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Предложен метод расчета границ качественных классов для количественных характеристик систем любой природы. Метод позволяет установить: связи, не поддающиеся обнаружению при помощи корреляционного и регрессионного анализа; границы для качественных классов индикатора состояния систем и факторов, влияющих на это состояние; вклад факторов в степень «неприемлемости» значений индикатора; достаточность программы наблюдений за
факторами для описания причин «неприемлемости» значений индикатора.

A calculation method for boundaries of quality classes for quantitative systems characteristics of any nature is suggested. The method allows to determine interactions which are not detectable using correlation and regression analysis; quality classes’ boundaries of systems’ condition indicator and boundaries of the factors influencing this condition; contribution of the factors to a degree of «inadmissibility» of indicator values; sufficiency of the program observing the factors to describe the causes of «inadmissibility» of indicator values.

Метод расчета границ качественных классов для количественных характеристик систем любой природы адаптирован к поиску границ при наличии трех качественных классов. Адаптация метода позволила в дополнение к другим результатам определить границы между качественными классами при одновременной «неприемлемости» высоких и низких значений индикаторной характеристики состояния системы и одновременной «недопустимости» высоких и низких значений факторов, влияющих на систему.

The method of calculation of the boundaries of quality classes for quantitative characteristics of systems with any properties is adapted to search for boundaries of three quality classes. In addition to other results, adaptation of the method allowed to determine boundaries between quality classes at simultaneous «unacceptability » of high and low values of indicator characteristic of the system condition and simultaneous «inadmissibility » of high and low values of factors affecting the system.

Accurate segmentation of liver plays important in contouring during diagnosis and the planning of treatment. Imaging technology analysis and processing are wide usage in medical diagnostics, and therapeutic applications. Liver segmentation referring to the process of automatic or semi-automatic detection of liver image boundaries. A major difficulty in segmentation of liver image is the high variability as; the human anatomy itself shows major variation modes. In this paper, a proposed approach for computed tomography (CT) liver segmentation is presented by combining exponential entropy and fuzzy c-partition. Entropy concept has been utilized in various applications in imaging computing domain. Threshold techniques based on entropy have attracted a considerable attention over the last years in image analysis and processing literatures and it is among the most powerful techniques in image segmentation. In the proposed approach, the computed tomography (CT) of liver is transformed into fuzzy domain and fuzzy entropies are defined for liver image object and background. In threshold selection procedure, the proposed approach considers not only the information of liver image background and object, but also interactions between them as the selection of threshold is done by find a proper parameter combination of membership function such that the total fuzzy exponential entropy is maximized. Differential Evolution (DE) algorithm is utilizing to optimize the exponential entropy measure to obtain image thresholds. Experimental results in different CT livers scan are done and the results demonstrate the efficient of the proposed approach. Based on the visual clarity of segmented images with varied threshold values using the proposed approach, it was observed that liver segmented image visual quality is better with the results higher level of threshold.

Accurate segmentation of liver plays important in contouring during diagnosis and the planning of treatment. Imaging technology analysis and processing are wide usage in medical diagnostics, and therapeutic applications. Liver segmentation referring to the process of automatic or semi-automatic detection of liver image boundaries. A major difficulty in segmentation of liver image is the high variability as; the human anatomy itself shows major variation modes. In this paper, a proposed approach for computed tomography (CT) liver segmentation is presented by combining exponential entropy and fuzzy c-partition. Entropy concept has been utilized in various applications in imaging computing domain. Threshold techniques based on entropy have attracted a considerable attention over the last years in image analysis and processing literatures and it is among the most powerful techniques in image segmentation. In the proposed approach, the computed tomography (CT) of liver is transformed into fuzzy domain and fuzzy entropies are defined for liver image object and background. In threshold selection procedure, the proposed approach considers not only the information of liver image background and object, but also interactions between them as the selection of threshold is done by find a proper parameter combination of membership function such that the total fuzzy exponential entropy is maximized. Differential Evolution (DE) algorithm is utilizing to optimize the exponential entropy measure to obtain image thresholds. Experimental results in different CT livers scan are done and the results demonstrate the efficient of the proposed approach. Based on the visual clarity of segmented images with varied threshold values using the proposed approach, it was observed that liver segmented image visual quality is better with the results higher level of threshold.