JP5927180B2 - 画像の異形を識別するための画像データの処理方法、システムおよびプログラム - Google Patents
画像の異形を識別するための画像データの処理方法、システムおよびプログラム Download PDFInfo
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Description
ここで、h()とは単調関数である。
ここで、
であり、Nとは表示ポイントの数である
Claims (19)
- 画像の異形を識別するためにコンピュータ装置によって実行される画像データの処理方法であって、当該方法は、
第1の異形サイズスケールに関するサブサンプルファクターによってデジタル画像をサブサンプリングし、これにより、サブサンプルされた画像を発生し、
サブサンプルされた画像を平滑化し、平滑化された画像を発生させ、
平滑化された画像の各画素についての二次導関数の負の最小値を決定し、
各画素についての前記最小値に基づき、下向きの凸状の曲率を有する各画素を決定し、
下向きの凸状の曲率を有する各8つの近隣が接続された画素を結合し、それぞれの初期の異形領域を識別し、
前記初期の異形領域の各々についてのそれぞれの前記二次導関数の負の最大値に基づき、最も大きな下向きの凸状の曲率を有する初期の異形領域を選択し、
各選択された異形領域についての1つもしくはそれ以上の特徴を抽出し、
前記抽出された1つもしくはそれ以上の分類特徴に基づき選択された異形領域を分類する、方法。 - 前記画像は、マンモグラムであり、前記異形は、悪性の可能性のある質量であり、1つもしくはそれ以上の分類特徴が、検索幅インデックス、乳頭距離およびy位置、信号対ノイズ比(SNR)、オブジェクトランク、相対的な弧の長さ、ディップSNR、全体のSNR、他の側のSNRおよびこれらの組合せからなるグループから選択される、請求項1に記載の方法。
- 前記方法はさらに、複数の異なる異形サイズのスケールについて前記方法を繰り返すことを含む、請求項1に記載の方法。
- 前記方法はさらに、サブサンプリング前に、デジタル画像から明るい領域を取り除くことを含む、請求項1に記載の方法。
- 前記方法はさらに、明るい領域を取り除いた後であってかつサブサンプリング前に、デジタル画像の強度を平坦化することを含む、請求項4に記載の方法。
- 各画素についての二次導関数の負の最小値を決定することは、
各画素の中心でありかつ大きなスケールで線形に離間された3つの画像地点での二次導関数を計算し、
各画素の周囲の複数の配向について二次導関数を計算することを繰り返すことを含む、請求項1に記載の方法。 - 前記方法はさらに、平滑化された画像のノイズマップを確立することを含み、当該ノイズマップの確立は、
各画素の中心でありかつ大きなスケールで線形に離間された3つの画像地点での二次導関数を計算し、
各画素の周囲の複数の配向について二次導関数を計算することを繰り返し、かつ
各画素のノイズマップとして、複数の配向からの二次導関数の最小の絶対値を使用することを含む、請求項1に記載の方法。 - 前記方法はさらに、結合の後であってかつ選択の前に、前記初期の異形領域の各々を縮退しかつ拡張することを含む、請求項1に記載の方法。
- 前記方法はさらに、抽出前に、選択された初期の異形領域の各々の境界をリファイニングする、請求項1に記載の方法。
- 画素を含む画像の異形を識別するシステムであって、
デジタル画像をサブサンプリングし、サブサンプルされた画像を生じさせる画像サブサンプラーと、
サブサンプルされた画像を平滑化し、平滑化された画像を生じさせる画像平滑化するものと、
平滑化された画像の近隣の下向きの凸状の曲率を検出しかつ結合し、異形領域を生じさせる、曲率シグネチャー検出器と、
最も大きい下向きの凸状曲率を有する異形領域を選択し、選択された異形領域を生じさせる異形セレクタと、
選択された異形領域の各々についての1つもしくはそれ以上の分類特徴を抽出する特徴抽出器と、
