Ultrasound Cervical Cancer Based Abnormality Segmentation Using Adaptive Fuzzy C-Mean Clustering

Our main goal in this paper is to produce a method for the automated segmentation of an abnormality in a medical image, including acquiring first image data representative of the medical image; locating a suspicious site at which the abnormality may exist; establishing a selection point within the suspicious site; and preprocessing the suspicious site with a constraint function to produce second image data in which pixel values distant of the selected point are suppressed. The algorithm is an extension of the two-dimensional adaptive fuzzy C-means algorithm.It creatively adapts to the intensity in homogeneities and is completely automated. In this paper we focus our attention on methods for the segmentation of multimodal medical images. The algorithm is realized by modifying the objective function in the conventional fuzzy c-means algorithm. The proposed paper uses two adaptive different multimodal data sets and the results have been compared to those obtained by using the classical fuzzy c-means algorithm. For the purpose of early treatment with radiotherapy and surgery the newly proposed AFCM is preferred to provide more information for medical images. Furthermore, a discussion is presented about the role of fuzzy clustering as a support to diagnosis in medical imaging.