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Automatic Optimization of Chirp Setting Parameters in Medical Ultrasound Contrast Imaging

Journal of Life Sciences 2013年第6期7卷 592-598页

作者： Amira Zaylaa Sebastien Menigot jamal charara Jean-Marc Girault Department of Medical Biophysics and Imaging Universite Franqois Rabelais de Tours UMR-S930 and ERL3106 B.P 3223Tours France INSERM U930 Tours France Department of Physics and Electronics Lebanese University Hadath Campus B.P. 5 Beirut Lebanon IUT Ville d'Avray Universite Paris Ouest Nanterre La Defense B.P. 105 Avray France

Medical ultrasound contrast imaging is a powerful modality undergoing successive developments in the last decade to date. Lately, pulse inversion has been used in both ultrasound tissue harmonic and contrast imaging. However, there was a tradeoff between resolution and penetration. Chirp excitations partially solved the tradeoff, but the chirp setting parameters were not optimized. The present work proposes for the first time combining chirp inversion with ultrasound contrast imaging, with the motivation to improve the contrast, by automatically optimizing the setting parameters of chirp excitation, it is thus an optimal command problem. Linear chirps, 5 μm diameter microbubbles and gradient ascent algorithm were simulated to optimize the chirp setting parameters. Simulations exhibited a gain of 5 dB by automatic optimization of chirp inversion relative to pulse inversion. The automatic optimization process was quite fast. Combining chirp inversion with ultrasound contrast imaging led to a maximum backscattered power permitting high contrast outcomes and optimum parameters.

关键词：线性啁啾医学超声自动优化造影设置成像模拟优化高对比度

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Automated Classification of Segmented Cancerous Cells in Multispectral Images

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Journal of Life Sciences 2013年第4期7卷 358-362页

作者： Alaa Hilal jamal charara Ali Al Houseini Walid Hassan Mohamad Nassreddine Signal and Image Processing Lab Faculty of Sciences I Lebanese University Beirut Lebanon

Automatic reading procedures in colon cells biopsies allow a faster and precise reading of microscopic biopsies. These procedures implement automatic image segmentation in order to classify cell types as cancerous or noncancerous. The authors have developed a new approach aiming to detect colon cancer cells derived from the "Snake" method but using a progressive division of the dimensions of the image to achieve rapid segmentation. The aim of the present paper was to classify different cancerous cell types based on nine morphological parameters and on probabilistic neural network. Three types of cells were used to assess the efficiency of our classifications models, including BH (Benign Hyperplasia), IN (Intraepithelial Neoplasia) that is a precursor state for cancer, and Ca (Carcinoma) that corresponds to abnormal tissue proliferation (cancer). Results showed that among the nine parameters used to classify cells, only three morphologic parameters (area, Xor convex and solidity) were found to be effective in distinguishing the three types of cells. In addition, classification of unknown cells was possible using this method.

关键词：多光谱图像自动分类癌细胞形态学参数细胞类型分段概率神经网络图像分割

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Automatic segmentation of Colon Cancer Cells Based on Active Contour Method： A New Approach

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Journal of Life Sciences 2013年第2期7卷 105-109页

作者： jamal charara Alaa Hilal Ali Al Houseini Walid Hassan Mohamad Nassreddine Department of Physics and Electronics Faculty of Sciences I Lebanese University Beirut Lebanon

Automatic interpretation of the images of colon cell biopsies requires automatic segmentation of these cells in the image obtained. The active contour method for image segmentation is a well known method for automatic detection of the cell contour. However, the application of this method on colon cell images was not effective. In this paper, the authors have proposed a new technique to reduce the analysis time needed to detect cells in a given image. This technique is based on the active contour method but now using a progressive division of the dimensions of the image to achieve convergence. The model proposed succeeded in detecting cells whose boundaries are not necessarily defined by a gradient. The initial curve can be anywhere in the image, and interior contours can be automatically detected. The developed algorithm was successfully applied on textured multispectral images of three types of cells, including benign hyperplasia (BH), intraepithelial neoplasia (IN), and carcinoma (Ca) cells.

关键词：主动轮廓法癌细胞自动分割结肠图像分割方法自动检测方法多光谱图像自动判读

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