PCA based clustering for brain tumor segmentation of T1w MRI images.

BACKGROUND AND OBJECTIVE: Medical images are huge collections of information that are difficult to store and process consuming extensive computing time. Therefore, the reduction techniques are commonly used as a data pre-processing step to make the image data less complex so that a high-dimensional data can be identified by an appropriate low-dimensional representation. PCA is one of the most popular multivariate methods for data reduction. This paper is focused on T1-weighted MRI images clustering for brain tumor segmentation with dimension reduction by different common Principle Component Analysis (PCA) algorithms. Our primary aim is to present a comparison between different variations of PCA algorithms on MRIs for two cluster methods. METHODS: Five most common PCA algorithms; namely the conventional PCA, Probabilistic Principal Component Analysis (PPCA), Expectation Maximization Based Principal Component Analysis (EM-PCA), Generalize Hebbian Algorithm (GHA), and Adaptive Principal Component Extraction (APEX) were applied to reduce dimensionality in advance of two clustering algorithms, K-Means and Fuzzy C-Means. In the study, the T1-weighted MRI images of the human brain with brain tumor were used for clustering. In addition to the original size of 512 lines and 512 pixels per line, three more different sizes, 256 × 256, 128 × 128 and 64 × 64, were included in the study to examine their effect on the methods. RESULTS: The obtained results were compared in terms of both the reconstruction errors and the Euclidean distance errors among the clustered images containing the same number of principle components. CONCLUSION: According to the findings, the PPCA obtained the best results among all others. Furthermore, the EM-PCA and the PPCA assisted K-Means algorithm to accomplish the best clustering performance in the majority as well as achieving significant results with both clustering algorithms for all size of T1w MRI images.

Affected States soft independent modeling by class analogy from the relation between independent variables, number of independent variables and sample size.

OBJECTIVE: The aim of study is to introduce method of Soft Independent Modeling of Class Analogy (SIMCA), and to express whether the method is affected from the number of independent variables, the relationship between variables and sample size. STUDY DESIGN: Simulation study. MATERIAL AND METHODS: SIMCA model is performed in two stages. In order to determine whether the method is influenced by the number of independent variables, the relationship between variables and sample size, simulations were done. Conditions in which sample sizes in both groups are equal, and where there are 30, 100 and 1000 samples; where the number of variables is 2, 3, 5, 10, 50 and 100; moreover where the relationship between variables are quite high, in medium level and quite low were mentioned. RESULTS: Average classification accuracy of simulation results which were carried out 1000 times for each possible condition of trial plan were given as tables. CONCLUSION: It is seen that diagnostic accuracy results increase as the number of independent variables increase. SIMCA method is a method in which the relationship between variables are quite high, the number of independent variables are many in number and where there are outlier values in the data that can be used in conditions having outlier values.

Diagnosis of several diseases by using combined kernels with Support Vector Machine.

Machine learning techniques have gained increasing demand in biomedical research due to capability of extracting complex relationships and correlations among members of the large data sets. Thus, over the past few decades, scientists have been concerned about computer information technology to provide computational learning methods for solving the complex medical problems. Support Vector Machine is an efficient classifier that is widely applied to biomedical and other disciplines. In recent years, new opportunities have been developed on improving Support Vector Machines' classification efficiency by combining with any other statistical and computational methods. This study proposes a new method of Support Vector Machines for influential classification using combined kernel functions. The classification performance of the developed method, which is a type of non-linear classifier, was compared to the standart Support Vector Machine method by applying on seven different datasets of medical diseases. The results show that the new method provides a significant improvement in terms of the probability excess.