CBPT-based BCI experiment and another control group for EEG-EMG coherence based BCI experiment. Results The classification accuracy of the CBPT-based BCI system was found to be 92.81±2.09% for the healthy experimental group and 84.53±4.58% for the patients’ group. Comparison with existing method The CBPT method significantly (p−value < 0.05) outperformed the conventional EEG-EMG coherence method in terms of classification accuracy. Conclusions The experimental results clearly indicate that the EEG-EMG CBPT is a better alternative as a corticomuscular feature to drive a BCI system. Additionally, it is also feasible to use the proposed method to design BCI-based robotic neurorehabilitation paradigms

ProHealth eCoach: user-centered design and development of an eCoach app to promote healthy lifestyle with personalized activity recommendations

Background: Regular physical activity (PA), healthy habits, and an appropriate diet are recommended guidelines to maintain a healthy lifestyle. A healthy lifestyle can help to avoid chronic diseases and long-term illnesses. A monitoring and automatic personalized lifestyle recommendation system (i.e., automatic electronic coach or eCoach) with considering clinical and ethical guidelines, individual health status, condition, and preferences may successfully help participants to follow recommendations to maintain a healthy lifestyle. As a prerequisite for the prototype design of such a helpful eCoach system, it is essential to involve the end-users and subject-matter experts throughout the iterative design process. Methods: We used an iterative user-centered design (UCD) approach to understend context of use and to collect qualitative data to develop a roadmap for self-management with eCoaching. We involved researchers, non-technical and technical, health professionals, subject-matter experts, and potential end-users in design process. We designed and developed the eCoach prototype in two stages, adopting diferent phases of the iterative design process. In design workshop 1, we focused on identifying end-users, understanding the user’s context, specifying user requirements, designing and developing an initial low-fdelity eCoach prototype. In design workshop 2, we focused on maturing the low-fdelity solution design and development for the visualization of continuous and discrete data, artifcial intelligence (AI)-based interval forecasting, personalized recommendations, and activity goals. Results: The iterative design process helped to develop a working prototype of eCoach system that meets end-user’s requirements and expectations towards an efective recommendation visualization, considering diversity in culture, quality of life, and human values. The design provides an early version of the solution, consisting of wearable technology, a mobile app following the “Google Material Design” guidelines, and web content for self-monitoring, goal setting, and lifestyle recommendations in an engaging manner between the eCoach app and end-users. Conclusions: The adopted iterative design process brings in a design focus on the user and their needs at each phase. Throughout the design process, users have been involved at the heart of the design to create a working.publishedVersio

Species diversity of genus Capsicum using agromorphological descriptors and simple sequence repeat markers

906-915Sustainability of crops in most demand depends upon their genetic diversity. Capsicum, commonly called chilli, is one such crop with its fruits extensively used as vegetable across the world. Knowledge on various traits is important for genetic improvement of such species. Here, we assessed the genetic diversity among 10 genotypes of six Capsicum species, namely Capsicum annuum, C. chinense, C. chacoense, C. frutescens, C. tovarii and C. galapagoense. C. annuum MS-12 is a genetic male sterile line. We used morphological descriptors and simple-sequence repeat (SSR) molecular markers for this study. Out of 60 SSR screened, 22 markers (36.66%) showed polymorphism. Alleles number per locus varied from 3 to 7. Average PIC value for 22 polymorphic markers was 0.69, and ranged from 0.54 for the primer Hpms 1-139 to 0.85 for the primer CAMS-072. Ten genotypes of Capsicum species were grouped into three major clusters such that genotypes in a single cluster had less dissimilarity matrix values among themselves than which belongs to other clusters. Range of fruit weight and pericarp thickness varied from 0.1 g (‘PAU-621’) to 2.3 g (‘MS-12’), and from 0.29 mm (‘PAU-621’) to1.09 mm (‘MS12’), respectively. These two genotypes can be used in hybridization or in recombinant breeding program for obtaining higher heterotic effects/ heterosis or for transgressive segregants in chilli pepper

Species diversity of genus Capsicum using agromorphological descriptors and simple sequence repeat markers

Sustainability of crops in most demand depends upon their genetic diversity. Capsicum, commonly called chilli, is one such crop with its fruits extensively used as vegetable across the world. Knowledge on various traits is important for genetic improvement of such species. Here, we assessed the genetic diversity among 10 genotypes of six Capsicum species, namely Capsicum annuum, C. chinense, C. chacoense, C. frutescens, C. tovarii and C. galapagoense. C. annuum MS-12 is a genetic male sterile line. We used morphological descriptors and simple-sequence repeat (SSR) molecular markers for this study. Out of 60 SSR screened, 22 markers (36.66%) showed polymorphism. Alleles number per locus varied from 3 to 7. Average PIC value for 22 polymorphic markers was 0.69, and ranged from 0.54 for the primer Hpms 1-139 to 0.85 for the primer CAMS-072. Ten genotypes of Capsicum species were grouped into three major clusters such that genotypes in a single cluster had less dissimilarity matrix values among themselves than which belongs to other clusters. Range of fruit weight and pericarp thickness varied from 0.1 g (‘PAU-621’) to 2.3 g (‘MS-12’), and from 0.29 mm (‘PAU-621’) to1.09 mm (‘MS- 12’), respectively. These two genotypes can be used in hybridization or in recombinant breeding program for obtaining higher heterotic effects/ heterosis or for transgressive segregants in chilli pepper