Image Segmentation using Extended Edge Operator for Mammographic Images

Detection of edges in an image is a very important step towards understanding image features. Since edges often occur at image locations representing object boundaries, edge detection is extensively used in image segmentation when images are divided into areas corresponding to different objects. This can be used specifically for enhancing the tumor area in mammographic images. In this paper extended Sobel , Prewitt and Kirsch edge operators are proposed for image segmentation of mammographic images. Edges and tumor location can be seen clearly by using this method. For comparison purpose Gray level co-occurrence matrix, watershed algorithm, present Sobel, Prewitt and Kirsch edge operators are used and their results are displayed. Diagnostic imaging is an invaluable tool in medicine today. These imaging modalities provide an effective means for noninvasive mapping of the anatomy of a subject. These technologies have greatly increased knowledge of normal and diseased anatomy for medical research and are a critical component in diagnosis and treatment planning. With the increasing size and number of medical images, the use of computers in facilitating their processing and analysis has become necessary. Estimation of the volume of the whole organ, parts of the organ and/or objects within an organ i.e. tumors is clinically important in the analysis of medical image. The relative change in size, shape and the spatial relationships between anatomical structures obtained from intensity distributions provide important information in clinical diagnosis for monitoring disease progression. Therefore, radiologists are particularly interested to observe the size, shape and texture of the organs and/or parts of the organ. For this, organ and tissue morphometry performed in every radiological imaging centre. These routine assessments are commonly subjective and quantitative, and reports typically refer to lesions as large, small, and prominent. The clinical reports usually offer morphometric data in terms of change relative to a prior study. The recognition, labeling and the quantitative measurement of specific objects and structures are involved in the analysis of medical images. Therefore, to provide the information about an object clinically in terms of its size and shape, image segmentation and classification are important tools needed to give the desired information. Medical images edge detection is an important work for object recognition of the human organs such as lungs and ribs, and it is an essential pre-processing step in medical image