Clustering mechanism for electric tomography imaging

Yue, Shihong; Wu, Teresa; Cui, Lijun; Wang, HuaXiang X.

doi:10.1007/s11432-012-4748-7

Cited by 20 publications

(6 citation statements)

References 26 publications

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“…Clustering [1,2] is an unsupervised learning process to discover significant patterns in a given data set by partitioning a data set into groups such that the data objects assigned to the same group are as similar as possible while those in different groups are dissimilar to the greatest extent. Clustering has been widely applied in many fields [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Fuzziness parameter selection in fuzzy c-means: The perspective of cluster validation

Zhou

Yang

2014

Sci. China Inf. Sci.

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Intelligent diagnosis of the solder bumps defects using fuzzy C-means algorithm with the weighted coefficients SCIENCE CHINA Technological Sciences 58, 1689 (2015); Defect inspection of flip chip package using SAM technology and fuzzy C-means algorithm SCIENCE CHINA Technological Sciences 61, 1426 (2018); Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm SCIENCE CHINA Earth Sciences 57, 1306 (2014);. RESEARCH PAPER. SCIENCE CHINA Information Sciences

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Section: Introductionmentioning

confidence: 99%

Fuzziness parameter selection in fuzzy c-means: The perspective of cluster validation

Zhou

Yang

2014

Sci. China Inf. Sci.

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“…Essentially, any measure data is associated with the material distributions of the whole investigated field. This is called "soft-field" effect which is a much undesired case in practice [23]. The existing ET imaging algorithms are limited for this problem as the algorithms result in the obtained ET image being unstable and often unacceptable in noisy conditions.…”

Section: Eit Principle and Prior Electrical Information Effectmentioning

confidence: 99%

Classification of normal and cancerous lung tissues by electrical impendence tomography

Gao

Yue

Chen

et al. 2014

Bio-Medical Materials and Engineering

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Biological tissue impedance spectroscopy can provide rich physiological and pathological information by measuring the variation of the complex impedance of biological tissues under various frequencies of driven current. Electrical Impedance Tomography (EIT) technique can measure the impedance spectroscopy of biological tissue in medical field. Before application, a key problem must be solved on how to generally distinguish normal tissues from the cancerous in terms of measurable EIT data. In this paper, the impedance spectroscopy characteristics of human lung tissue are studied. On the basis of the measured data of 109 lung cancer patients, Cole-Cole Circle radius (CCCR) and the complex modulus are extracted. In terms of the two characteristics, 71.6% and 66.4% samples of cancerous and normal tissues can be correctly classified, respectively. Furthermore, two characteristics of the measured EIT data of each patient consist of a two-dimensional vector and all such vectors comprise a set of vectors. When classifying the vector set, the rate of correctly partitioning normal and cancerous tissues can be raised to 78.2%. The main factors to affect the classification results on normal and cancerous tissues are generally analyzed. The proposed method will play an important role in further working out an efficient and feasible diagnostic method for potential lung cancer patients, and provide theoretical basis and reference data for electrical impedance tomography technology in monitoring pulmonary function.

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“…During the past few decades, electrical tomography (ET) imaging technology has been widely used as an advanced measurement and visualization technique. Due to the fastresponse, non-radiant, low-cost advantages in obtaining 2D/3D distribution parameter information, ET has shown attractive and developing potential in many fields such as industrial measurement, clinical diagnosis, and patient bedside monitoring [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A new linear back projection algorithm to electrical tomography based on measuring data decomposition

Sun

Yue

Cui

et al. 2015

Meas. Sci. Technol.

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As an advanced measurement technique of non-radiant, non-intrusive, rapid response, and low cost, the electrical tomography (ET) technique has developed rapidly in recent decades. The ET imaging algorithm plays an important role in the ET imaging process. Linear back projection (LBP) is the most used ET algorithm due to its advantages of dynamic imaging process, real-time response, and easy realization. But the LBP algorithm is of low spatial resolution due to the natural ‘soft field’ effect and ‘ill-posed solution’ problems; thus its applicable ranges are greatly limited. In this paper, an original data decomposition method is proposed, and every ET measuring data are decomposed into two independent new data based on the positive and negative sensing areas of the measuring data. Consequently, the number of total measuring data is extended to twice as many as the number of the original data, thus effectively reducing the ‘ill-posed solution’. On the other hand, an index to measure the ‘soft field’ effect is proposed. The index shows that the decomposed data can distinguish between different contributions of various units (pixels) for any ET measuring data, and can efficiently reduce the ‘soft field’ effect of the ET imaging process. In light of the data decomposition method, a new linear back projection algorithm is proposed to improve the spatial resolution of the ET image. A series of simulations and experiments are applied to validate the proposed algorithm by the real-time performances and the progress of spatial resolutions.

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Clustering mechanism for electric tomography imaging

Cited by 20 publications

References 26 publications

Fuzziness parameter selection in fuzzy c-means: The perspective of cluster validation

Fuzziness parameter selection in fuzzy c-means: The perspective of cluster validation

Classification of normal and cancerous lung tissues by electrical impendence tomography

A new linear back projection algorithm to electrical tomography based on measuring data decomposition

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