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Application
2380.2.01
US-20150012794-A1
US-20150205664-A1
US-20100023800-A1
US-8737141-A1
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OPT-9
FLO-2
FLO-5PROV
ONSO3175(B) - Onsemi378
ONSO3305US - Onsemi346
GTS-3DES
FLO-4
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KMN-1PROV
PAT-2
PER-8 PROV
PER-9 PROV
INS-4PROV
HAR-1
CES-16
NXT-5PROV NXT-5, 6, 7, 8
IPP-0051-US14 cross roads
FLO-7PROV
IMI-5PROV
IPP-0050-US35 nextremity
VIL-12
OPT-13
TOY-1
US10998041B1
FSP1845
US6559866B2
Placeholder App
PER-10
KBR-1 1400.2.623
PER-13PROV
PAT-3
US20030023453
RMS-1DES
SMG-1DES
FLO-5
US10318495
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PER-11
US20140066758
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JBR-1
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PON-1PROV, 2PROV, 3PROV
PER-33
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MCC-1
FLO-10
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PER-19
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PER-24
TMC-PAT-1
DAR-2
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PER-21
BRT-PAT-1
TMC-PAT-5
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BRT-PAT-2-PROV
71212.157.USU1
FPR-PAT-1-PROV
71212.158.USP1
RMT-1
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DAR-2PROV
PON-1PROV
PON-2PROV
PON-3PROV
PER-18PROV
TMC-1PROV
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PER-13
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TMC-4PROV
TMC-3
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VEH-1
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TEST.001
E2E-TEST.001
TEST-001
TEST-002
TEST-003
TEST-004
ZED006
FSP1011
GAV-PAT-1-PROV
Matter Number
Paragraph Number
90
Application Number
Reference Case 1
Reference Case 2
Content
Image registration is a process of correctly aligning images; Shape-Based Segmentation: Many methods parametrize a template shape for a given structure, often relying on control points along the boundary. The entire shape is then deformed to match a new image. Two of the most common shape-based techniques are Active Shape Models and Active Appearance Models; Image-Based Segmentation: Some methods initiate a template and refine its shape according to the image data while minimizing integral error measures, like the Active contour model and its variations; Interactive Segmentation: Interactive methods are useful when clinicians can provide some information, such as a seed region or rough outline of the region to segment. An algorithm can then iteratively refine such a segmentation, with or without guidance from the clinician. Manual segmentation, using tools such as a paint brush to explicitly define the tissue class of each pixel, remains the gold standard for many imaging applications. Recently, principles from feedback control theory have been incorporated into segmentation, which give the user much greater flexibility and allow for the automatic correction of errors; Subjective surface Segmentation: This method is based on the idea of evolution of segmentation function which is governed by an advection-diffusion model. To segment an object, a segmentation seed is needed (that is the starting point that determines the approximate position of the object in the image). Consequently, an initial segmentation function is constructed. With the subjective surface method, the position of the seed is the main factor determining the form of this segmentation function; and Hybrid segmentation which is based on combination of methods. (Search "medical image computing" on Wikipedia.com June 24, 2021. CC-BY-SA 3.0 Modified. Accessed June 24, 2021.)
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Added by DJM Jan 2024
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