巻号一覧
21 巻 (2004)
- 3 号 p. 224-
- 2 号 p. 177-
- 1 号 p. 8-
21 巻, 2 号
May
選択された号の論文の4件中1～4を表示しています
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特別講演資料
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柳原 圭雄2004 年 21 巻 2 号 p. 177-181
発行日: 2004年
公開日: 2005/10/14
ジャーナル フリーPDF形式でダウンロード (1025K) -
林田 真昌2004 年 21 巻 2 号 p. 182-186
発行日: 2004年
公開日: 2005/10/14
ジャーナル フリーPDF形式でダウンロード (867K)
論文
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佐々木 伸司, 木戸 尚治, 岩永 秀幸2004 年 21 巻 2 号 p. 187-193
発行日: 2004年
公開日: 2005/10/14
ジャーナル フリーTo eliminate rib opacities on chest radiographs for quantitatively evaluation of lung opacities, we have developed an automatic algorithm, which sets up sub-regions of interest (sub-ROIs) for avoiding rib opacities in the analysis on chest radiographs. In the first step, a region of interest (ROI) was selected on a chest radiograph, and a 4th-order-polynomial surface was used for background density correction. In the next step, a one-directional Laplacian-Gaussian filter was used for enhancement of rib opacities. In the third step, the image was binarized on the ROI with a multiple threshold method, and then a morphological filter was used for elimination of noise components. Finally, we divided the ROI into some lattice regions and set up sub-ROIs. We calculated the area ratio of rib opacities in the sub-ROIs and judged the existence of rib opacities according to this sub-ROIs. We employed ROC analysis to evaluate the performance of this algorithm. In conclusion, this algorithm could set up sub-ROIs, which avoid rib opacities for image feature analysis of lung opacities.抄録全体を表示PDF形式でダウンロード (896K)
研究速報
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李 文光, 張 学軍, 兼松 雅之, 原 武史, 周 向栄, 藤田 広志, 加藤 博基, 近藤 浩史, 星 博昭2004 年 21 巻 2 号 p. 194-200
発行日: 2004年
公開日: 2005/10/14
ジャーナル フリーCirrhosis of liver is a late stage of progressive liver disease defined as structural distortion of entire liver by fibrosis and parenchymal nodules. As the liver regenerates, fibrous connective tissue forms that may cause gross and microscopic distortion of normal hepatic morphology. In MR images, shape and texture analysis is regarded as an important and useful tool to differentiate cirrhosis from normal liver. In this paper, we propose a method to calculate the shape features from the segmented liver regions on MR image. Meanwhile, the texture features are quantified by using gray-level difference method (GLDM) within the small ROIs (regions of interest) selected in the liver region. The degree of liver cirrhosis is derived from integrating the shape and texture features of liver into a three-layer feed-forward artificial neural network (ANN). A liver is finally regarded as cirrhosis if the percentage of the ROIs with the degree over 0.5 is greater than 50%. The initial result showed that the ANN based method classified liver cirrhosis with a training accuracy of 100% on the 100 ROIs in the training set and that 82% liver cirrhosis and 100% normal cases were correctly differentiated from 18 test cases, which demonstrates the effectiveness of our proposed method.抄録全体を表示PDF形式でダウンロード (1141K)
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