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3 July 2001 Tissue color image segmentation and analysis for automated diagnostics of adenocarcinoma of the lung
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Designing and developing computer-assisted image processing techniques to help doctors improve their diagnosis has received considerable interests over the past years. In this paper, we present a method for segmentation and analysis of lung tissue that can assist for the diagnosis of the adenocancinoma of the lung. The segmentation problem is formulated as minimization of an energy function synonymous to that of Hopfield Neural Network (HNN) for optimization. We modify the HNN to reach a status close to the global minimum in a pre-specified time of convergence. The energy function constructed with two terms, the cost-term as a sum of squared errors, and the second term a temporary noise added to the network as an excitation to escape certain local minima to be close to the global minimum. Each lung color image is represented in RGB and HSV color spaces and the segmentation results are comparatively presented. Furthermore, the nuclei are automatically extracted based on green color histogram threshold. Then, the nucleus radius is computed using the maximum drawable circle inside the object. Finally, all nuclei with abnormal size are extracted, and their morphology in the raw tissue image drew automatically. These results can provide the pathologists with more accurate quantitative information that can help greatly in the final decision.
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Mohamed Sammouda, Noboru Niki, Toshiro Niki, and Naohito Yamaguchi "Tissue color image segmentation and analysis for automated diagnostics of adenocarcinoma of the lung", Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001);

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