Segmentation Of Lung Structures With Fuzzy Clustering Algorithm

Cancer are considered to be the major health threat in several regions of the world. After HIV, it is the second foremost infectious disease in worldwide causing death. When it is left undiagnosed and untreated, humanity rates of patients are high. The diagnostic methods are slow and still unreliable to detect. In order to reduce the liability of the disease, this work presents our automatic methodology for identifying Cancer. Initially, the extraction of the lung region is done using a graph cut segmentation method. Using this lung region, we figure out a set of texture and shape features, which enable the X-rays to be classified as normal or abnormal using the SVM classifier. This paper presents a simplified methodology using fuzzy logic segmentation from the natural image processing to lung segmentation tasks over GC segmentation. The proposed indicative system for analyzing CANCER segmentation achieves a better performance than the approaches of graph cut segmentation Citation: Dr.A.Umarani, K.L.N. College of Engineering; R.Arunjunai Rani ,; Su.Raja Subhashini ,. "Segmentation Of Lung Structures With Fuzzy Clustering Algorithm." Global Research and Development Journal For Engineering : 350 - 355.