Diagnosis of Lung Nodule Using Reinforcement Learning and Geometric Measures

Silva, Aristófanes Correâ; Silva, Valdeci Ribeiro da; Neto, Areolino de Almeida; Paiva, Anselmo Cardoso de

doi:10.1007/11510888_29

“…We see that for Sphericity shape index selected as example in figure (Figure 5). To calculate the 3D model sphericity, we chose among several equations that use volume and surface area [20] and radii [14] as follow:…”

Section: Shape Indexes Selectedmentioning

confidence: 99%

“…The convexity shape indexes are computed by comparing the volume and surface area of the convex hull to the corresponding 3D model [20]:…”

Section: Shape Indexes Selectedmentioning

confidence: 99%

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2011

IJCSIT

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“…λ is the object's eigenvalues along the main axes The convexity shape indexes are computed by comparing the volume and surface area of the convex hull to the corresponding 3D model [20]:…”

Section: Shape Indexes Selectedmentioning

confidence: 99%

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2011

IJCSIT

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The semantic-based 3D models retrieval systems have become necessary since the increase of 3D models databases. In this paper, we propose a new method for the mapping problem between 3D model data and semantic data involved in semantic based retrieval for 3D models given by polygonal meshes. First, we focused on extracting invariant descriptors from the 3D models and analyzing them to efficient semantic annotation and to improve the retrieval accuracy. Selected shape descriptors provide a set of terms commonly used to describe visually a set of objects using linguistic terms and are used as semantic concept to label 3D model. Second, spatial relationship representing directional, topological and distance relationships are used to derive other high-level semantic features and to avoid the problem of automatic 3D model annotation. Based on the resulting semantic annotation and spatial concepts, an ontology for 3D model retrieval is constructed and other concepts can be inferred. This ontology is used to find similar 3D models for a given query model. We adopted the query by semantic example approach, in which the annotation is performed mostly automatically. The proposed method is implemented in our 3D search engine (SB3DMR), tested using the Princeton Shape Benchmark Database.

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From 3D Model Data to Semantics

Kassimi¹,

Beqqali²

2011

IJCSIT

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The semantic-based 3D models retrieval systems have become necessary since the increase of 3D models databases. In this paper, we propose a new method for the mapping problem between 3D model data and semantic data involved in semantic based retrieval for 3D models given by polygonal meshes. First, we focused on extracting invariant descriptors from the 3D models and analyzing them to efficient semantic annotation and to improve the retrieval accuracy. Selected shape descriptors provide a set of terms commonly used to describe visually a set of objects using linguistic terms and are used as semantic concept to label 3D model. Second, spatial relationship representing directional, topological and distance relationships are used to derive other high-level semantic features and to avoid the problem of automatic 3D model annotation. Based on the resulting semantic annotation and spatial concepts, an ontology for 3D model retrieval is constructed and other concepts can be inferred. This ontology is used to find similar 3D models for a given query model. We adopted the query by semantic example approach, in which the annotation is performed mostly automatically. The proposed method is implemented in our 3D search engine (SB3DMR), tested using the Princeton Shape Benchmark Database.

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Diagnosis of Lung Nodule Using Reinforcement Learning and Geometric Measures

Cited by 3 publications

References 8 publications

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From 3D Model Data to Semantics

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