JP5937175B2 - 領域拡張アルゴリズム - Google Patents
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- 238000000034 method Methods 0.000 claims description 30
- 238000004590 computer program Methods 0.000 claims description 9
- 230000011218 segmentation Effects 0.000 claims description 8
- 238000002591 computed tomography Methods 0.000 claims description 7
- 210000003484 anatomy Anatomy 0.000 claims description 5
- 210000003437 trachea Anatomy 0.000 claims description 5
- 230000000977 initiatory effect Effects 0.000 claims 1
- 238000002372 labelling Methods 0.000 claims 1
- 210000004072 lung Anatomy 0.000 description 7
- 238000004458 analytical method Methods 0.000 description 3
- 238000012805 post-processing Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000007547 defect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000006837 decompression Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000010339 dilation Effects 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 230000003902 lesion Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
- G06T2207/20156—Automatic seed setting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30061—Lung
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- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
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- Apparatus For Radiation Diagnosis (AREA)
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Description
本出願は、「領域−拡張アルゴリズム」(Region-Growing Algorithm)の名称で2010年2月1日に出願された米国仮出願番号第61/300,423号の優先権を主張し、その全体が参照として本明細書中に組み込まれる。
以下に、今回の手続補正において特許請求の範囲より削除した本出願の当初請求項8ないし20に相当する内容を付記する。
[8]
プロセッサを備える、解剖学的構造の仮想モデルを作成するための領域拡張アルゴリズムにおいて、前記プロセッサは、
複数の二次元画像から作成された三次元画像体積を提供し、
閾値を超えるものが、画像化されたボクセルが組織を表していないことを示す閾値を設定し、
前記三次元画像体積中のシード点ボクセルを、出発点として選択し、
複数の第1の反復を実行し、
セグメント化されたボクセルの数がゼロに等しい場合を検出し、
前記セグメント化されたボクセルの数がゼロに等しくなるまで、漏れのあるオブジェクト上に複数の第2の反復を実行する
ように構成されている、領域拡張アルゴリズム。
[9]
前記複数の第1の反復のそれぞれは、複数の方向において前記シード点に隣接して位置している、前記閾値を満たすか、または超えるボクセルをセグメント化することを含む、上記8記載の領域拡張アルゴリズム。
[10]
前記複数の第1の反復のそれぞれは、その反復においてセグメント化されたボクセルの数を決定し、前記ボクセルの数がゼロより大きい場合、前記ボクセルの数が漏れを示すのに十分なほど大きいか否かを決定することをさらに含む、上記9記載の領域拡張アルゴリズム。
[11]
前記複数の第1の反復のそれぞれは、漏れのあるボクセルを除外することと、前記漏れのあるボクセルを、後処理のための漏れのあるオブジェクトとして記憶させることとをさらに含む、上記10記載の領域拡張アルゴリズム。
[12]
前記複数の第1の反復のそれぞれは、何の漏れも示されない場合、次の反復のためのシード点としてセグメント化されたボクセルを識別することをさらに含む、上記11記載の領域拡張アルゴリズム。
[13]
前記複数の第2の反復のそれぞれは、所定の漏れのあるオブジェクトに対して漏れを検出した第1の反復におけるボクセルを、シード点として選択することを含む、上記8記載の領域拡張アルゴリズム。
[14]
前記複数の第2の反復は、前記閾値を減少させることをさらに含む、上記13記載の領域拡張アルゴリズム。
[15]
前記複数の第2の反復は、複数の方向において前記シード点に隣接して位置している、前記閾値を満たすか、または超える、ボクセルをセグメント化することを含む、上記14記載の領域拡張アルゴリズム。
[16]
前記複数の第2の反復は、その反復においてセグメント化されたボクセルの数を決定し、前記ボクセルの数がゼロより大きい場合、前記ボクセルの数が漏れを示すのに十分なほど大きいか否かを決定することをさらに含む、上記15記載の領域拡張アルゴリズム。
[17]
前記複数の第2の反復は、漏れのあるボクセルを除外することと、前記漏れのあるボクセルを、後処理のための漏れのあるオブジェクトとして記憶させることとをさらに含む、上記16記載の領域拡張アルゴリズム。
[18]
