Research And Application On CT Image

With the development of science and technology, medical images have become important assistant means of diagnosis and therapy. Computed Tomography (CT) image is an important method to research of pathology and anatomy, as its high resolution and little harm to human beings. However, there are many inevitable defects in biomedical images. In order to improve the readability of medical image and make the doctor to adopt more effective observation and diagnoses way to anatomy structure and pathological part of the patient, it' s necessary to study the medical image processing.The image segmentation technology plays an important part in image processing and therefore many researchers have paid much attention on it. The basic idea of image segmentation is to divide the image space into some special regions. As for CT image, it is to extract meaningful or useful features, which are original information in image space, like the pixel grayscale level in the region occupied by object or the features of spatial spectrum or histogram.The paper mainly aims at CT image. On the basis of approximate introduction CT device, its imaging theory and the features of CT image, the methods of image processing applied to the CT image have been studied, including: image representation and display, image color processing. Some pretreatment methods of image transformation, histogram and image enhancement in space field have been done before CT image segmentation. Besides, the algorithms of edge detection, contour extraction and contour trace have been adopted in CT imaging processing.The main research content in the paper is the application of image segmentation to CT images. According to the features of CT image, thesegmentation methods based on histogram have been studied, including the segmentations of single threshold and multiple thresholds to CT image. The automatic methods of choosing threshold, including the methods based on entropy, moment, concavity, gradient mean, etc, have been designed. When applied to CT images, these methods mentioned above have acquired better results comparing with traditional methods of image segmentation. The automatic multi-threshold method of image segmentation to CT image has been improved as well. In addition, the segmentations to CT images based on wavelet transform, spatial cluster and relaxation iteration have been realized. The results obtained in this paper have the significance in clinical applications.