Multi-level adaptive segmentation of multi-parameter MR brain images

Autor: Aleksandar Zavaljevski, Jeffrey D. Johnson, M. Gaskil, William S. Ball, Atam P. Dhawan
Rok vydání: 2000
Předmět:
Pathology
medicine.medical_specialty
Computer science
Normal Distribution
Color
Contrast Media
Scale-space segmentation
Expert Systems
Gadolinium
Health Informatics
Image processing
Markov model
Pattern Recognition
Automated
User-Computer Interface
Image Processing
Computer-Assisted
medicine
Humans
Radiology
Nuclear Medicine and imaging
Segmentation
Least-Squares Analysis
Child
Cerebrospinal Fluid
Observer Variation
Likelihood Functions
Models
Statistical
Radiological and Ultrasound Technology
Pixel
Segmentation-based object categorization
business.industry
Brain
Discriminant Analysis
Pattern recognition
Image segmentation
Linear discriminant analysis
Magnetic Resonance Imaging
Computer Graphics and Computer-Aided Design
Markov Chains
Stroke
Cerebrovascular Disorders
Blood
Computer Vision and Pattern Recognition
Artificial intelligence
business
Algorithms
Follow-Up Studies
Zdroj: Computerized Medical Imaging and Graphics. 24:87-98
ISSN: 0895-6111
DOI: 10.1016/s0895-6111(99)00042-7
Popis: MR brain image segmentation into several tissue classes is of significant interest to visualize and quantify individual anatomical structures. Traditionally, the segmentation is performed manually in a clinical environment that is operator dependent and may be difficult to reproduce. Though several algorithms have been investigated in the literature for computerized automatic segmentation of MR brain images, they are usually targeted to classify image into a limited number of classes such as white matter, gray matter, cerebrospinal fluid and specific lesions. We present a novel model-based method for the automatic segmentation and classification of multi-parameter MR brain images into a larger number of tissue classes of interest. Our model employs 15 brain tissue classes instead of the commonly used set of four classes, which were of clinical interest to neuroradiologists for following-up with patients suffering from cerebrovascular deficiency (CVD) and/or stroke. The model approximates the spatial distribution of tissue classes by a Gauss Markov random field and uses the maximum likelihood method to estimate the class probabilities and transitional probabilities for each pixel of the image. Multi-parameter MR brain images with T 1 , T 2 , proton density, Gd+ T 1 , and perfusion imaging were used in segmentation and classification. In the development of the segmentation model, true class-membership of measured parameters was determined from manual segmentation of a set of normal and pathologic brain images by a team of neuroradiologists. The manual segmentation was performed using a human–computer interface specifically designed for pixel-by-pixel segmentation of brain images. The registration of corresponding images from different brains was accomplished using an elastic transformation. The presented segmentation method uses the multi-parameter model in adaptive segmentation of brain images on a pixel-by-pixel basis. The method was evaluated on a set of multi-parameter MR brain images of a twelve-year old patient 48h after suffering a stroke. The results of classification as compared to the manual segmentation of the same data show the efficacy and accuracy of the presented methods as well as its capability to create and learn new tissue classes.
Databáze: OpenAIRE
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