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QUALITATIVE EVALUATION OF QUANTUM ENHANCEMENT FOR EDGE DETECTION OF MEDICAL IMAGES

Achmad Benny Mutiara, Melisa Chatrine Kamu

Published Jan 1, 2015• 4 citations
OpenAlex

Abstract

In recent years, quantum mechanics has been connected with computer science, information theory in communication and digital signal processing. Developing new computing methods and signal processing algorithms by borrowing the principle of quantum mechanics is a very interesting and new research topic. In this research, a quantum digital image processing algorithms will be presented and compared to five other edge detection algorithms, such as Roberts, Sobel, Prewitt, Laplacian of Gaussian, and Canny. Therefore an edge detection method comparison using MATLAB is done in order to find the best edge detection method for qualitative evaluation, namely sensitivity and specificity. From all the comparisons process, the best edge detection method for sensitivity and specificity is known. For the sensitivity, Canny’s edge detection algorithm without quantum enhancement is the best method with sensitivity value amount 83.33% and for the specificity, Roberts edge detection algorithm using quantum enhancement is the best method with specificity value amount 83.33%.

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In recent years, quantum mechanics has been connected with computer science, information theory in communication and digital signal processing. Developing new computing methods and signal processing algorithms by borrowing the principle of quantum mechanics is a very interesting and new research topic. In this research, a quantum digital image processing algorithms will be presented and compared to five other edge detection algorithms, such as Roberts, Sobel, Prewitt, Laplacian of Gaussian, and Canny. Therefore an edge detection method comparison using MATLAB is done in order to find the best edge detection method for qualitative evaluation, namely sensitivity and specificity. From all the comparisons process, the best edge detection method for sensitivity and specificity is known. For the sensitivity, Canny’s edge detection algorithm without quantum enhancement is the best method with sensitivity value amount 83.33% and for the specificity, Roberts edge detection algorithm using quantum enhancement is the best method with specificity value amount 83.33%.
Identification of image edges using edge detection is done to obtain images that are sharp and clear. The selection of the edge detection algorithm will affect the result. Canny operators have an advantage compared to other edge detection operators because of their ability to detect not only strong edges but also weak edges. Until now, Canny edge detection has been done using classical computing where data are expressed in bits, 0 or 1. This paper proposes the identification of image edges using a quantum Canny edge detection algorithm, where data are expressed in the form of quantum bits (qubits). Besides 0 or 1, a value can also be 0 and 1 simultaneously so there will be many more possible values that can be obtained. There are three stages in the proposed method, namely the input image stage, the preprocessing stage, and the quantum edge detection stage. Visually, the results show that quantum Canny edge detection can detect more edges compared to classic Canny edge detection, with an average increase of 4.05%.
边缘检测是图像处理中的基本问题之一，边缘检测的许多算法是基于经典的图像处理技术。本文将介绍基于量子力学框架下的边缘检测量子算法，及其图像特征向量技术在图像检测中的应用。量子算法基于图像经典信息的量子化，通过图像处理中各类元素与量子力学基本物理量一一对应，通过图像特征构造的系统的哈密顿量，应用量子演化技术学习图像的轮廓，从而，给出我们期望的图像轮廓。我们应用像素灰度梯度、像素灰度的二阶差分、Sobel算子卷积等方法作为特征向量来构造哈密顿量。通过一组标准图(从BSDS500公开数据集的图片)测试我们的量子算法，结果显示我们的量子算法的灵敏度比传统Canny算法提高了25%，而且具有更强的抗噪声能力。 Edge detection is one of the fundamental problems in the image processing. Many algorithms of edge detection are based on the classical image processing technology. In this paper, we introduce the quantum algorithm of edge detection based on quantum mechanics and its application in image detection. Quantum algorithm is based on the quantization of the classical information of the image and one-to-one correspondence between various elements of the image processing and the concept and variables of quantum mechanics. By constructing the Hamiltonian for the image features, the quantum evolution technology for the quantum information of images learns the profile of the image, so as to give the desired image contour. We use the techniques of the pixel gray gradient, second order difference of pixel gray, Sobel operator convolution as the feature vectors to construct Hamiltonian. Our quantum algorithms are tested by a set of standard pictures (coming from BSDS500 exposed database). The testing results show that our quantum algorithm has a 25% higher sensitivity than the traditional Canny algorithm and has a stronger denoise ability.
The detection of object edges in images is a crucial step employed in a vast amount of computer vision applications, for which a series of different algorithms has been developed in the last decades. This paper proposes a new edge detection method based on quantum information, which is achieved in two main steps: (i) an image enhancement stage that employs the quantum superposition law and (ii) an edge detection stage based on the probability of photon arrival to the camera sensor. The proposed method has been tested on synthetic and real images devoted to agriculture applications, where Fram & Deutsh criterion has been adopted to evaluate its performance. The results show that the proposed method gives better results in terms of detection quality and computation time compared to classical edge detection algorithms such as Sobel, Kayyali, Canny and a more recent algorithm based on Shannon entropy.
Quantum edge detection is one of the important part of quantum image processing. In this paper, a quantum edge detection algorithm is designed for the quantum representation of multi-wavelength image (QRMW) model. The algorithm includes all stages of filtering, enhancement and detection. The proposed algorithm is also designed to apply any filtering operation to QRMW images, not only for a particular filtering operation. The proposed algorithm aims to solve the problems that quantum edge detection algorithms in the literature have processing only for a particular operator and noise reduction. Moreover, the algorithm aims to perform operations more efficiently by using less resources. Low-pass filter (LPF) smoothing operators are applied in the filtering stage for the noise reduction problem. In order to apply all filtering operations to the image, arithmetic operators that can operate with all signed integers are used in the algorithm. The operators Sobel, Prewitt and Scharr in the enhancement stage and the gradient method in the detection stage are used for both verification of the proposed algorithm and comparisons with the existing algorithms. A method with quantitative outcomes is shown to evaluate the performance of the edge detection algorithms. Analysis of the simulations performed on sample images with different operators. The circuit complexity of the algorithm is presented and the comparisons are made with the existing studies. The superiority of the proposed algorithm and its flexibility to be used in other studies are clearly demonstrated by analysis.
Quantum image processing is a system where both quantum computing and image processings are key. Now it is required to investigate how to apply image processing concepts such as image edge detection and improved Sobel operator, to quantum image processing. Just as traditional image processing helps to image manipulation, our efforts in quantum image processing contribute to the further development of quantum algorithm and quantum information theory. Edge detection is an important problem in traditional image processing, and image edge detection algorithm can filter image edge features and retain important attributes. We proposed a quantum edge detection algorithm, which employs improved Sobel operator to improve the performance, so as to solve the problem of unsatisfactory traditional image edge detection methods. In practical application, the amount of image data to be processed increases sharply, and the computing power of classical computer becomes a limitation. Quantum information processing can effectively accelerate many classical problems by virtue of quantum mechanical characteristics, such as quantum superposition, entanglement, parallelism. Our proposed method uses the novel enhanced quantum representation to store quantum images, which stores all the pixels in the image in a superimposed state, realizing parallel computation. The improved eight-direction Sobel operator is used to calculate the gray gradient, and the quantum circuit is designed to realize quantum edge detection. Simulation results have shown that our algorithm can extract edge with dimension 2n× 2nwhen the computational complexity is O(n2+ 2q+3). The improved algorithm can detect more edge details and has strong adaptability.