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This paper presents a method for objective segmentation in two-segmental situations based on the Heaviside function and a non-linear regression where the intersection between segments is a constraint.
Background and Objective: Segmentation of the femur from the hip joint in computed tomography (CT) is an important preliminary step in hip surgery planning and simulation.
The proposed segmentation scheme represents a step toward a scalable approach for objective segmentation of the habenula suitable for both morphological evaluation and habenula seed region selection in functional and diffusion MRI applications.
We report an objective segmentation technique for automated quantification of the contrast effect and confirm that this faithfully reflects tissue levels of VCAM-1 transcript using real time quantitative real time RT-PCR.
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PET images that more accurately reflect the underlying radiotracer distribution allow for more reliable automatic or objective tumor segmentation.
This measure was shown in [26] to be an effective objective measure of segmentation quality.
The objective of this segmentation is to identify relatively homogeneous groups of customers.
Nevertheless, as the main objective was to compare segmentation algorithms, the results remain informative.
In this work, we have developed an objective semi-automated habenula segmentation scheme consisting of histogram-based thresholding, region growing, geometric constraints, and partial volume estimation steps.
We expect the more the two segments differ, the larger is the predictive bias of the MNL and thus the larger is the bias of the objective function value if segmentation is neglected.
The objective of using catalyst segmentation and cavities in a micro-reactor is to integrate the advantages of the hetero- and homogeneous reactions to enhance fuel conversion and to promote complete combustion in a confined distance.
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