Brain image clustering by wavelet energy and CBSSO optimization algorithm

Previously, the diagnosis of brain abnormality was significantly important in the saving of social and hospital resources. Wavelet energy is known as an effective feature detection which has great efficiency in different utilities. This paper suggests a new method based on wavelet energy to automatically classify magnetic resonance imaging (MRI) brain images into two groups (normal and abnormal), utilizing support vector machine (SVM) classification based on chaotic binary shark smell optimization (CBSSO) to optimize the SVM weights. The results of the suggested CBSSO-based KSVM are compared favorably to several other methods in terms of better sensitivity and authenticity. The proposed CAD system can additionally be utilized to categorize the images with various pathological conditions, types, and illness modes

A novel statistical cerebrovascular segmentation algorithm with particle swarm optimization

AbstractWe present an automatic statistical intensity-based approach to extract the 3D cerebrovascular structure from time-of flight (TOF) magnetic resonance angiography (MRA) data. We use the finite mixture model (FMM) to fit the intensity histogram of the brain image sequence, where the cerebral vascular structure is modeled by a Gaussian distribution function and the other low intensity tissues are modeled by Gaussian and Rayleigh distribution functions. To estimate the parameters of the FMM, we propose an improved particle swarm optimization (PSO) algorithm, which has a disturbing term in speeding updating the formula of PSO to ensure its convergence. We also use the ring shape topology of the particles neighborhood to improve the performance of the algorithm. Computational results on 34 test data show that the proposed method provides accurate segmentation, especially for those blood vessels of small sizes

Developing improved algorithms for detection and analysis of skin cancer

University of Technology Sydney. Faculty of Engineering and Information Technology.Malignant melanoma is one of the deadliest forms of skin cancer and number of cases showed rapid increase in Europe, America, and Australia over the last few decades. Australia has one of the highest rates of skin cancer in the world, at nearly four times the rates in Canada, the US and the UK. Cancer treatment costs constitute more 7.2% of health system costs. However, a recovery rate of around 95% can be achieved if melanoma is detected at an early stage. Early diagnosis is obviously dependent upon accurate assessment by a medical practitioner. The variations of diagnosis are sufficiency large and there is a lack of detail of the test methods. This thesis investigates the methods for automated analysis of skin images to develop improved algorithms and to extend the functionality of the existing methods used in various stages of the automated diagnostic system. This in the long run can provide an alternative basis for researchers to experiment new and existing methodologies for skin cancer detection and diagnosis to help the medical practitioners. The objective is to have a detailed investigation for the requirements of automated skin cancer diagnostic systems, improve and develop relevant segmentation, feature selection and classification methods to deal with complex structures present in both dermoscopic/digital images and histopathological images. During the course of this thesis, several algorithms were developed. These algorithms were used in skin cancer diagnosis studies and some of them can also be applied in wider machine learning areas. The most important contributions of this thesis can be summarized as below: - Developing new segmentation algorithms designed specifically for skin cancer images including digital images of lesions and histopathalogical images with attention to their respective properties. The proposed algorithm uses a two-stage approach. Initially coarse segmentation of lesion area is done based on histogram analysis based orientation sensitive fuzzy C Mean clustering algorithm. The result of stage 1 is used for the initialization of a level set based algorithm developed for detecting finer differentiating details. The proposed algorithms achieved true detection rate of around 93% for external skin lesion images and around 88% for histopathological images. - Developing adaptive differential evolution based feature selection and parameter optimization algorithm. The proposed method is aimed to come up with an efficient approach to provide good accuracy for the skin cancer detection, while taking care of number of features and parameter tuning of feature selection and classification algorithm, as they all play important role in the overall analysis phase. The proposed method was also tested on 10 standard datasets for different kind of cancers and results shows improved performance for all the datasets compared to various state-of the art methods. - Proposing a parallelized knowledge based learning model which can make better use of the differentiating features along with increasing the generalization capability of the classification phase using advised support vector machine. Two classification algorithms were also developed for skin cancer data analysis, which can make use of both labelled and unlabelled data for training. First one is based on semi advised support vector machine. While the second one based on Deep Learning approach. The method of integrating the results of these two methods is also proposed. The experimental analysis showed very promising results for the appropriate diagnosis of melanoma. The classification accuracy achieved with the help of proposed algorithms was around 95% for external skin lesion classification and around 92 % for histopathalogical image analysis. Skin cancer dataset used in this thesis is obtained mainly from Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital. While for comparative analysis and benchmarking of the few algorithms some standard online cancer datasets were also used. Obtained result shows a good performance in segmentation and classification and can form the basis of more advanced computer aided diagnostic systems. While in future, the developed algorithms can also be extended for other kind of image analysis applications

