An Application of Received Signal Strength Indicator in Sea Border Detection and Ship Tracking System

Fishermen plays an important role in the development of fisheries and aquaculture sector. Further they will contribute to the Indian economy. The challenges attributed to the day-to-day activities of the fishermen are many. The extensive among this many includes the difficulties faced by them when they are in dilemma about the borders in the sea. They are often arrested or killed by the navy and their boats are captured by the border coastal guards when this borderland scenario is breached. There are many existing technologies with GPS and GSM to help them. But most of the time they are inaccurate and inefficient. As a key solution to these problems the sea border detection and ship tracking system using RSSI is developed. The technology proliferation of RSSI is used to provide location-based positioning and time details in all climatic conditions and even anywhere at any time.

An algorithm to measure unsymmetrical circle shape of intravascular ultrasound image using image processing techniques

In diagnosing coronary artery disease, measurement of the cross-sectional area of the lumen, maximum and minimum diameter is very important. Mainly, it will be used to confirm the diagnosing, to predict the stenosis if any and to ensure the size of the stent to be used. However, the measurement only offers by the existing software and some of the software needs human interaction to complete the process. The purpose of this paper is to present the algorithm to measure the region of interest (ROI) on intravascular ultrasound (IVUS) using an image processing technique. The methodology starts with image acquisition process followed by image segmentation. After that, border detection for each ROI was detected and the algorithm was applied to calculate the corresponding region. The result shows that the measurement is accurate and could be used not only for IVUS but applicable to solid circle and unsymmetrical circle shape.

Automatic Lumen Border Detection in IVUS Images Using Deep Learning Model and Handcrafted Features

In the clinical analysis of Intravascular ultrasound (IVUS) images, the lumen size is an important indicator of coronary atherosclerosis, and is also the premise of coronary artery disease diagnosis and interventional treatment. In this study, a fully automatic method based on deep learning model and handcrafted features is presented for the detection of the lumen borders in IVUS images. First, 193 handcrafted features are extracted from the IVUS images. Then hybrid feature vectors are constructed by combining handcrafted features with 64 high-level features extracted from U-Net. In order to obtain the feature subsets with larger contribution, we employ the extended binary cuckoo search for feature selection. Finally, the selected 36-dimensional hybrid feature subset is used to classify the test images using dictionary learning based on kernel sparse coding. The proposed algorithm is tested on the publicly available dataset and evaluated using three indicators. Through ablation experiments, mean value of the experimental results (Jaccard: 0.88, Hausdorff distance: 0.36, Percentage of the area difference: 0.06) prove to be effective improving lumen border detection. Furthermore, compared with the recent methods used on the same dataset, the proposed method shows good performance and high accuracy.