Dr. Swati Bhisikar
@jspmrscoe.edu.in
Associate Professor
Jayawant Shikshan Prasarak Mandal's Rajarshi Shahu College of Engineering, Pune
EDUCATION
Engineering)
RESEARCH INTERESTS
Image and signal Processing, Artificial Intelligence, Machine Learning
Scopus Publications
Scopus Publications
Swati Bhisikar, Simran Taneja, Omkar Yadav, and Swarnika Srivastava
Auricle Technologies, Pvt., Ltd.
Peer-to-peer (P2P) networking has emerged as a promising technology that enables distributed systems to operate in a decentralized manner. P2P networks are based on a model where each node in the network can act as both a client and a server, thereby enabling data and resource sharing without relying on centralized servers. The P2P model has gained considerable attention in recent years due to its potential to provide a scalable, fault-tolerant, and resilient architecture for various applications such as file sharing, content distribution, and social networks.In recent years, researchers have also proposed hybrid architectures that combine the benefits of both structured and unstructured P2P networks. For example, the Distributed Hash Table (DHT) is a popular hybrid architecture that provides efficient lookup and search algorithms while maintaining the flexibility and adaptability of the unstructured network.To demonstrate the feasibility of P2P systems, several prototypes have been developed, such as the BitTorrent file-sharing protocol and the Skype voice-over-IP (VoIP) service. These prototypes have demonstrated the potential of P2P systems for large-scale applications and have paved the way for the development of new P2P-based systems.
Hemant Kasturiwale, Swati Bhisikar, and Sandhya Save
Wiley
S. A. Bhisikar and S. N. Kale
Springer Singapore
Arthritis is a disabling and agonizing disease. The rapid growth of biomedical image processing techniques assists the doctor in diagnosis and treatment of the disease. In Rheumatoid Arthritis as the disease progresses, it results in reducing physical activity level of the patient. The method presented in this paper is a completely automated framework to detect and quantify joint space width. This system detects severe stage of RA that are contaminated by disease to the degree that the joint space is no longer noticeable in the X-ray image. In proposed work RA is classified in three stages Normal (Non-RA), Abnormal (RA) and Severe stage RA. Joint location accuracy achieved is 92%. 60 images were tested, Out of 60 Test images 20 images are Normal, 22 images are abnormal i.e. RA affected and 18 images are severe. SVM classifier with Radial basis function kernel is efficient compared to FFNN and k-NN as Non-RA i.e. Normal patient classification accuracy is 95%, RA classification accuracy is 70%, Severe stage classification accuracy of RA is 100%.
Swati A. Bhisikar and Sujata N. Kale
IEEE
Rheumatoid arthritis destroys joints of the body like erosion in bones which intern may cause deformity and ankylosis in the later stage of the disease. At the beginning of this disease mainly the joints of hand and wrist are affected making hand radiograph analysis very important. Lately manual JSW measurement in hand X-ray digital radiograph of Arthritis patients were in use but it has disadvantages like inaccuracy, inter-reader variability. Also hand radiograph analysis is difficult for radiologist since in all there are 14 number of hand joints. To avoid observer dependency, computer-aided analysis is required. We have proposed the use of image processing techniques using MATLAB to analyze joint space narrowing. In this paper bone boundaries are delineated with Active Shape Model which contains statistical model of bone shape and local texture. Joint positions are identified by local linear mapping based on texture features. We have examined five hand radiograph images affected by RA. Joint location estimate accuracy is 92%. The automated analysis helps to reduce need of skilled personnel. Also remote analysis and medication is possible.
Dany Bright, Amrita Nair, Devashish Salvekar, and Swati Bhisikar
IEEE
Lately myoelectric prosthetics are in use. But it has certain disadvantages like it relies on the nerves to be undamaged and it's very expensive, which can be overcome by EEG-based brain controlled prosthetic arm. EEG-based brain controlled prosthetic arm is a non-invasive technique that can serve as a powerful aid for severely disabled people in their daily life, especially to help them move their arm voluntarily. In this paper, EEG-based brain controlled prosthetic arm has been developed using BCI with the help of Neurosky Mindwave headset to yield the two main movements of fingers in the arm: Flexion and Extension. BCI system consists of an EEG sensor to capture the brain signal, which will be processed using ThinkGear module in MATLAB. The extracted brain signals act as command signals that are transmitted to the Microcontroller via RF medium. The prosthetic arm module designed consists of Arduino coupled with servo motors to perform the command. The flexion and extension of finger can be successfully controlled with an accuracy of 80 per cent. The low cost wireless BCI system could allow the disabled people to control their prosthetic arm to lead a self-reliant life with the help of their brain signals.
Swati A. Bhisikar and Sujata N. Kale
IEEE
Arthritis is an inflammatory disease which causes erosion in bones or narrowing of joint space in various joints of the body. First symptom of this disease is seen in joints of hand finger and wrist joints thus making hand radiograph analysis extremely important. Lately Reading hand X-ray radiographic image to measure joint space width is very tedious and time consuming task for the radiologist since there are 14 joints in hand and also the structure of hand is complicated to carry out joint space width measurement and analysis. It has certain disadvantages like inaccuracy because of visual measurement and also variation from one reader to another, which can be overcome by automatic technique that can serve as a powerful aid for peoples suffering from disability due to pain, stiffness in joints. In this paper, Image processing based algorithm is developed to yield solution to two major problems joint detection and JSW measurement. The algorithm is divided into following steps, First image preprocessing is carried out using Gaussian filter. Second hand mask is extracted by separating foreground and background by using Otsu's binarization method. Third morphological thinning is applied to get thinned skeleton of binarized image. Fourth To detect joint location in original X-ray image Gabor filter is used. Fifth edge Finally of minimal joint space width is extracted and analyzed automatically. We have experimented 10 digital hand X-ray radiograph of resolution 2000pixels×2000pixels and calculated 120 readings of JSW of finger joints successfully.