The population is aging as the years pass. There is an increase in life expectancy, but also a decrease in the quality of life for the presence of chronic degenerative diseases. Processing medical images can identify brain changes typical of Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) at an early stage. We propose a new method of segmentation technique using Mean Shift algorithm applying probabilistic maps and Support Vector Machine with Linear and Radial Basis Kernel for segmentation of the hippocampus on Magnetic Resonance Images (MRI). The similarity index of DICE for a 8 control subject was calculated obtaining a mean value of 0:7053±0:0996 using Linear kernel and 0:7275±0:1335 using RBF kernel compared with the manual segmentation made by radiologist.
Proc. SPIE. 5748, Medical Imaging 2005: PACS and Imaging Informatics
KEYWORDS: Staring arrays, Modeling, Statistical analysis, Visualization, Medical imaging, Software development, Image storage, Computer architecture, Systems modeling, Picture Archiving and Communication System
A PACS development requires to fill the needs of a specific imagenology area in a hospital and, as consequence, the amount of requirements associated to a PACS implicate a great complexity. This can be observed through methods that allow the size and complexity of a PACS software system to be quantified and measured, by analyzing the user requirements and interactions with other systems to be realized. When a PACS development is proposed, it can be difficult to actually launch the development project since a lot of time may be invested in defining the initial activities to be performed. In this work a model to address the complexity of a PACS development is proposed, and a strategy to divide the different tasks involved is defined. The model can offer an estimation about the effort to be spent. To face the problem, a correct planning and schedule can be defined. The model was obtained applying the first steps of the introductory Team Software Process (TSPi) methodology, and was represented using Unified Modelling Language activity diagrams. The model shows the different activities that have to be realized during the PACS development, and also the products that are generated once activities are accomplished. Another main aspect is a dependence view which shows the synchronization and dependence between tasks. This allows the possible sequences of activities to be visualized, and to be planned across different cycles. According to the TSPi, in each planned cycle a testable version of a PACS specific application should to be produced and the combination of the products, obtained through the different cycles should produce a final software system. With the model presented in this work, PACS developers can have a clear idea about the involved tasks and can schedule the work to accomplish specific PACS applications. A case study was conducted at the "Centro Nacional de Rehabilitacion" (National Rehabilitation Center)in Mexico City, using the proposed model.
The DICOM standard, as all standards, specifies in generic way the management in network and storage media environments of digital medical images and their related information. However, understanding the specifications for particular implementation is not a trivial work. Thus, this work is about understanding and modelling parts of the DICOM standard using Object Oriented methodologies, as part of software development processes. This has offered different static and dynamic views, according with the standard specifications, and the resultant models have been represented through the Unified Modelling Language (UML). The modelled parts are related to network conformance claim: Network Communication Support for Message Exchange, Message Exchange, Information Object Definitions, Service Class Specifications, Data Structures and Encoding, and Data Dictionary. The resultant models have given a better understanding about DICOM parts and have opened the possibility of create a software library to develop DICOM conformable PACS applications.
In this paper, a nonparametric statistical segmentation procedure based on the computation of the mean shift within the joint space-range feature representation of brain MR images is presented. The mean shift is a simple, nonparametric estimator, which can be implemented in a data-driven approach. The number of classes and other initialization parameters are not needed to compute the mean shift. The procedure estimates the local modes of the probability density function in order to define the cluster centers on the feature space. Local segmentation quality is improved by including a measure of edge confidence among adjacent segmented regions. This measure drives the iterative application of transitive closure operations on the region adjacency graph until convergence to a stable set of regions. In this manner, edge detection and region segmentation techniques are combined for the extraction of weak but significant edges from brain images. With the proposed methodology, the modes of the classes' distribution can be robustly estimated and homogeneous regions defined, but also fine borders are preserved. The main contribution of this work is the combined use of mean shift estimation, together with a robust, edge-oriented region fusion technique to delineate structures in brain MRI.