2006/11 Vol. J89 D No. 11 [10] [15], [16] [5], [17] [21] Tamura [5] RGB 2% [17] Yannis [18] Li [19] 35 1% Hoover [20] fuzzy convergence 50 82% %
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1 a) Recognition of Optic Nerve Head Using Blood-Vessel-Erased Image and Its Application to Production of Simulated Stereogram in Computer-Aided Diagnosis System for Retinal Images Toshiaki NAKAGAWA a), Yoshinori HAYASHI, Yuji HATANAKA, Akira AOYAMA, Yutaka MIZUKUSA,AkihiroFUJITA, Masakatsu KAKOGAWA, Takeshi HARA,HiroshiFUJITA, and Tetsuya YAMAMOTO CAD Black-top-hat RGB P % 73/78 CAD 1. Department of Intelligent Image Information, Graduate School of Medicine, Gifu University, 1 1 Yanagido, Gifushi, Japan TAK Co., Ltd., Kono, Ogaki-shi, Japan Department of Electronic Control Engineering, Gifu National College of Technology, Kamimakuwa, Motosu-shi, Japan Kowa Company, Ltd., Shinmiyakoda, Hamamatsushi, Japan a) nakagawa@fit.info.gifu-u.ac.jp [1] computer-aided diagnosis CAD [2] [4] [5] [6] [7] CAD [8] [14] [8] [10] [10] [14] [13], [14] D Vol. J89 D No. 11 pp c
2 2006/11 Vol. J89 D No. 11 [10] [15], [16] [5], [17] [21] Tamura [5] RGB 2% [17] Yannis [18] Li [19] 35 1% Hoover [20] fuzzy convergence 50 82% % Mendels [21] Closing 9 [22] [23] 1 [24] 2492
3 [25] Ballester [26] [27] BPLP back projection for lost pixels 2. 1 P- [28] Kowa nonmyd 7 1 Fig. 1 Flowchart of the overall process. JPEG 24 bitrgb (a) [29] 2. 1 RGB RGB G G Black-top-hat Black-top-hat (x, y) z Dilation Erosion Closing Black-top-hat 3 2(b) (d) RGB Black-top-hat Dilation 3(b) Dilation 2(b)Erosion 3(c) 2493
4 2006/11 Vol. J89 D No (a) (b) Dilation (c) Erosion (d) Black-top-hat (e) G 2 (f) (g) (h) Fig. 2 Result images on each process. (a) Original image, (b) Dilation, (c) Erosion, (d) Black-top-hat conversion, (e) Binarization on G image, (f) Deletion of blood vessel regions, (g) Erase of blood vessels, (h) Blood vessel image. 3 Black-top-hat (a) (b) Dilation (c) Erosion (d) (e) Black-top-hat Fig. 3 Transition of intensity profile curve in the black-top-hat conversion. (a) Original image, (b) Dilation, (c) Erosion, (d) Original and Closing processed image, (e) Black-top-hat conversion. 2(c) Dilation Erosion Closing Closing 3(d) Closing 2494
5 2(d) G 2(d) G 2 2(e) (f) RGB 4 P d n n P k (k =1,...,n) l k n P k l k k=1 P = (1) n 1 l k k=1 d m(m <n) m m m 1 4 Fig. 4 Interpolation for erased regions. d n m 31 2(g) 2(a) 2. 3 R G B P- [28] 2 R R 5 RGB 3 P- 2 3 RGB 2 3 RGB
![2006/11 Vol. J89 D No. 11 R P- 10 1 [29] 2.](/docs-images/93/114344721/images/6-0.jpg "2 6 6 1 2. 4 [29] 2(h) 2(g) 3.")
6 2006/11 Vol. J89 D No. 11 R P [29] [29] 2(h) 2(g) (b) Black-top-hat Black-top-hat 2 6(c) Closing BPLP [27] 7 1 Table 1 Number of images used in the optic nerve head recognition. Fig. 5 5 Procedure of the recognition of optic nerve head. 2496
7 論文 眼底画像診断支援システムのための血管消去画像を用いた視神経乳頭の自動認識及び擬似立体視画像生成への応用 Fig. 6 図 6 結果画像 (a) 原画像 (b) 血管抽出 (c) 血管消去 (d) 乳頭認識 Result images. (a) Original image, (b) Blood vessel extraction, (c) Blood vessel erasing, and (d) Recognition of optic nerve head. 示す Closing 処理では血管領域は良好に消去されて いるものの 同じ画素値をもつ画素が構造要素の形状 で並ぶ部分が多くあるため補間された領域が目立って いる 提案手法による血管消去画像では 補間した領 域が背景領域の画素値に応じて段階的に変化した画 素値をもつため 視認されにくく また 原画像の網 膜の情報を多く残していた BPLP 法による画像補 間を行った結果では 視神経乳頭付近で多くのアーチ ファクトが存在した これは 学習サンプルに用いた 局所画像と視神経乳頭付近の局所画像の自己相関性 が低いことが原因と考えられる 背景に比較的大きな 画素値の変化がない画像であれば 本手法で推定され る画素値は ある程度妥当であると考えられる 対象 物の消去の精度は 対象物の抽出精度に大きく依存 する Black-top-hat 変換に用いる構造要素の選択に は 消去する対象物の形状を考慮して決定するが 構 造要素より大きい領域は抽出不足となり 小さい領域 図 7 画像補間の結果 (a) 原画像 (b) 提案手法 (c) Closing 処理画像の画素値を用いて補間した画像 (d) BPLP 法 Fig. 7 Results of image interpolation. (a) Original image, (b) Proposed method, (c) Interpolated image using pixels on closing processed image, and (d) BPLP method. は抽出過多になる傾向がある このため 対象物の大 きさ及び形状が未知である場合には適していないと いえる また 対象物の大きさが画像と比較して大き く 補間の際の内挿点から観測点までの距離が長くな る場合は 観測点を増加しても内挿点の画素値の推測 精度が低くなると考えられる ただし 眼底写真にお 2497
8 2006/11 Vol. J89 D No Table 2 Recognition results of optic nerve heads in each method. Black-top-hat 6(d) RGB RGB 2 1 RGB RGB 2 94% 73/78 R % 70/78 R RGB 2 8 Fig. 8 Failure cases of the optic nerve head recognition % CAD 9 9(b) Closing 9(c) P
![blood-vesseleliminated image. [30], [31] 4. 10 (a) (b) Fig. 10 Images for stereophonic vision.](/docs-images/93/114344721/images/9-1.jpg "(a) Original image and (b) Simulated stereogram. P- 10 2 z 2 78 94% (73/78) 100% 1 CAD CAD [1] pp.")