前記複数の第2の反復は、何の漏れも示されない場合、次の反復のためのシード点としてセグメント化されたボクセルを識別することをさらに含む、上記17記載の領域拡張アルゴリズム。
[19]
プロセッサを備える、三次元画像体積から解剖学的構造の仮想モデルを作成するための領域拡張アルゴリズムにおいて、前記プロセッサは、
閾値として、流体を示す画像ボクセルの濃度を使用し、
前記閾値を満たすボクセルをセグメント化し、
前記解剖学的構造の内部管腔または内腔の仮想モデルを作成し、
セグメント化されたボクセルをシード点として使用して、一連の反復を実行し、
複数の方向において前記シード点に隣接する、セグメント化されていないボクセルを分析し、
前記複数の方向における前記シード点に隣接する、セグメント化されていないボクセルが無くなるまで、次の反復に対するシード点として、反復からの新しくセグメント化されたボクセルを使用して前記反復を繰り返し、
前記閾値を満たすが、公知の解剖学的構造に対する適合に基づく1つ以上の基準を満たすことができない、漏れとなったオブジェクトを形成するボクセルを分離すること
ように構成されている、領域拡張アルゴリズム。
[20]
前記プロセッサは、減少された閾値を使用して、前記漏れとなったオブジェクトをさらに再セグメント化し、前記減少された閾値を満たすが、前記漏れとなったオブジェクトを形成するボクセルを分離する、上記19記載の領域拡張アルゴリズム。
Claims (7)
- 漏れの量を制御するための方法をプロセッサに実行させるためのコンピュータプログラムにおいて、前記方法は、
データのセグメント化のための出発点の選択を受け取ることと、
複数の領域拡張反復を実行し、それぞれの反復は、単一のボクセル層の拡張として規定されることと、
現在の反復において、セグメント化されたボクセルの数を決定することと、
前記出発点の周囲の隣接するボクセルを指定することにより、伸長プロセスを開始することと、
前記セグメント化されたボクセルをカウントして分析し、前記セグメント化されたボクセルが所定の率に従って増加しているか否かを決定することと、
前記セグメント化されたボクセルが所定の率に従って増加していない場合、漏れを含まない前の反復のセグメント化されたボクセルを識別して記録することと、
前記前の反復の前記セグメント化されたボクセルのラベル付けを実行することと、
前記前の反復の前記セグメント化されたボクセルを選択することと、
前記前の反復中で見つけられた受け入れ可能なセグメント化されたボクセルに基づいて、ボクセルリストを作成することと
を含む、コンピュータプログラム。 - 前記出発点は、気管内の点である、請求項1記載のコンピュータプログラム。
- 前記出発点は、高い閾値を満たす、前記気管内の単一のボクセルである、請求項2記載のコンピュータプログラム。
- 前記単一のボクセルは、26個の隣接するボクセルによって取り囲まれており、このうち17個のボクセルは拡張方向に延びている、請求項3記載のコンピュータプログラム。
- 前記データは、コンピュータ断層撮影法(CT)スキャンデータである、請求項1記載のコンピュータプログラム。
- 前記出発点の遠位の方向にあるボクセルがセグメント化される、請求項1記載のコンピュータプログラム。
- 前記ボクセルリストを使用して、解剖学的構造の仮想モデルを作成する、請求項1記載のコンピュータプログラム。
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US30042310P | 2010-02-01 | 2010-02-01 | |
US61/300,423 | 2010-02-01 |
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JP2012550530A Division JP5650248B2 (ja) | 2010-02-01 | 2011-02-01 | 領域拡張アルゴリズム |
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JP2016095154A Division JP6189477B2 (ja) | 2010-02-01 | 2016-05-11 | 領域拡張アルゴリズム |
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JP2015091317A JP2015091317A (ja) | 2015-05-14 |
JP5937175B2 true JP5937175B2 (ja) | 2016-06-22 |
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JP2012550530A Expired - Fee Related JP5650248B2 (ja) | 2010-02-01 | 2011-02-01 | 領域拡張アルゴリズム |
JP2014229546A Expired - Fee Related JP5937175B2 (ja) | 2010-02-01 | 2014-11-12 | 領域拡張アルゴリズム |
JP2016095154A Expired - Fee Related JP6189477B2 (ja) | 2010-02-01 | 2016-05-11 | 領域拡張アルゴリズム |
JP2017150213A Expired - Fee Related JP6534707B2 (ja) | 2010-02-01 | 2017-08-02 | 領域拡張アルゴリズム |
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JP2017150213A Expired - Fee Related JP6534707B2 (ja) | 2010-02-01 | 2017-08-02 | 領域拡張アルゴリズム |
Country Status (6)
Country | Link |
---|---|
US (6) | US8428328B2 (ja) |
EP (2) | EP3407261A3 (ja) |
JP (4) | JP5650248B2 (ja) |
AU (1) | AU2011210257B2 (ja) |
CA (1) | CA2788406C (ja) |
WO (1) | WO2011092594A2 (ja) |
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