Pre-processing Technique for Wireless Capsule Endoscopy Image Enhancement

Wireless capsule endoscopy (WCE) is used to examine human digestive tract in order to detect abnormal area. However, it has been a challenging task to detect abnormal area such as bleeding due to poor quality and dark images of WCE. In this paper, pre-processing technique is introduced to ease classification of the bleeding area. Anisotropic contrast diffusion method is employed in our pre-processing technique as a contrast enhancement of the images. There is a drawback to the method proposed B. Li in which the quality of WCE image is degraded when the number of iteration increases. To solve this problem, variance is employed in our proposed method. To further enhance WCE image, Discrete Cosine Transform is used with anisotropic contrast diffusion. Experimental results show that both proposed contrast enhancement algorithm and sharpening WCE image algorithm provide better performance compared with B. Li’s algorithm since SDME and EBCM value is stable whenever number of iterations increases, and sharpness measurement using gradient and PSNR are both improved by 31.5% and 20.3% respectively

Bleeding classification of enhanced wireless capsule endoscopy images using deep convolutional neural network

This paper investigates the performance of a Deep Convolutional Neural Network (DCNN) algorithm to identify bleeding areas of wireless capsule endoscopy (WCE) images without known prior knowledge of bleeding and normal features of the images. In this study, a pre-processing technique has been proposed to improve the classification accuracy of WCE images into bleeding areas and normal areas by enhancing the WCE images. The proposed technique is applied to WCE images from six cases and divided into one training case and five test cases. To evaluate the effectiveness of the processes, the results were then compared between DCNN, SVM and Fuzzy, and also between DCNN with completely enhanced images and DCNN with normalized images. DCNN has shown to give a better result compared to SVM and Fuzzy logic; and the latter experiment has shown that the WCE images that have undergone the proposed enhancement technique gives better classification result compared to those images that did not go through the technique. The specificity, sensitivity and average are 0.8703, 0.8271 and 0.8907 respectively. In conclusion, DCNN has been proven to be able to successfully detecting bleeding areas from images without having any specific knowledge on imaging diagnosis or pathology

Two dimensional active contour model on multigrids for edge detection of images

Low-level tasks have been widely regarded as autonomous bottom-up processes in computational vision research. Examples of low-level tasks are edge detection, stereo matching, and motion tracking. In medical imaging, active contours have also been widely applied for various applications. In fact, active contours is one of the most popular PDE-based tools and powerful tool in performing object tracking. Active contour model, also called classical explicit snake was first introduced by Kass, Witkin and Terzopoulos. The main weaknesses of this method relate to not only the intrinsic characteristics of the contour, but also the parameterization, in which it is unable to handle topological changes. To solve these problems, a different model for active contours based on geometric partial different equation is proposed which is independent of parameterization, intrinsic and very stable. The important development has been the introduction of geodesic active contours. Levelset method was introduced for the moving fronts capture, where the active contour method is given implicitly as the zero level-set of a scalar function defined on implementing the entire image domain. This allows for a much more natural changes in the topology of the curve than parametric snakes. However, the main weakness of level set methods is that the complexity of the computational cost is high. A fast algorithm using semi-implicit addictive operator splitting (AOS) technique is used to restrict the computational cost. Edge detection based on semi-implicit is implemented for the edge detection on medical images such as medical resonance image (MRI). Multigrid is a numerical method that has a good accuracy and stability even with big time step. Exploiting these properties, multigrid was adopted for implementation of the geodesic active contour model. MATLAB has been chosen as the development platformfor the implementations and the experiments since it is well suited for the kind of computations that are required. Besides it is widely used by the image processing community. Experimental results demonstrate the multigrid is the most appropriate method that can applied with AOS implementation for medical imaging to detect the location of the tumor which can decrease number of iterations

Efficient 3D temperature propagation for laser glass interaction

A new algorithm in the class of the AGE method is for developed to solve the heat equation in 3 space dimensions laser glass model problem. AGE method is one of the iterative, convergent, stable and second order accurate with respect to space and time. All the parallel strategies were developed on a CPUs. The distributed parallel computer system was run on the homogeneous cluster of 20 Intel Pentium IV PCs, each with a storage of 20GB and speed of 1.6 MHz. where data decomposition is run asynchronously and concurrently at every time level. The performance evaluations of the algorithm are increasing in terms of speed-up, efficiency and effectiveness

Anisotropic contrast diffusion enhancement using variance for wireless capsule endoscopy images

Wireless capsule endoscopy (WCE) is useful to detect abnormal area in human digestive tract. However, many WCE images have low quality such as rather dark and noisy. Smoothing is an effective way to removing noise and to enhance the images. However, smoothing has a drawback since it can damage the image features. Studies had shown that anisotropic diffusion is a technique used to reduce image noise without removing image features such as edges, lines, textures or other details. These significant parts of the image content are important for the interpretation of the image. To further enhance WCE images, contrast diffusion is used by using Hessian matrix. The original method proposed by B. Li has weakness where it degrades the quality of image when number of iteration goes up. To overcome this problem, standard variance and Gaussian filter is employed in our proposed scheme. Experimental and comparative results of the proposed method are presented in this pape

Pre-processing technique based on discrete cosine transform (DCT) and anisotropic contrast diffusion for wireless capsule endoscopy images

Wireless capsule endoscopy (WCE) is useful to detect abnormal area in human digestive tract. However, many WCE images have low quality such as rather dark and noisy. Pre-processing is an important step in Computer Aided Detection (CAD) system to enhance the quality of WCE images in order to detect the abnormalities easier. To further enhance the image quality, anisotropic diffusion with Hessian matrix proposed by B. Li is applied to get contrast the image. However, this method still has weakness where an image is not sharp. To overcome this problem, Discrete Cosine Transform (DCT) is employed with anisotropic contrast diffusion in our proposed scheme. Experimental results of the proposed method are also presented in this paper