9 9 (a) (b) (c) Fig. 9 Result of optic nerve head recognition. (a) Original image, (b) Result with using original image, and (c) Result with using blood-vesseleliminated image. [30], [31] (a) (b) Fig. 10 Images for stereophonic vision. (a) Original image and (b) Simulated stereogram. P z % (73/78) 100% 1 CAD CAD [1] pp.2 4, [2] 1 vol.77, no.8, pp , [3] 2499
10 2006/11 Vol. J89 D No. 11 vol.39 p.479, [4] F. Zana and J.C. Klein, Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation, IEEE Trans. Image Process, vol.10, no.7, pp , [5] S. Tamura, Y. Okamoto, and K. Yanashima Zerocrossing interval correction in tracing eye-fundus blood vessels Pattern Recognit., vol.21, no.3, pp , [6] 1 vol.12, no.3, pp , [7] Dvol.J64-D, no.8, pp , Aug [8] vol.16, no.39, pp , [9] vol.56, no.3, pp , [10] J. Hayashi, T. Kunieda, J. Cole, R. Soga, Y. Hatanaka, M. Lu, T. Hara, and H. Fujita A development of computer-aided diagnosis system using fundus images Proc. 7th International Conference on Virtual Systems and MultiMedia (VSMM), pp , [11] Automatic distribution and shape analysis of blood vessels on retinal images MI , [12] vol.42, no.4, pp , [13] Y. Hatanaka, X. Zhou, T. Hara, H. Fujita, Y. Hayashi, A. Aoyama, and T. Yamamoto Automated detection algorithm for abnormal vessels on retinal fundus images, Proc. 10th International Conference on Virtual Systems and MultiMedia (VSMM), pp , [14] Y. Hatanaka, T. Nakagawa, Y. Hayashi, A. Aoyama, T.Hara,H.Fujita,T.Yamamoto,Y.Mizukusa,A. Fujita, and M. Kakogawa Automated detection algorithm for arteriolar narrowing on fundus images Proc. 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), paper#291, [15] EBM vol.46, pp , [16] [17] Dvol.J64-D, no.10, pp , Oct [18] Y. Yannis and P.M. Stavros A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering IEEE Trans. Med. Imaging, vol.17, no.2, pp , [19] H. Li and O. Chutatape Boundary detection of opticdiskbyamodifiedasmmethod Pattern Recognit., vol.36, no.9, pp , [20] A. Hoover, M. Goldbaum Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels IEEE Trans. Med. Imaging, vol.22, no.8, pp , [21] F. Mendels, C. Heneghan, and J.P. Thiran, Identification of the optic disk boundary in retinal images using actvive coutours, Proc. Irish Machine Vision and Image Processing Conference, pp , [22] D-IIvol.J84-D-II, no.10, pp , Oct [23] D-IIvol.J87-D-II, no.5, pp , May [24] D-IIvol.J87-D-II, no.5, pp , May [25] [26] C. Ballester, M. Bertalmio, V. Caselles, G. Sapiro, and J. Verdera, Filling-in by joint iterpolation of vector fields and gray levels, IEEE Trans. Image Process., vol.10, no.8, pp , [27] BPLP D-IIvol.J85-D-II, no.3, pp , March [28] [29] [30] 3 D-II vol.j76-d-ii, no.2, pp , Feb [31] 3 MI99-9,
11 ) CAD International Glaucoma Society 2501
2007/8 Vol. J90 D No. 8 Stauffer [7] 2 2 I 1 I 2 2 (I 1(x),I 2(x)) 2 [13] I 2 = CI 1 (C >0) (I 1,I 2) (I 1,I 2) Field Monitoring Server
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258 5) GPS 1 GPS 6) GPS DP 7) 8) 10) GPS GPS 2 3 4 5 2. 2.1 3 1) GPS Global Positioning System
Vol. 52 No. 1 257 268 (Jan. 2011) 1 2, 1 1 measurement. In this paper, a dynamic road map making system is proposed. The proposition system uses probe-cars which has an in-vehicle camera and a GPS receiver.
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3 2 2 (1) (2) (3) (4) 4 4 AdaBoost 2. [11] Onishi&Yoda [8] Iwashita&Stoica [5] 4 [3] 3. 3 (1) (2) (3)
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4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q
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270 MEDICAL IMAGING TECHNOLOGY Vol.24 No.4 September 2006 * 研究論文 * 眼底画像における高血圧症診断支援のための血管交叉部の自動解析 高橋亮 畑中裕司 *2 中川俊明 林佳典,3 青山陽 水草豊 *4 藤田明宏 *4 加古川正勝 *3 原武史 藤田広志 要 旨 本研究では, 高血圧性網膜症に伴う細動脈硬化性変化の診断所見である血管の交叉現象を眼科医が診断することを支援するために自動解析する手法を提案する.
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本文6(599) (Page 601)
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