An Adaptive Observer via Optimal Control Law

This paper deals with the adaptive observer which estimates the states and parameters of unknown system. It is shown that the adaptive observer problem is reduced to the identification of the transformation matrix for an arbitrary designable observer. Moreover, the adaptive process of the unknown parameters is reduced to the linear optimal regulator problem. As the result, a new method is presented to obtain an appropriate adaptive process with good insight. And, in this identification, a linear filter is found to be also useful against noises in input-output data. To achieve high accuracy, a particular nonlinear filtering can improve SN ratio only in the direction of the unknown vector. Even if SN ratio of input-output data has zero dB, sufficient accuracy can be accomplished within suitable correction time. This design algorithm seems to be rather straightforward and practical. Since input sequence is required to be only sufficiently general, the method is applicable to on-line identification also

Regeneration of Bone- and Tendon/Ligament-Like Tissues Induced by Gene Transfer of Bone Morphogenetic Protein-12 in a Rat Bone Defect

Members of the bone morphogenetic protein (BMP) family have diverse physiological roles. For instance, BMP-2 stimulates osteogenesis, while BMP-12 induces the formation of tendon/ligament-like tissues. Here, we designed a study to determine whether BMP-12 has bone and/or cartilage regeneration abilities similar to those of BMP-2. We implanted plasmid vectors encoding either BMP-2 or BMP-12 in rats with femur defects, and monitored the bone healing process for 8-weeks. The BMP-12 transgene induced prominent fibrogenesis by 2 weeks, with bone substitution occurring by 8 weeks. BMP-2, however, was associated predominantly with osteogenesis throughout the 8 week period. Thus, we conclude that BMP-12 does not function similarly to BMP-2 during bone healing. Further work is needed to better understand the mechanisms by which it stimulates bony growths to replace the connective tissues formed during the first stages of bone healing

Watershed法を用いた大腸拡大内視鏡画像からのpit pattern抽出

特別企画「学生ポスターセッション

NBIを用いた大腸拡大内視鏡画像からの血管領域の抽出

平成19年度電気・情報関連学会中国支部第58回連合大会発表資料。開催地:広島大学 ; 開催日:2007年10月20

Feature extraction from images of endoscopic large intestine

In this paper, we propose feature extraction methods from two types of images of endoscopic large intestine taken by a colonoscopy for diagnosis of colon cancer. Today, there are two observation methods. One is staining surface of large intestine. The other is colonoscopy using Narrow Band Imaging (NBI) system, a new feature of endoscope. We describe extraction methods of features for each observation method so that the features may be used to estimate colon cancer staging from an observed image. Pit pattern is a texture that appears on the surface of stained intestine and they are categorized and used for diagnosis. Thus, we extract pits from an endoscope image to analyze patterns. First, color edge of the image is extracted, then watershed segmentation is applied. In the result, pits are roughly extracted. NBI system can observe vasucular structure under the surface of large intestine. The vascular structure can be used to estimate cancer staging. A vascular area is roughly extracted by adaptive binarization, then the fine shape of vascular area is extracted by the level set method

A consideration for condition analysis with pit pattern of endoscope image

医療の分野において,大腸の拡大内視鏡を用いた病状レベル推定を行うシステムが要望されている.大腸管腔内への腺管の開口部の形態をpit patternと呼び,これは内視鏡診断の際に組織診断を推定する基準とされている.そこで本論文ではpit patternを画像から抽出し,その特徴量を算出することで病状レベルの推定を行う手法について述べる.大腸観察の際には病変部を染色しpit patternを強調する.そこで,画像中の色エッジを抽出し,watershed法を用いて領域分割を行いpit patternを抽出する.各pit patternの特徴量を算出し,病状レベルとの相関について検討する.Diagnosis system of condition level with an endoscope of large intestines is demanded in the field of medical treatment. The form of opening of duct of the gland in a large intestines lumen is called pit pattern, and this is used for an organizational diagnosis with an endoscope. In this paper we consider a method for analysing pit pattern from endoscope image. Pit pattern is extracted by color edge of image, and watershed segmentation. Feature of the extracted pits are examined to find correlation between the condition level and the features

A consideration for condition analysis with pit pattern of endoscope image

MIRU 2007 第10回 画像の認識・理解シンポジウム ポスター資料 ; 開催場所 : 広島市立大学, 広島 ; 開催日時:2007年7月30日～8月1

Feature extraction from images of endoscopic large intestine

The 14th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2008), Poster ; Place : Beppu, Oita, Japan ; Date : January 23-26, 200

Watershed法を用いた大腸拡大内視鏡画像からのpit pattern抽出

2007年電子情報通信学会総合大会 情報システムソサイエティ特別企画「学生ポスターセッション」, ポスター ; 開催場所 : 名城大学, 名古屋 ; 開催日 : 2007年3月21-23