Hybrid Segmentation and 3D Modeling of Pleural Effusion on CT

Abstract

Pleural effusion is the excess fluid within the pleural space. Pleural effusion detection helps in the diagnosis of diseases. If the effluent is not within the safe zone then it creates many problems including the death of human. The pleural level can be detected manually which is time consuming. The objective of this study is to determine the pleural fluid on computed tomography (CT) scan images automatically. The pleural space is segmented by parietal pleura extraction and visceral pleura extraction. The method is based on nonlinear anisotropic diffusion filtering and hybrid segmentation. 3D deformable modeling is applied for three dimensional view of the pleural effusion. We compare this method to manual segmentations and result is closer as expected. This method is useful in diagnosis of pleural effusion effectively rather than manual determination.

Artificial intelligence-powered intelligent reflecting surface systems countering adversarial attacks in machine learning

“…The principle is to train the models to automatically understand the original designs of the working procedure and relationships in data using the trained algorithms [1]. Post training is mostly used to calculate and analyze the outlines in given information [14]. Figure 1 shows the steps involved in wrong prediction due to attack on machine learning technique.…”