scholarly journals Nonreference Medical Image Edge Map Measure

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Karen Panetta ◽  
Chen Gao ◽  
Sos Agaian ◽  
Shahan Nercessian
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Medical Image ◽  
Visual Analysis ◽  
Medical Image Processing ◽  
Ground Truth ◽  
Detection Algorithm ◽  
Reconstructed Image ◽  
False Alarms ◽  
Original Image

Edge detection is a key step in medical image processing. It is widely used to extract features, perform segmentation, and further assist in diagnosis. A poor quality edge map can result in false alarms and misses in cancer detection algorithms. Therefore, it is necessary to have a reliable edge measure to assist in selecting the optimal edge map. Existing reference based edge measures require a ground truth edge map to evaluate the similarity between the generated edge map and the ground truth. However, the ground truth images are not available for medical images. Therefore, a nonreference edge measure is ideal for medical image processing applications. In this paper, a nonreference reconstruction based edge map evaluation (NREM) is proposed. The theoretical basis is that a good edge map keeps the structure and details of the original image thus would yield a good reconstructed image. The NREM is based on comparing the similarity between the reconstructed image with the original image using this concept. The edge measure is used for selecting the optimal edge detection algorithm and optimal parameters for the algorithm. Experimental results show that the quantitative evaluations given by the edge measure have good correlations with human visual analysis.

scholarly journals Noise Estimation using Kalman Filter in Image Restoration

2019 ◽  
Vol 8 (6S2) ◽  
pp. 888-891
Keyword(s):  
Image Processing ◽  
Kalman Filter ◽  
Image Restoration ◽  
Medical Image ◽  
Blind Deconvolution ◽  
Medical Images ◽  
Medical Image Processing ◽  
Noise Removal ◽  
Original Image ◽  
Degraded Image

Medical image processing plays a vital role in medical sciences from the past decades. Medical image processing becomes simple and useful with the advancement of image processing techniques. Medical images are used to observe the information related to inside the organs of human body. For better diagnoses and analysis of disease the image should be clear, noise free and more informative also. Usually medical images are corrupted by different noises in image acquisition and transmission process. The basic challenge in medical image processing is noise removal without losing diagnostic information. Image restoration is the one of the technique to recover the original image from the degraded image. In this paper, we are proposing a kalman filter to estimate the noise function from the degraded image and to reconstruct the original image. Here we are taking into account that the medical image was corrupted by the gaussian, speckle and salt & pepper noise. The simulation result infers that the proposed blind deconvolution method can be able to suppress the noise well and also preserve edge information without losing diagnostic data.

Application of Improved Sobel Algorithm in Medical Image Edge Detection

2014 ◽  
Vol 678 ◽  
pp. 151-154 ◽  
Author(s):  
De Hai Shen ◽  
Xu E ◽  
Long Chang Zhang
Keyword(s):  
Edge Detection ◽  
Medical Image ◽  
Medical Image Processing ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Gradient Image ◽  
Image Edge Detection ◽  
Image Edge ◽  
Sobel Edge Detection ◽  
Local Gradient

Edge detection plays an important role in medical image processing; its accuracy directly affects the diagnosis and treatment of the disease. In view of the shortcomings of the traditional Sobel algorithm, an improved Sobel edge detection algorithm is proposed in this paper. Algorithm increased 135 º and 45 º direction templates, used the local gradient and standard deviation to filter and strengthen the gradient of initial gradient image. Experiments show that the image edge detected by the new algorithm is relatively accurate, complete and clear compared with the traditional Sobel algorithm, which verifies the effectiveness of the new algorithm.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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