IJECT Vol. 15.1 (Jan – March 2024)

Vol 15 Issue 1 (Jan – March 2024)

International Journal of Electronics & Communication Technology Vol 15 Issue 1 (Jan – March 2024)
S.No. Research Topic Paper ID
01 A Study of Lung Field Segmentation Technique Using Image Processing
Priya Rani, Dr Priyanka Anand

In the realm of medical imaging, lung field segmentation in chest radiographs is an essential technique that plays a significant part in the accurate diagnosis and effective treatment planning for a variety of pulmonary disorders. This review study investigates the development of approaches that are utilised for lung field segmentation as well as the present condition of those techniques. The move from early methods that utilised fundamental image processing techniques, such as edge detection and morphological operations, to more advanced and accurate procedures is brought to light by this. In this article, we address the implementation of structured edge detection methods, which identify lung boundaries through contrast differences. Additionally, we examine the adaption of shape modelling methodologies, particularly dynamic models, in order to take into account variations in lung architecture. The research also highlights the tremendous influence that deep learning, and more specifically convolutional neural networks (CNNs), has had in revolutionising lung field segmentation. CNNs have been able to automatically extract important features from chest radiographs, which has been a significant impact. The use of statistical approaches to obtain personalised and precise segmentation is also investigated, taking into consideration the fact that people’ lung shapes and sizes might vary greatly from one another. These technical developments have not only increased the accuracy and efficiency of lung field segmentation, but they also have broad implications in helping to enhance patient care. This is notably true in the diagnosis and study of illnesses such as cardiomegaly and tuberculosis.
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