In this paper, a new segmentation method is proposed. This method is based on the residuals of morphological opening and closing transforms with a geodesic metric. It may be considered analogous to a region growing technique. However, in contrast to using statistical local properties like in region growing approaches, the proposed method uses a pixel similarity rule based on the morphological characteristic of the connected components in the image. The proposed method is particularly well suited for the segmentation of complex image scenes such as aerial or fine-resolution images where very thin, enveloped and/or nested regions may have to be retained, and where the gradient calculation has a major drawback.
Medical images are at the heart of the healthcare diagnostic procedures. They have provided not only a noninvasive mean to view anatomical cross-sections of internal organs but also a mean for physicians to evaluate the patient’s diagnosis and monitor the effects of the treatment. For a Medical Center, the emphasis may shift from the generation of image to post processing and data management since the medical staff may generate even more processed images and other data from the original image after various analyses and post processing. A medical image data repository for
health care information system is becoming a critical need. This data repository would contain comprehensive patient records, including information such as clinical data and related diagnostic images, and post-processed images. Due to the large volume and complexity of the data as well as the diversified user access requirements, the implementation of the medical image archive system will be a complex and challenging task. This paper discusses content standards for
medical image metadata. In addition it also focuses on the image metadata content evaluation and metadata quality management.