{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T23:23:55Z","timestamp":1782516235013,"version":"3.54.5"},"reference-count":40,"publisher":"Wiley","issue":"7Part1","license":[{"start":{"date-parts":[[2006,6,26]],"date-time":"2006-06-26T00:00:00Z","timestamp":1151280000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2006,7]]},"abstract":"We developed an advanced computer\u2010aided diagnostic (CAD) scheme for the detection of various types of lung nodules on chest radiographs intended for implementation in clinical situations. We used 924 digitized chest images (992 noncalcified nodules) which had a matrix size with a 1024 gray scale. The images were divided randomly into two sets which were used for training and testing of the computerized scheme. In this scheme, the lung field was first segmented by use of a ribcage detection technique, and then a large search area ( matrix size) within the chest image was automatically determined by taking into account the locations of a midline and a top edge of the segmented ribcage. In order to detect lung nodule candidates based on a localized search method, we divided the entire search area into regions of interest (ROIs: matrix size). In the next step, each ROI was classified anatomically into apical, peripheral, hilar, and diaphragm\/heart regions by use of its image features. Identification of lung nodule candidates and extraction of image features were applied for each localized region ( matrix size), each having its central part ( matrix size) located at a position corresponding to a ROI that was classified anatomically in the previous step. Initial candidates were identified by use of the nodule\u2010enhanced image obtained with the average radial\u2010gradient filtering technique, in which the filter size was varied adaptively depending on the location and the anatomical classification of the ROI. We extracted 57 image features from the original and nodule\u2010enhanced images based on geometric, gray\u2010level, background structure, and edge\u2010gradient features. In addition, 14 image features were obtained from the corresponding locations in the contralateral subtraction image. A total of 71 image features were employed for three sequential artificial neural networks (ANNs) in order to reduce the number of false\u2010positive candidates. All parameters for ANNs, i.e., the number of iterations, slope of sigmoid functions, learning rate, and threshold values for removing the false positives, were determined automatically by use of a bootstrap technique with training cases. We employed four different combinations of training and test image data sets which was selected randomly from the 924 cases. By use of our localized search method based on anatomical classification, the average sensitivity was increased to 92.5% with 59.3 false positives per image at the level of initial detection for four different sets of test cases, whereas our previous technique achieved an 82.8% of sensitivity with 56.8 false positives per image. The computer performance in the final step obtained from four different data sets indicated that the average sensitivity in detecting lung nodules was 70.1% with 5.0 false positives per image for testing cases and 70.4% sensitivity with 4.2 false positives per image for training cases. The advanced CAD scheme involving the localized search method with anatomical classification provided improved detection of pulmonary nodules on chest radiographs for 924 lung nodule cases.<\/jats:p>","DOI":"10.1118\/1.2208739","type":"journal-article","created":{"date-parts":[[2006,7,12]],"date-time":"2006-07-12T22:29:20Z","timestamp":1152743360000},"page":"2642-2653","source":"Crossref","is-referenced-by-count":68,"title":["Computer\u2010aided diagnostic scheme for the detection of lung nodules on chest radiographs: Localized search method based on anatomical classification"],"prefix":"10.1002","volume":"33","author":[{"given":"Junji","family":"Shiraishi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenji","family":"Suzuki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roger","family":"Engelmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunio","family":"Doi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2006,6,26]]},"reference":[{"key":"e_1_2_7_2_1","unstructured":"Cancer Facts & Figures2004 The American Cancer Society Inc."},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.151.2.6324279"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140\u20106736(97)08229\u20109"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.lungcan.2004.06.007"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140\u20106736(99)06093\u20106"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMcp042630"},{"key":"e_1_2_7_8_1","unstructured":"UNSCEAR2000. 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