scholarly journals A New Adaptive Edge Enhancement Algorithm for Color Laser Printers

2007 ◽  
Author(s):  
Manoj Kumar Masthan Reddy ◽  
Vladimir Misic ◽  
Eli Saber ◽  
Jeff Trask
Keyword(s):  
Edge Enhancement ◽  
Laser Printers ◽  
Enhancement Algorithm

A Urinary Sediment Segmentation Algorithm of Cast and Epithelial Cells

2013 ◽  
Vol 457-458 ◽  
pp. 1046-1049
Author(s):  
Yan Hai Wu ◽  
Meng Kang
Keyword(s):  
Epithelial Cells ◽  
Edge Detection ◽  
Small Area ◽  
Segmentation Algorithm ◽  
Edge Enhancement ◽  
Fusion Technology ◽  
Morphological Operation ◽  
Urinary Sediment ◽  
Sobel Edge Detection ◽  
Enhancement Algorithm

Based on cast and epithelial cells to segment difficultly in urinary sediment, this paper propose one modified segmentation algorithm that is combining Sobel edge detection with edge enhancement algorithm, and using the fusion technology to get the whole binary results. Using cast and epithelial having great area is to remove small area impurity. Last after morphological operation can deserve a good split binary image. Experimental results show that the algorithm performs faster, complete binary cells and higher precision than segmentation algorithm from now on. The algorithm is not only suitable for cast and other large cells, but also satisfied for red and white cells, crystallize, sperm in urinary sediment.

scholarly journals Detection of Proximal Caries at The Molar Teeth Using Edge Enhancement Algorithm

2018 ◽  
Vol 8 (5) ◽  
pp. 3259
Author(s):  
Jufriadif Na'am ◽  
Johan Harlan ◽  
Sarifuddin Madenda ◽  
Julius Santony ◽  
Catur Suharinto
Keyword(s):  
Edge Detection ◽  
General Hospital ◽  
Edge Enhancement ◽  
Radiographic Image ◽  
Accuracy Rate ◽  
Proximal Caries ◽  
X Ray ◽  
Improve Accuracy ◽  
Molar Teeth ◽  
Enhancement Algorithm

<span>Panoramic X-Ray produces produces the most common oral digital radiographic image that it used in dentistry practice. The image can further improve accuracy compared to analog one. This study aims to establish proximal caries edge on enhancement images so they can be easily recognized. The images were obtained from the Department of Radiology, General Hospital of M. Djamil Padang Indonesia. Total file of images to be tested were 101. Firstly, the images are analyzed by dentists who practiced at Segment Padang Hospital Indonesia. They concluded that there is proximal caries in 30 molar teeth. Furthermore, the images were processed using Matlab software with the following steps, i.e. cropping, enhancement, edge detection, and edge enhancement. The accuracy rate of detection of edge enhancement images being compared with that of dentist analysis was 73.3%. In the edge enhancement images proximal caries edge can be found conclusively in 22 teeth and dubiously in eight teeth. The results of this study convinced that edge enhancement images can be recommended to assist dentists in detecting proximal caries. </span>

Combined Enhancement Algorithm Based on Muti-Scale Analysis

2013 ◽  
Vol 756-759 ◽  
pp. 3215-3219
Author(s):  
Wei Guo Yuan ◽  
Song Wang
Keyword(s):  
Wavelet Transform ◽  
Image Enhancement ◽  
Conventional Method ◽  
Simulation Experiment ◽  
Curvelet Transform ◽  
Cross Entropy ◽  
Scale Analysis ◽  
Edge Enhancement ◽  
Visual Effect ◽  
Enhancement Algorithm

Based on combinations of wavelet and curvelet transforms, an adaptive combined enhancement algorithm (ACEA) was proposed to solve the problems of the conventional method for image enhancement based on wavelet transform that the enhancement of edges whose contrast was same but angles were different was greatly different. The algorithm uses the wavelet transform to enhancement homogeneous areas and the curvelet transform to enhancement areas with edges. Simulation experiment results demonstrated that the algorithm got better cross entropy and CORR, improved effectively the effect of edge enhancement with good visual effect.

Self-Example-Based Edge Enhancement Algorithm for Around View Monitor Images

2017 ◽  
Vol 2017 (2) ◽  
pp. 60-64 ◽  
Author(s):  
Dong Yoon Choi ◽  
Ji Hoon Choi ◽  
Jin Wook Choi ◽  
Byung Cheol Song
Keyword(s):  
Edge Enhancement ◽  
Enhancement Algorithm
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