Volume : 2, Issue : 11, November - 2013

Image Segmentation Using Modified Spatial Fuzzy c–Means

Mrs. J. Nithya, S. Anu Vaishali

Abstract :

Disease diagnosis based on ultrasound imaging is popular because of its non–invasive nature. However, ultrasound imaging system produces low quality images due to the presence of spackle noise and wave interferences. This shortcoming requires a considerable effort from experts to diagnose a disease from the fetal ultrasound images. Image segmentation is one of the techniques, which can help efficiently in diagnosing a disease from the ultrasound images. Most of the pixels in an image are highly correlated. Considering the spatial information of surrounding pixels in the process of image segmentation may further improve the results. When data is highly correlated, one pixel may belong to more than one cluster with different degree of membership. This paper deals with an image segmentation technique namely improved spatial fuzzy c–means and an ensemble clustering approach for the fetal ultrasound images to identify the abnormal organ development. Spatial, wavelets and gray level co–occurrence matrix (GLCM) features are extracted from fetal ultrasound images. Redundant and less important features are removed from the features set using genetic search process. Finally, segmentation process is performed on optimal or reduced features. Ensemble clustering with reduced feature set provides segmentation time as well as clustering accuracy. Parameters are measured from the images that are segmented. Based on the values, Multi–Layer Back–Propagation Neural Networks (MLBPNN) is used to classify the images into normal or abnormal. Experimental results show the learning capability of MLBPNN classifier. This paper deals with the segmentation and classification of fetal ultrasound images that are very useful for detection of normal or abnormal organ development