An Optimization Clustering Algorithm Based on Texture Feature Fusion for Color Image Segmentation

We introduce a multi-feature optimization clustering algorithm for color image segmentation. The local binary pattern, the mean of the min-max difference, and the color components are combined as feature vectors to describe the magnitude change of grey value and the contrastive information of neighbor pixels. In clustering stage, it gets the initial clustering center and avoids getting into local optimization by adding mutation operator of genetic algorithm to particle swarm optimization. Compared with well-known methods, the proposed method has an overall better segmentation performance and can segment image more accurately by evaluating the ratio of misclassification.© 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Applying machine learning to predict patient-specific current CD4 cell count in order to determine the progression of human immunodeficiency virus (HIV) infection

This work shows the application of machine learning to predict current CD4 cell count of an HIV-positive patient using genome sequences, viral load and time. A regression model predicting actual CD4 cell counts and a classification model predicting if a patient’s CD4 cell count is less than 200 was built using a support vector machine and neural network. The most accurate regression and classification model took as input the viral load, time, and genome and produced a correlation of co-efficient of 0.9 and an accuracy of 95%, respectively, proving that a CD4 cell count measure may be accurately predicted using machine learning on genotype, viral load and time.Keywords: Human immunodeficiency virus (HIV), antigens, CD4, computational biology, artificial intelligence, data mining, pattern recognition.African Journal of Biotechnology Vol. 12(23), pp. 3724-373

Klasifikasi Alzheimer dan Non Alzheimer Menggunakan Fuzzy C-Mean, Gray Level Co-Occurence Matrix dan Support Vector Machine

Based on the Alzheimer's Charter, 2-3 million cases of dementia by Alzheimer's disease occur every year. People with Alzheimer's disease experience memory and cognitive disorders progressively for 3 to 9 years. Patients experience confusion in understanding the question and have a chaotic sequence of memory, which can interfere with daily activities and unchecked well, it cause death. The classification system is based on Alzheimer's and non-Alzheimer's disease Magnetic Resonance Imaging (MRI) using Support Vector Machine (SVM). The feature data segmentation using Fuzzy C-Means (FCM) and feature extraction using Gray Level Co-Occurrence Matrix (GLCM) and give accuracy result of 93.33%

Sample selection based on kernel-subclustering for the signal reconstruction of multifunctional sensors

The signal reconstruction methods based on inverse modeling for the signal reconstruction of multifunctional sensors have been widely studied in recent years. To improve the accuracy, the reconstruction methods have become more and more complicated because of the increase in the model parameters and sample points. However, there is another factor that affects the reconstruction accuracy, the position of the sample points, which has not been studied. A reasonable selection of the sample points could improve the signal reconstruction quality in at least two ways: improved accuracy with the same number of sample points or the same accuracy obtained with a smaller number of sample points. Both ways are valuable for improving the accuracy and decreasing the workload, especially for large batches of multifunctional sensors. In this paper, we propose a sample selection method based on kernel-subclustering distill groupings of the sample data and produce the representation of the data set for inverse modeling. The method calculates the distance between two data points based on the kernel-induced distance instead of the conventional distance. The kernel function is a generalization of the distance metric by mapping the data that are non-separable in the original space into homogeneous groups in the high-dimensional space. The method obtained the best results compared with the other three methods in the simulation

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

Breast cancer is the most prevalent cancer that affects women all over the world. Early detection and treatment of breast cancer could decline the mortality rate. Some issues such as technical reasons, which related to imaging quality and human error, increase misdiagnosis of breast cancer by radiologists. Computer-aided detection systems (CADs) are developed to overcome these restrictions and have been studied in many imaging modalities for breast cancer detection in recent years. The CAD systems improve radiologists’ performance in finding and discriminat- ing between the normal and abnormal tissues. These procedures are performed only as a double reader but the absolute decisions are still made by the radiologist. In this study, the recent CAD systems for breast cancer detec- tion on different modalities such as mammography, ultrasound, MRI, and biopsy histopathological images are introduced. The foundation of CAD systems generally consist of four stages: Pre-processing, Segmentation, Fea- ture extraction, and Classification. The approaches which applied to design different stages of CAD system are summarised. Advantages and disadvantages of different segmentation, feature extraction and classification tech- niques are listed. In addition, the impact of imbalanced datasets in classification outcomes and appropriate methods to solve these issues are discussed. As well as, performance evaluation metrics for various stages of breast cancer detection CAD systems are reviewed

Segmentación de Imágenes Médicas aplicando ICA y Kernelized Fuzzy c-Means

La segmentación de tejidos cerebrales es un reto importante, a la vez que tedioso, debido a que las imágenes de resonancia magnética tomadas de tejidos cerebrales presentan niveles de escala de grises muy similares, lo que hace que se puedan confundir unos tejidos con otros con cierta facilidad. En este trabajo se desarrollan diversas técnicas como K-Means, Fuzzy C-Means y clúster kernelizado con fuzzy c-means (KFCM) combinado con análisis de componentes independientes (ICA) que obtienen una segmentación de los distintos tejidos o regiones de interés en las imágenes cerebrales de resonancia magnética (MRI). El método propuesto parte imágenes cebrebrales multimodales denominadas T1-Weighted, T2- Weigthed y PD-Weighted. En primera instancia, se aplica a estas imágenes un pre-procesado en el que se elimina el cráneo de las imágenes cerebrales. A través del análisis ICA se extraen tres componentes independientes. Como las imágenes multimodales son consideradas como una combinación lineal de señales, aplicar ICA hace que se produzca una mejora en el contraste de las imágenes cerebrales. El resultado de extraer las tres componentes independientes será la entrada de los distintos algoritmos de clasificación para extraer los tejidos cerebrales. Haciendo un análisis de los resultados del experimento, el método propuesto es capaz de extraer con precisión formas complicadas de los tejidos cerebrales.Universidad de Sevilla. Máster en Ingeniería de Telecomunicació