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    • 63. 发明公开
    • COMPUTERIZED DETECTION OF LUNG NODULES USING ENERGY-SUBTRACTED SOFT-TISSUE AND STANDARD CHEST IMAGES
    • LUNGENNODULEN计算机化检测中减去软组织和乳腺标准
    • EP1025535A1
    • 2000-08-09
    • EP99933560.7
    • 1999-07-21
    • Arch Development Corporation
    • XU, Xin-WeiDOI, KunioMACMAHON, Heber
    • G06K9/00
    • G06T7/0012
    • A method, system and computer readable medium configured for computerized detection of lung abnormalities, including obtaining a standard digital chest image (10) and a soft-tissue digital chest image; generating a first difference image from the standard digital chest image (20) and a second difference image from the soft-tissue digital chest image; identifying candidate abnormalities in the first and second difference images; extracting from the standard digital chest image and the first difference image predetermined first features of each of the candidate abnormalities identified in the first difference image; extracting from the soft-tissue digital chest image and the second difference images predetermined second features of each of the candidate abnormalities identified in the second difference image; analyzing the extracted first features and the extracted second features to identify and eliminate false positive candidate abnormalities respectively corresponding thereto; applying extracted features from remaining candidate abnormalities derived respectively from the first and second difference images and remaining after the elimination of the false positive candidate abnormalities to respective artificial neural networks to eliminate further false positive candidate abnormalities by performing a logical OR operation of the candidate abnormalities derived respectively from the first and second difference images.
    • 65. 发明公开
    • METHODS FOR IMPROVING THE ACCURACY IN DIFFERENTIAL DIAGNOSIS ON RADIOLOGIC EXAMINATIONS
    • 方法以提高在放射性检查鉴别诊断准确性
    • EP0993269A2
    • 2000-04-19
    • EP98936974.9
    • 1998-07-24
    • Arch Development Corporation
    • NISHIKAWA, Robert, M.JIANG, YuleiASHIZAWA, KazutoDOI, Kunio
    • A61B5/05
    • G06T7/0012G06F19/00Y10S128/925
    • A computer-aided method for detecting, classifying, and displaying candidate abnormalities, such as microcalcifications and interstitial lung disease in digitized medical images, such as mammograms and chest radiographs, a computer programmed to implement the method, and a data structure for storing required parameters, wherein in the classifying method candidate abnormalities in a digitized medical image are located, regions are generated around one or more of the located candidate abnormalities, features are extracted from at least one of the located candidate abnormalities within the region and from the region itself, the extracted features are applied to a classification technique, such as an artificial neural network (ANN) to produce a classification result (i.e., probability of malignancy in the form of a number and a bar graph), and the classification result is displayed along with the digitized medical image annotated with the region and the candidate abnormalities within the region. In the detecting method candidate abnormalities in each of a plurality of digitized medical images are located, regions around one or more of the located candidate abnormalities in each of a plurality of digitized medical images are generated, the plurality of digitized medical images annotated with respective regions and candidate abnormalities within the regions are displayed, and a first indicator (e.g., blue arrow) is superimposed over candidate abnormalities comprising of clusters and a second indicator (e.g., red arrow) is superimposed over candidate abnormalities comprising of masses. In a user modification mode, during classification, a user modifies the located candidate abnormalities, the determined regions, and/or the extracted features, so as to modify the extracted features applied to the classification technique and the displayed results, and, during detection, a user modifies the located candidate abnormalities, the determined regions, and the extracted features, so as to modify the displayed results.