Rapid advances in the field of medical imaging are revolutionizing medicine. The determination of the presence or severity of disease will impact clinical care for a patient or outcome status in a study. The use of computer-aided diagnosis (CAD) systems to improve the sensitivity and specificity of lesion detection has become a focus of medical imaging and diagnostic radiology research. Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. In this paper, segmentation problems in medical imaging modalities especially for lung CT as well as for thyroid ultrasound (US) are discussed along with their comparative results are shown using automatic tools as well as with some specific algorithms. Various automatic tools have been used and discussed. The results shows that though segmentation is the crucial, required and most difficult phase yet the outcome is really advantageous in medicine for the perfect diagnosis of any disease. Both the outcomes either from automatic tool as well as using an algorithm provide the required ROI (region of interest).
No abstract
Image segmentation plays a crucial role in many medical imaging applications by extracting the regions of interest. Accurate segmentation of medical images is a key step in the use of computer-aided diagnosis (CAD) systems to improve the sensitivity and specificity of lesion detection. In this paper, segmentation problems in medical imaging modalities especially for lung CT as well as for thyroid ultrasound (US) are discussed along with their comparative results are shown using automatic tools as well as with some specific algorithms. In this paper various automatic tools as well as manual segmentation algorithms have been used and compared. Both the outcomes either from automatic tool as well as using an algorithm provide the required ROI (region of interest) but automatic tool’s output is more efficient and perfect. 3D visualization as well as volumetric segmentation is done accurately with the help of these tools which help in segmenting CT (3D) images especially.
Abstrac-Among the applications of computer science in the field of medicine, the processing of medical image data is playing an increasingly important role. With medical imaging techniques such as X-Ray, computer tomography, magnetic resonance imaging, and ultrasound, the amount of digital images that are produced in hospitals is increasing incredibly. Thus the need for systems that can provide efficient retrieval of images of particular interest is becoming very high. Unfortunately, only very few medical image retrieval systems are currently used in clinical routine. This paper presents a highlight on the role of image retrieval in medical domain. Also it reviews the techniques used in medical image. Paper has also briefed about retrieval trends, key issues and limitations. It also point out scope and challenges in designing image retrieval systems.
The image segmentation is the basic step in the detection of tumors in various medical images. Specially when used for CAD system. Presence of pectoral muscles gives very false results in the detection process. Removing pectoral muscles is a very important issue in Mammograms. This paper address this issues of Pectorial muscles removal from the mammogram image. We have extracted various features of the mammogram images and their ranges to remove the unwanted part of pectoral muscles which remains even after the segmentation. This method is very simple and yet very effective to achieve the exact ROI.
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