Leukaemia’s Cells Pattern Tracking Via Multi-phases Edge Detection Techniques

Edge detection involves identifying and tracing the sudden sharp discontinuities to extract meaningful information from an image. The purpose of this paper is to improve detecting the leukaemia edges in the blood cell image. Toward this end, two distinctive procedures are developed which are Ant Colony Optimization Algorithm and the gradient edge detectors (Sobel, Prewitt and Robert). The latter involves image filtering, binarization, kernel convolution filtering and image transformation. Meanwhile, ACO involves filtering, enhancement, detection and localisation of the edges. Finally, the performance of the edge detection methods ACO, Sobel, Prewitt and Robert is compared to determine the best edge detection method. The results revealed that the Prewitt edge detection method produced an optimal performance for detecting edges of leukaemia cells with a value of 107%. Meanwhile, the ACO, Sobel and Robert yielded performance results of 76%, 102% and 93% respectively. Overall findings indicated that the gradient edge detection methods are superior to the Ant Colony Optimization method

A Review of Algorithms for Retinal Vessel Segmentation

oai:ojs.pkp.sfu.ca:article/41This paper presents a review of algorithms for extracting blood vessels network from retinal images. Since retina is a complex and delicate ocular structure, a huge effort in computer vision is devoted to study blood vessels network for helping the diagnosis of pathologies like diabetic retinopathy, hypertension retinopathy, retinopathy of prematurity or glaucoma.  To carry out this process many works for normal and abnormal images have been proposed recently. These methods include combinations of algorithms like Gaussian and Gabor filters, histogram equalization, clustering, binarization, motion contrast, matched filters, combined corner/edge detectors, multi-scale line operators, neural networks, ants, genetic algorithms, morphological operators. To apply these algorithms pre-processing tasks are needed. Most of these algorithms have been tested on publicly retinal databases. We have include a table summarizing algorithms and results of their assessment

An Experiment with Ant Colony Optimization for Edge Detection in Images (Algebras, logics, languages and related areas)

Ant colony optimization (ACO) is a simulation of the natural behavior of ant species; where ants find the shortest path between its nest and food source. Image edge detection is a basic image processing task, where the outlines of the objects in an image are identified, and then extracted. We present the results of an experiment conducted with the ACO algorithm applied to the edge detection problem

A DYNAMIC APPROACH FOR BRAIN TUMOR DETECTION USING EDGE DETECTION TECHNIQUE

Image process is most typically victimized framework in medical imaging. A foundation uncovering is alive for its trustiness and warrant that delivers a stronger understanding of seeable representation within the applications of laptop modality, same prosy catching, confronting perception, and recording force succeed. Machine Learning and Deep Learning algorithms are principally victimized for analyzing the medical pictures which may make, stage and categorize the tumor into sub classes, coherent with that the identification would be through by the professionals. during this production, we've mentioned the technique that's used for tumor pre-processing, and sorting

Chapter Ant Algorithms for Adaptive Edge Detection

Diseases & disorder

Ant Algorithms for Adaptive Edge Detection

Diseases & disorder

On edge detection of images using ant colony optimization and fisher ratio

Edge detection is one of the important parts of image processing. It is essentially involved in the re-processing stage of image analysis and computer vision. It generally detects the contour of an image and thus provides important details about an image. So, it reduces the content to process for the high-level processing tasks like object recognition and image segmentation. The most important step in the edge detection, on which the success of generation of true edge map depends, lies on the determination of threshold. In this work, purpose of edge detection, inspired from Ant Colonies, is fulfilled by Ant Colony Optimisation (ACO). For the determination of threshold calculation, a novel technique of Fisher ratio (F-ratio) is used. The success of the work done is tested visually with the help of test images and empirically tested on the basis of several statistical parameter of comparison. De-noising is the process of extracting the important features present in an image, keeping the unnecessary or unimportant information present in the form of noise out as much as possible. There are many Denoising methods that have been developed in these field, but the most trustworthy and used among them is the wavelet thresholding denoising method with hard thresholding. The proposed novel method presented in this thesis is tested on the denoised images. The Edge detected images obtained on the denoised images are showing better results than the other conventional edge detectors

