Present day GPUs are equipped for performing vector operations and gliding point number-crunching, with the most recent cards fit for controlling twofold exactness drifting point numbers. Systems, for example, CUDA and Open GL empower projects to be composed for GPUs, and the way of GPUs make them most suited to very parallelizable operations, for example, in logical figuring, where a progression of specific GPU process cards can be a practical substitution for a little register group as in NVIDIA Tesla Personal
Shi Et al  used a local projection profile at each pixel of the image, and transform the original image into an adaptive local connectivity map (ALCM). For this process, first the gray scale image is reversed so the foreground pixels (text) have idensity values of up to 255. Then the image is downsample to ¼ of each size, ½ in each direction. Next a sliding window of size 2c scans the image from left to right, and right to left in order to compute the cumulative idensity of every neighborhood. This technique is identical to computing the projection profile of every sliding window, (i.e. counting the foreground pixels), but instead of outpiting a projection profile histogram, the entire sliding window sum is saved on the ALCM image. Finaly
In 2016, S.S. Pal, et al.  in the paper titled “Multi-level Thresholding Segmentation Approach Based on Spider Monkey Optimization Algorithm” introduced SMO for histogram based bi-level and multi-level segmentation of grey scale images. SMO has likewise been utilized to maximize Kapur’s and Otsu’s objective function. Results delineated that the new segmentation method is able to improve results in terms of optimum threshold values and CPU time when compared to other nature inspired algorithms.
The segmentation and classification of Neonatal brain structure from magnetic resonance imaging (MRI) is indispensable for the study of growth patterns and morphological changes in neurodevelopmental disorders. The Segmentation and Classification of Neonatal MRI is a challenging task mainly due to the low intensity contrast and the growth process of the brain tissues. A new method for Neonatal Brain image segmentation and classification is developed in this paper. Here the Segmentation method is based on the Minimum spanning tree Segmentation (MST) with Manhattan distance and brier score coupled shrunken centroid classifier for classification of neonatal brain tissues. MST simplifies Neonatal Brain image analysis tasks such as counting objects
After reading the digital image into internal memory, the program uses cvThreshold function to transfer the colorful image into a grayscale one. Then according to the optimal variance, transfer the grayscale image to a binary image. After that, the program uses cvDilate and cvErode functions to do dilation and erosion operation. Thus, in order to improve the accuracy rate, cvSmooth function is used to smooth the edges of the image. Then program uses canny operator to detect and outline edges. At the end, circle hough transform is used to detect the circles in the images, which are the sign of human head. So the program
b represents the bias field that indicates the intensity inhomogeneity. The bias field is slowly varying, which implies that b can be will approximated by a constant in a neighbourhood of each point in the image domain. Energy function has to be minimized within a boundary where the level set evolves. For this a Neumann Boundary condition is defined and is applied to the level set function to get object boundary. Within this specific boundary, the Level Set Evolution process will take place. The level set function is obtained by taking the signed function of randomized image and has values 0, 1, and -1. Local intensity clustering property indicates that the image can be segmented into three regions based on the values of level set function. Standard K-means Criterion is used to classify the local intensity which can be defined as
Segmentation of brain tumor is done to separate tumor from non tumor tissue. It is one of the most crucial step in medical image processing. We have used modified Radial basis function to segment the tumor. It proves to be best option then existing algorithms.
Padole et al. [PAD12] proposed an efficient technique for brain tumor detection. One of the maximum essential steps in tumor detection is segmentation. Combination of general algorithms, suggest shift and normalized cut is executed to hit upon the brain tumor surface area in MRI. Pre-processing step is first done by way of the use of the imply shift set of rules as a way to shape segmented regions. Inside the next step location nodes clustering are processed by way of n-cut approach. Inside the final step, the mind tumor is detected through element
Besides the tumour heterogeneity, the boundaries of the tumour may be composite and visually unclear to detect at the earlier stages. Some tumour may collapse the adjacent structures in the brain. Furthermore, artefacts and noise in the brain tumour images complicates the obscurity in tumour detection. Hence developing an efficient and automatic image segmentation approach is necessary to provide a better tumour detection performance especially in MRI brain
T.F.Chen  segmentation is the process of portioning the images, where we need to find the particular portion, there are several methods segmentation such as active contour, etc. segmentation can be done both manually and automatically. Here the new technique of segmentation known as level sets segmentation are described, the level set segmentation reduces the problems of finding the curves which is enclose with respect to the region of interest. The implementation of this involves the normal speed and vector field, entropy condition etc. The implementation results produced was two different curves, which can be splitted.
Granular parakeratosis is a skin disease that is identified by brownish-red keratotic papules that can coalesce into plaques. This is a rare disorder of keratinization with a distinctive histology wherein parakeratosis with retention of keratohyaline granules is identified in the epidermis. An effective image processing system support vector machine can be potentially used to segment the lesions of granular parakeratosis. Image Segmentation is one of the important issues occurred in times of before computer visualization. The fundamental objective of image segmentation is to segment a picture into its constituent areas. This segmentation can be applied to skin disease like granular parakeratosis and later Support Virtual Machine
One of the ultrasound image analysis is segmentation process to obtain the fetal biometric measurement. According to , an automatic segmentation technique on
Tumor segmentation from magnetic resonance imaging (MRI) data is an important but time consuming manual task performed by medical experts. Automating this process is a challenging task because of the high diversity in the appearance of tumor tissues among different patients and in many cases similarity with the normal tissues. MRI is an advanced medical imaging technique providing rich information about the human soft-tissue anatomy.. In this paper, we have proposed an automatic tumor detection framework to detect the multiple tumors in brain tumor databases. This system has four main phases, namely image preprocessing for image enhancement, Fuzzy C-Means segmentation algorithm is used for tumor segmentation, Apply thresholding on segmented
Abstract—Image captured by two-dimensional camera contains no depth information. However depth information is needed in many applications, for example in robotic vision, satellite imaging and target tracking. To extract depth information from images Stereo matching is used. The main aim of our project is to use stereo matching algorithms to plot the disparity map of segmented images which gives the depth information details. Particle Swarm Optimization and K-means algorithms are used for image segmentation. Our main objective is to implement stereo matching algorithms on the segmented images, compare the results of K-means and PSO on the basis of objective parameters such as PSNR, execution time, density of disparity map and compression ratio and perform subjective analysis of reconstructed 3-D images. The compared results show that the Particle Swarm Optimization algorithm gives better 3-D reconstructed image.
This paper is based on CUDA, a parallel computing platform model, which utilizes the resources of the Graphical Processing Unit (GPU), increasing the computing performance of our system, hence creating a robust parallel computing unit. In this paper, we will be introducing a brief history on CUDA, it’s execution flow and it’s architecture to handle processor intensive tasks. We will also be highlighting some of it’s real life applications and the difference in performance as compared of the only CPU based architectures. Also, since most of the CUDA applications are written in C/C++, we will also be exploring how CUDA provides the programmable interface in such languages as well. Finally, we will be including the current research activities