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Optimization of Medical Images Using Gabor Filter International Journal of Pure and Applied Mathematics Volume 118 No. 24 2018 ISSN: 1314-3395 (on-line version) url: http://www.acadpubl.eu/hub/ Special Issue http://www.acadpubl.eu/hub/ Optimization of Medical Images using Gabor Filter Mr.Veda Narayanan and Pauline Sheeba Department of ETE Sathyabama Institute of Science and Technology (Deemed to be University) Chennai-19, Tamilnadu, India [email protected] May 22, 2018 Abstract a new approach in medical science is to automate the medical image segmentation based on multi scale analysis and adaptive thresholding. The accurate identification of the any dangerous changes in the body plays an important role in medical diagnosis of many diseases. In contrast to the existing methods for computer aided diagnosis which are either window-based or tracking based, we propose a novel scheme which combines optimization and applications of ga- bor filter to identify abnormalities under various conditions such as identifying tumors , and various other conditions that may prove fatal if ignored due to human errors . Our method includes a multiscale analytical scheme based on Gabor filters and scale multiplication, and adaptive thresh- olding. The experimental results demonstrate the feasibility and effectiveness of the proposed algorithms which are good for detecting the lumps and tumors in the image captured accurately, with robustness to denoise and enhance the re- sponses at low contrast Key Words:medical image, multiscale analysis, adap- tive thresholding . 1 International Journal of Pure and Applied Mathematics Special Issue 1 Introduction Medical image processing is the most challenging and highly wanted field. Brain tumor detection in magnetic resonance imaging(MRI) has become an emerging field of medical image processing. Segmen- tation of images is one of the most difficult tasks holds an impotant position in image processing which determine the quality of the of the final result .Image segmentation is the process of dividing an :image into different regions .the aim of this paper is to provide a review on automated tool for brain tumor segmentation using MRI scanned image datasets .detection and extraction of tumor from MRI scan images of the brain is done by Matlab software. Under the existing medical conditions, in addition to surgery and radiation therapeutic methods, there are no more effective treatments, and the patients condition can be alleviated and con- trolled, but extremely difficult to cure, which will cause great bur- dens in both mentality and economy for the patients. In this sense, the treatment of cancer is a major social problem in both economic and financial aspects. A good solution to this problem will have important social and practical significance. Commonly used medical imaging methods are Computed To- mography (CT), Positron Emission Tomography (PET), CT / PET, Magnetic Resonance Imaging (MRI), and so on 2 EXISTING TECHNIQUES The existing techniques are discussed here. Few existing techniques are discussed here. The techniques that are discussed are Otsus optimal thresholding, snake active contour, SIFT (Scale Invariant Feature Transform), PCA (Principal Component Analysis), SVD (Singular Value Decomposition) and watershed. A. Region growing : In this technique the images are partitioned by organizing the nearest pixel of similar kind. It starts with a pixel (initial seed) that having similar properties. Accordingly the neighbouring pix- els based on homogeneity criteria are appended progressively to the seed. In splitting process ,region get divided into sub regions that do not satisfy a given homogeneity criteria. Spliting and merg- ing can be used together and its performance mostly depends on 2 International Journal of Pure and Applied Mathematics Special Issue the selected homogeneity criterion. Without tuning homogeneity parameters, the seeded region growing technique is controlled by a number of initial seeds. If the number of regions was approximately known & used it to estimate the corresponding parameters of edge detection. B. Clustering The method of clustering organizes the objects into groups based on some feature, attribute and characteristic. Hence a cluster con- sists of groups of similar objects. There are two types of clustering, supervised and unsupervised. In supervised type clustering, cluster criteria are specified by the user. In unsupervised type, the cluster criteria are decided by the clustering system itself. C. Soft-Computing A self-organizing map (SOM) or self-organizing feature map is a type of artificial neural network for unsupervised learning. SOMs organize in training and mapping mode. Training process builds map using vector quantization process and mapping automatically classifies a new input vector. SOM map consists of neurons or nodes. Self organizing maps each of which are neurons associated with a weight vector map data input vectors and position in the map space. The self-organizing maps a higher dimensional input space to a lower dimensional map