The rapid expansion of digital data content has led to the need for rich descriptions and efficient Retrieval Tool. To develop this, content based image Retrieval method has played an important role in the field of image retrieval. This paper aims to provide an efficient medical image data Retrieval from a huge content of medical database using one of the images content such as image shape, because, efficient content-based image Retrieval in the medical domain is still a challenging problem. The main objective of this paper is to provide an efficient tool which is used for efficient medical image retrieval from a huge content of medical image database and which is used for further medical diagnosis purposes.
New techniques to estimate local blood and tissue velocities have been developed by several groups, including our own. The performance of these techniques is ultimately limited by the characteristics of ultrasonic imaging systems that determine the second-order statistics of speckle. These statistical parameters vary widely depending on the dimension of analysis in the image plane (lateral or axial) and on the echo input signal (radio-frequency or detected data). We use experiments and theory to examine these factors and describe their impact on the performance of our correlation-based technique for angle-independent tracking of blood or tissue motion. The results indicate that the second-order statistics determine the performance of our correlation-based algorithm and can be used to predict the performance of other angle-independent flow detection techniques.
New techniques to estimate local blood and tissue velocities have been developed by several groups, including our own. The performance of these techniques is ultimately limited by the characteristics of ultrasonic imaging systems that determine the second-order statistics of speckle. These statistical parameters vary widely depending on the dimension of analysis in the image plane (lateral or axial) and on the echo input signal (radio-frequency or detected data). We use experiments and theory to examine these factors and describe their impact on the performance of our correlation-based technique for angle-independent tracking of blood or tissue motion. The results indicate that the second-order statistics determine the performance of our correlation-based algorithm and can be used to predict the performance of other angle-independent flow detection techniques.
Problem statement: Recently, there has been a huge progress in collection of varied image databases in the form of digital. Most of the users found it difficult to search and retrieve required images in large collections. In order to provide an effective and efficient search engine tool, the system has been implemented. In image retrieval system, there is no methodologies have been considered directly to retrieve the images from databases. Instead of that, various visual features that have been considered indirect to retrieve the images from databases. In this system, one of the visual features such as texture that has been considered indirectly into images to extract the feature of the image. That featured images only have been considered for the retrieval process in order to retrieve exact desired images from the databases. Approach: The aim of this study is to construct an efficient image retrieval tool namely, "Content Based Medical Image Retrieval with Texture Content using Gray Level Co-occurrence Matrix (GLCM) and k-Means Clustering algorithms". This image retrieval tool is capable of retrieving images based on the texture feature of the image and it takes into account the Preprocessing, feature extraction, Classification and retrieval steps in order to construct an efficient retrieval tool. The main feature of this tool is used of GLCM of the extracting texture pattern of the image and k-means clustering algorithm for image classification in order to improve retrieval efficiency. The proposed image retrieval system consists of three stages i.e., segmentation, texture feature extraction and clustering process. In the segmentation process, preprocessing step to segment the image into blocks is carried out. A reduction in an image region to be processed is carried out in the texture feature extraction process and finally, the extracted image is clustered using the k-means algorithm. The proposed system is employed for domain specific based search engine for medical Images such as CT-Scan, MRI-Scan and X-Ray. Results: For retrieval efficiency calculation, conventional measures namely precision and recall were calculated using 1000 real time medical images (100 in each category) from the MATLAB Workspace database. For selected query images from the MATLAB-Image Processing tool Box-Workspace Database, the proposed tool was tested and the precision and recall results were presented. The result indicates that the tool gives better performance in terms of percentage for all the 1000 real time medical images from which the scalable performance of the system has been proved. Conclusion: This study proposed a model for the Content Based Medical Image Retrieval System by using texture feature in calculating the Gray Level Co Occurrence matrix (GLCM) from which various statistical measures were computed in order to increasing similarities between query image and database images for improving the retrieval performance along with the large scalability of the databases.
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