Content-based Retrieval of Medical Images: Landmarking, Indexing, and Relevance Feedback (Paperback)

Content-based image retrieval (CBIR) is the process of retrieval of images from a database that are similar to a query image, using measures derived from the images themselves, rather than relying on accompanying text or annotation. To achieve CBIR, the contents of the images need to be characterized by quantitative features; the features of the query image are compared with the features of each image in the database and images having high similarity with respect to the query image are retrieved and displayed. CBIR of medical images is a useful tool and could provide radiologists with assistance in the form of a display of relevant past cases. One of the challenging aspects of CBIR is to extract features from the images to represent their visual, diagnostic, or application-specific information content. In this book, methods are presented for preprocessing, segmentation, landmarking, feature extraction, and indexing of mammograms for CBIR. The preprocessing steps include anisotropic diffusion and the Wiener filter to remove noise and perform image enhancement. Techniques are described for segmentation of the breast and fibroglandular disk, including maximum entropy, a moment-preserving method, and Otsu's method. Image processing techniques are described for automatic detection of the nipple and the edge of the pectoral muscle via analysis in the Radon domain. By using the nipple and the pectoral muscle as landmarks, mammograms are divided into their internal, external, upper, and lower parts for further analysis. Methods are presented for feature extraction using texture analysis, shape analysis, granulometric analysis, moments, and statistical measures. The CBIR system presented provides options for retrieval using the Kohonen self-organizing map and the k-nearest-neighbor method. Methods are described for inclusion of expert knowledge to reduce the semantic gap in CBIR, including the query point movement method for relevance feedback (RFb). Analysis of performance is described in terms of precision, recall, and relevance-weighted precision of retrieval. Results of application to a clinical database of mammograms are presented, including the input of expert radiologists into the CBIR and RFb processes. Models are presented for integration of CBIR and computer-aided diagnosis (CAD) with a picture archival and communication system (PACS) for efficient workflow in a hospital.

基於內容的圖像檢索（CBIR）是從數據庫中檢索與查詢圖像相似的圖像的過程，使用的度量來源於圖像本身，而不依賴於相關的文本或註釋。為了實現CBIR，需要對圖像的內容進行定量特徵的表徵；將查詢圖像的特徵與數據庫中每個圖像的特徵進行比較，檢索並顯示與查詢圖像相似度高的圖像。醫學圖像的CBIR是一個有用的工具，可以為放射科醫生提供相關過去案例的顯示。CBIR的一個具有挑戰性的方面是從圖像中提取特徵，以表示其視覺、診斷或應用特定的信息內容。本書介紹了用於CBIR的乳房X光攝影的預處理、分割、標誌、特徵提取和索引的方法。預處理步驟包括各向異性擴散和Wiener濾波器，以去除噪音並進行圖像增強。描述了乳房和纖維腺體盤的分割技術，包括最大熵、保持矩法和大津法。描述了在Radon域中通過分析自動檢測乳頭和胸肌邊緣的圖像處理技術。通過使用乳頭和胸肌作為標誌，將乳房X光攝影分為內部、外部、上部和下部進行進一步分析。介紹了使用紋理分析、形狀分析、顆粒分析、矩和統計量進行特徵提取的方法。所提供的CBIR系統提供了使用Kohonen自組織映射和k最近鄰方法進行檢索的選項。描述了將專家知識納入CBIR中以減少語義差距的方法，包括用於相關反饋（RFb）的查詢點移動方法。描述了以精確度、召回率和檢索的相關加權精確度為衡量標準的性能分析。介紹了對乳房X光攝影的臨床數據庫的應用結果，包括專家放射科醫生對CBIR和RFb過程的參與。提出了將CBIR和計算機輔助診斷（CAD）與圖像存檔和通信系統（PACS）整合的模型，以實現醫院的高效工作流程。