A Swarm-based Approach To Medical Image Analysis

Image segmentation is an indispensable part of the visualization of human tissues, particularly during analysis of Magnetic Resonance (MR) images. Unfortunately images always contain a significant amount of noise due to operator performance, equipment, and the environment can lead to serious inaccuracies with segmentation. A segmentation technique based on an extension to the traditional C-means (FCM) clustering algorithm is proposed in this paper. A neighborhood attraction, which is dependent on the relative location and features of neighboring pixels considered.. The degree of attraction is optimized by a Particle Swarm Optimization model. Paper demonstrates the superiority of the proposed technique to FCM-based method. This segmentation method is component of an MR image-based classification system for tumors, currently being developed

Optimal k-means clustering using artificial bee colony algorithm with variable food sources length

Clustering is a robust machine learning task that involves dividing data points into a set of groups with similar traits. One of the widely used methods in this regard is the k-means clustering algorithm due to its simplicity and effectiveness. However, this algorithm suffers from the problem of predicting the number and coordinates of the initial clustering centers. In this paper, a method based on the first artificial bee colony algorithm with variable-length individuals is proposed to overcome the limitations of the k-means algorithm. Therefore, the proposed technique will automatically predict the clusters number (the value of k) and determine the most suitable coordinates for the initial centers of clustering instead of manually presetting them. The results were encouraging compared with the traditional k-means algorithm on three real-life clustering datasets. The proposed algorithm outperforms the traditional k-means algorithm for all tested real-life datasets

MMFO: modified moth flame optimization algorithm for region based RGB color image segmentation

Region-based color image segmentation is elementary steps in image processing and computer vision. Color image segmentation is a region growing approach in which RGB color image is divided into the different cluster based on their pixel properties. The region-based color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, in which three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper, L*a*b color space conversion has been used to reduce the one dimension and geometrically it converts in the array hence the further one dimension has been reduced. This paper introduced an improved algorithm MMFO (Modified Moth Flame Optimization) Algorithm for RGB color image Segmentation which is based on bio-inspired techniques for color image segmentation. The simulation results of MMFO for region based color image segmentation are performed better as compared to PSO and GA, in terms of computation times for all the images. The experiment results of this method gives clear segments based on the different color and the different no. of clusters is used during the segmentation process

Radial Basis Function Neural Networks : A Review

Radial Basis Function neural networks (RBFNNs) represent an attractive alternative to other neural network models. One reason is that they form a unifying link between function approximation, regularization, noisy interpolation, classification and density estimation. It is also the case that training RBF neural networks is faster than training multi-layer perceptron networks. RBFNN learning is usually split into an unsupervised part, where center and widths of the Gaussian basis functions are set, and a linear supervised part for weight computation. This paper reviews various learning methods for determining centers, widths, and synaptic weights of RBFNN. In addition, we will point to some applications of RBFNN in various fields. In the end, we name software that can be used for implementing RBFNNs

Automatic Spinal Cord Segmentation From Medical MR Images using Hybrid Algorithms

Medical image processing is the top most research area. There are huge amount of researches found in the medical image analyze, classification and segmentation process. Spinal cord segmentation of MRI images is the challenging process. In the proposed research work, automatic Spinal Cord (SC) segmentation from medical MRI image is performed with various techniques. The proposed work improves the segmentation with less iteration and improved accuracy by adopting improved Weighted Expectation Maximization (WEM) and Strong Fitness Firefly (SFF) algorithms. The proposed work effectively segments the spinal cord by applying effective pre-processing, image enhancement process and clustering with less iterations. Using the combination of different techniques, the proposed system effectively identifies the spinal cord from the MRI image, the experiments performed using Matlab tool. The accuracy is calculated and shown for the proposed system. The result shows, the mixed approach of WEM and SFF increases the segmentation accuracy than using the WEM alone