Segmentation of Lung Images Using Hybridization of Fuzzy C Means and Morphological Techniques

One of the serious illnesses that could shorten a patient’s life is lung cancer. In addition, it is challenging to detect lung cancer early on and most cases are discovered after the disease has spread to other lung regions. The lung cancer tumoral tissue must be located by radiologist and other medical specialists. Consequently, the suggested work makes it easier to segment tumoral tissue from the CT lung image. Lung image segmentation is a crucial task in the field of medical image analysis because it allows lung abnormalities to be recognized and described. In order to achieve better performance hybrid strategies—which combine several approaches—have grown in popularity recently. Morphological operations are combined with fuzzy c-means clustering in this particular hybrid approach. In this work we provide a state-of-the-art overview of lung image segmentation using hybrid fuzzy c-means and morphological approaches. Hybrid techniques are an intriguing topic for additional research because it has been observed that their application can lead to increased resilience and accuracy when analyzing lung images.