Abstract
Pleural effusion (PE) is the extra fluid with the purpose of accumulating between the two pleural layers and the fluid-stuffed gap so as to surround the lungs. The buildups such as fluid inside the pleural opening are commonly a symptom of an extra illness consisting of congestive heart failure, pneumonia, or metastatic cancers. Computed tomography (CT) chest examines experiment and is presently used to measure PE as radiographs and ultrasonic methods had been located to be much less correct in prediction. The proposed approach focuses on automating the process of detecting edges and measuring PE from CT scan images. The CT scanned images are processed initially to reduce intensity from the image by making it smooth. Then, edge detection algorithm is applied to that smooth image to identify visceral pleura (inner layer) along with parietal pleura (outer layer). The ending points of these two identified layers are detected using a high-speed raster scan algorithm. The pixels identified within these end points are detected to measure the affected area. This proposed is evaluated and uses advanced image processing techniques. Hence, it proves to be good implementations in clinical diagnostic purposes, as the processes are entirely computerized with time-effective.
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Rameshkumar, C., Hemlathadhevi, A. (2019). Automatic Edge Detection and Growth Prediction of Pleural Effusion Using Raster Scan Algorithm. In: Chaki, N., Devarakonda, N., Sarkar, A., Debnath, N. (eds) Proceedings of International Conference on Computational Intelligence and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 28. Springer, Singapore. https://doi.org/10.1007/978-981-13-6459-4_9
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DOI: https://doi.org/10.1007/978-981-13-6459-4_9
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