Implementasi Algoritma Ant-Inspired Untuk Mendeteksi Tepi Gambar

Deteksi tepi merupakan salah satu proses pengolahan yang sering dibutuhkan pada analisis citra. Untuk mendapatkan citra yang baik dan citra yang sesuai dengan keinginan, biasanya citra diproses terlebih dahulu dengan perbaikan kualitas citra. Tepi citra selalu memiliki informasi penting mengenai objek gambar. Pada awalnya terdapat beberapa metode deteksi tepi tradisional seperti canny yang mengekstrak tepi dengan menggunakan template tertentu dengan kombinasi fungsi smoothing (penghalusan) gambar. Deteksi tepi metode tradisional ini kadangkala belum maksimal saat digunakan untuk mendeteksi tepi citra, seperti garis-garis tepi yang dihasilkan masih terlihat tidak menyambung satu sama lain. Dalam tugas akhir ini, algoritma ant-inspireddigunakan untuk melakukan deteksi terhadap tepi citra yang sudah ditransformasi terlebih dahulu ke dalam format citra keabuan. Transformasi ke citra keabuan diperlukan untuk mempermudah proses deteksi tepi citra. Deteksi tepi yang digunakan didasarkan pada pergerakan semut yang berjalan mencari makanan dengan meninggalkan feromon sebagai jejaknya. Ada beberapa tahapan dalam tugas akhir ini. Tahap pertama adalah melakukan konversi citra ke format keabuan. Tahap selanjutnya adalah proses perubahan citra menjadi citra gradien. Tahap yang terakhir adalah melakukan proses deteksi tepi citra menggunakan algoritma ant-inspired. Dari percobaan yang dilakukan, dapat dibuktikan bahwa algoritma ant-inspired dapat menghasilkan deteksi tepi citra yang lebih baik dan akurat dengan nilai akurasi rata-rata sebesar 88,09%. Berdasarkan eksperimen yang dilakukan semakin besar jumlah iterasi, maka semakin meningkat pula kemampuan algoritma ini dalam mendeteksi tepi citra. Perubahan nilai threshold juga memperlihatkan perbedaan yang signifikan pada hasil deteksi tepi citra. ======================================================================================================================================== Edge detection is one of the important image digital processes that are often needed in image analysis. To get a good of the desired result, image is usually processed first with image quality improvement. The edges of an image have important information about the object of the images. There are some traditional edge detection methods such as canny edges that extracted the edges using a specific template with a combination of smoothing functions images. Sometimes, traditional edge detection methods are not effective enough when it used to detect the edges of the image. The edges result of these methods still looks not connected to each other. In this final project, ant-inspired algorithms are used to perform edge detection on the image that has been transformed first into grayscale image format. Transformation into gray image is needed in order to the process of image edge detection become easier. This detection method based on the movement of walking ants looking for food by leaving a trail of pheromones. There are several stages in the final project. The first stage is converted the image to a grayscale format. The next stage is the process of changing the image into image gradient. The last stage is the process of image edge detection using ant-inspired algorithm. From the result of experiments, it can be proven that the ant-inspired algorithm can produce better and accurate edge on image with 88,09% of accuracy rate. Based on the experiments, the greater the number of iterations, it also increases the ability of the algorithm to detect the edges of the image. Increasing the threshold value changing also showed a significant difference in detecting the edges of the image

Generic Techniques in General Purpose GPU Programming with Applications to Ant Colony and Image Processing Algorithms

In 2006 NVIDIA introduced a new unified GPU architecture facilitating general-purpose computation on the GPU. The following year NVIDIA introduced CUDA, a parallel programming architecture for developing general purpose applications for direct execution on the new unified GPU. CUDA exposes the GPU's massively parallel architecture of the GPU so that parallel code can be written to execute much faster than its sequential counterpart. Although CUDA abstracts the underlying architecture, fully utilising and scheduling the GPU is non-trivial and has given rise to a new active area of research. Due to the inherent complexities pertaining to GPU development, in this thesis we explore and find efficient parallel mappings of existing and new parallel algorithms on the GPU using NVIDIA CUDA. We place particular emphasis on metaheuristics, image processing and designing reusable techniques and mappings that can be applied to other problems and domains. We begin by focusing on Ant Colony Optimisation (ACO), a nature inspired heuristic approach for solving optimisation problems. We present a versatile improved data-parallel approach for solving the Travelling Salesman Problem using ACO resulting in significant speedups. By extending our initial work, we show how existing mappings of ACO on the GPU are unable to compete against their sequential counterpart when common CPU optimisation strategies are employed and detail three distinct candidate set parallelisation strategies for execution on the GPU. By further extending our data-parallel approach we present the first implementation of an ACO-based edge detection algorithm on the GPU to reduce the execution time and improve the viability of ACO-based edge detection. We finish by presenting a new color edge detection technique using the volume of a pixel in the HSI color space along with a parallel GPU implementation that is able to withstand greater levels of noise than existing algorithms

Edge Detection Improvement by Ant Colony Optimization

[[abstract]]Edge detection is a technique for marking sharp intensity changes, and is important in further analyzing image content. However, traditional edge detection approaches always result in broken pieces, possibly the loss of some important edges. This study presents an ant colony optimization based mechanism to compensate broken edges. The proposed procedure adopts four moving policies to reduce the computation load. Remainders of pheromone as compensable edges are then acquired after finite iterations. Experimental results indicate that the proposed edge detection improvement approach is efficient on compensating broken edges and more efficient than the traditional ACO approach in computation reduction.[[notice]]補正完畢[[incitationindex]]SC