space. Energy, entropy, contrast, mean, median, variance, correlation, maximum and minimum intensity values used to provide clear description of tumor. D. System analysis : Existing technique : Nowadays, brain tumor has become one of the main cause for increasing• mortality among children and adults. Based on some researches, it has been found that the number of people suffering and dying from brain tumors has been increased to 300 per year during past few decades. Existing technique has been practiced to determine tumor pa- tients• response to treatment since long time ago. The radiologist has made series of cross-sectional diameter measurements for indi- cator lesions purposes by using axial, incremental CT image data. Later, these measurements will be compared with the previous mea- surement scans. bullet Nevertheless, the measurement of lesion diameter does not represent the exact assessment of tumor size due to some fac- 3 International Journal of Pure and Applied Mathematics Special Issue tors, such as: Irregular lesions The lesions that grow other than sphere shape may not adequately represented by diameters changes; Different measurement between inter-observer and intra-observer Referred to the image selection used for the measurement and the location of lesion boundary; Different levels of scanning results collected from various di- agnoses• The lesions may not be captured exactly at the same spot from one diagnosis to another. Hence, it affects the lesions image in which causes comparisons between examinations becoming more difficult. 3 PROPOSED TECHNIQUE I. Pre-processing generally means removing noise and improving or altering image quality to suit a purpose. For this study, only commonly used enhancement and noise reduction techniques were implemented. II. The image enhancement that the study is interested in should yield the result of more prominent edges and a sharpened image, noise will be reduced thus reducing the blurring or salt paper effect from the image that might produce errors. Fig: flowchart of proposed system INPUT MRI IMAGE: Image segmentation is one of the fun- damental approaches of digital image processing. During past few years, brain tumor segmentation in magnetic resonance imaging (MRI) has become a popular research area in the field of medical imaging system. MRI is used in radiology for analysing internal structures and makes easy to extract the required region. 4 International Journal of Pure and Applied Mathematics Special Issue GRAY SCALE IMAGE Gray scale imaging is sometimes called ”black and white”,but technically this is misnomer in true black and white ,also known as halftone, and the only possible shades are pure black and pure white gray shading in a halftone image is obtained by considering the images as a grid of black dots on white background (or vice versa ) and the sizes of the individuals dots determine the apparent lightness of the gray in their vicinity. The lightness of the gray is directly proportional to the number representing the brightness levels of the colors. Grayscale imaging can be collectively called as the as the ranges of shades of gray. Grayscale can be collectively called as the ranges of the shades of gray.MRI images are used in the GENETIC ALGORITHM A genetic algorithm is a search heuristic that is inspired by Charles Darwins theory of natural evolution. This algorithm re- flects the process of natural selection where the fittest individuals are selected for reproduction in order to produce offspring of the next generation. Five phases are considered in a genetic algorithm. 1. Initial population 2. Fitness function 3. Selection 4. Crossover 5. Mutation The process begins with a set of individuals which is called a Population. Each individual is a solution to the problem you want to solve. An individual is characterized by a set of parameters (variables) known as Genes. Genes are joined into a string to form a Chromosome (solution). In a genetic algorithm, the set of genes of an individual is represented using a string, in terms of an alphabet. Usually, binary values are used (string of 1s and 0s). We say that we encode the genes in a chromosome. 5 International Journal of Pure and Applied Mathematics Special Issue Figure 5: population , chromosome and gene Selection The idea of selection phase is to select the fittest individuals and let them pass their genes to the next generation. Two pairs of individuals (parents) are selected based on their fitness scores. Individuals with high fitness have more chance to be selected for reproduction. Crossover Crossover is the most significant phase in a genetic algorithm. For each pair of parents to be mated, a crossover point is chosen at random from within the genes. For example, consider the crossover point to be 3 as shown below. FIGURE 4.2 Crossover point Offspring are created by exchanging the genes of parents among themselves until the crossover point is reached. MUTATION In certain new offspring formed, some of their genes can be sub- jected to a mutation with a low random probability. This implies that some of the bits in the bit string can be flipped.
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