それぞれの抽出された1つもしくはそれ以上の分類特徴についての1つもしくはそれ以上のしきい値に基づき選択された異形領域を分類する分類器とを有し、
前記曲率シグネチャー検出器は、前記平滑化された画像の各画素についての二次導関数の負の最小値を決定し、かつ前記平滑化された画像の各画素についての前記最小値に基づき下向きの凸状の曲率を検出する、システム。 - 前記画像は、マンモグラムであり、前記異形は、悪性の可能性のある質量であり、1つもしくはそれ以上の分類特徴が、検索幅インデックス、乳頭距離およびy位置、信号対ノイズ比(SNR)、オブジェクトランク、相対的な弧の長さ、ディップSNR、全体のSNR、他の側のSNRおよびこれらの組合せからなるグループから選択される、請求項10に記載のシステム。
- 前記画像サブサンプラーは、第1の異形サイズのスケールに関連するサブサンプルファクターを使用する、請求項10に記載のシステム。
- 異形セレクタは、異形領域の各々についての二次導関数の負の最大値に基づき、最も大きい凸状の下に向かう曲率を決定する、請求項10に記載のシステム。
- 異形を識別するコンピュータプログラムであって、当該コンピュータプログラムは、コンピュータプログラムが埋め込まれた、一時的でないコンピュータ読出し可能な媒体を有している、前記コンピュータプログラムであって、
第1の異形サイズのスケールに関連されたサブサンプルファクターによってデジタル画像をサブサンプルし、これによりサブサンプルされた画像を生じさせるコンピュータプログラムコードと、
サブサンプルされた画像を平滑化し、平滑化された画像を生じさせるコンピュータプログラムコードと、
前記平滑化された画像の各画素についての二次導関数の負の最小値を決定するコンピュータプログラムコードと、
各画素についての前記最小値に基づき、下向きの凸状の曲率を有する各画素を決定するコンピュータプログラムコードと、
下向きの凸状の曲率を有する8つの近隣の接続された画素を結合しそれぞれ初期の異形領域を識別するコンピュータプログラムコードと、
初期の異形領域の各々についてのそれぞれの二次導関数の負の最大値に基づき最も大きい下向きの凸状の曲率を有する初期の異形領域を選択するコンピュータプログラムコードと、
選択された異形領域の各々についての1つもしくはそれ以上の分類特徴を抽出するコンピュータプログラムコードと、
前記抽出された1つもしくはそれ以上の分類特徴に基づき選択された異形領域を分類するコンピュータプログラムコードと、を有するコンピュータプログラム。 - 前記画像は、マンモグラムであり、前記異形は、悪性の可能性のある質量であり、1つもしくはそれ以上の分類特徴が、検索幅インデックス、乳頭距離およびy位置、信号対ノイズ比(SNR)、オブジェクトランク、相対的な弧の長さ、ディップSNR、全体のSNR、他の側のSNRおよびこれらの組合せからなるグループから選択される、請求項14に記載のコンピュータプログラム。
- 前記コンピュータプログラムはさらに、複数の異なる異形サイズのスケールについて異形の識別を繰り返すコンピュータプログラムコードを含む、請求項14に記載のコンピュータプログラム。
- 前記コンピュータプログラムはさらに、サブサンプリングの前にデジタル画像から明るい領域を取り除くコンピュータプログラムコードを含む、請求項14に記載のコンピュータプログラム。
- コンピュータプログラムはさらに、サブサンプリングの前にデジタル画像の強度を平坦化するコンピュータプログラムコードを含む、請求項17に記載のコンピュータプログラム。
- 前記各画素についての負の二次導関数の最小値を決定するコンピュータプログラムコードは、
各画素の中心でありかつ大きなスケールで線形に離間された3つの画像地点での二次導関数を計算するプログラムコードと、
各画素の周囲の複数の配向について二次導関数を計算することを繰り返すプログラムコードとを含む、請求項14に記載のコンピュータプログラム。
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