• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.329-332
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• 2009

Speckle noise reduction for ultrasound CT image using morphological adaptive median filtering based on edge preservation is presented in this paper. Speckle noise is multiplicative feature and causes ultrasound image to degrade widely from transducer. An input image is classified into edge region and homogeneous region in preprocessing. The speckle is reduced by morphological operation on the 2D gray scale by using convolution and correlation, and edges are preserved. The adaptive median is processed to reduce an impulse noise. As the result the proposed method enhances the image to about 20% in comparison with Winer filter by Edge Preservation Index and PSNR.

• Journal of KIISE:Software and Applications
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• v.31 no.1
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• pp.89-99
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• 2004

This paper proposes a morphological analysis method that enables morphological analysis by checking conditions between two adjacent morphemes. These conditions are fed from a dictionary. This method eliminates a code conversion module and the application of transformational rules for candidate generation. The method claims that very high speed morphological analysis is attainable through simple bit operations for adjacency condition check. MACH, an implementation of the proposed method, is a supersonic Korean morphological analyzer which is able to analyze a document of 1 GB in 5 minutes on a PC with 1.13 GHz Pentium III CPU. The analysis accuracy of MACH is 99.2 %.

• Journal of Chest Surgery
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• v.12 no.2
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• pp.119-126
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• 1979

This is one case report of the extremely rare congenital cardiac malformation, Double-outlet of left ventricle in corrected transposition of great arteries. 11-year-old boy complained acrocyanosis and exertional dyspnea, the parents noticed cyanosis since birth. Physical examination revealed acrocyanosis, clubbed fingers and toes, G-III pansystolic murmur on 2nd and 3rd ICS, LSB. Right heart catheterization revealed significant $O_2$ jump in ventricular level. Right and left ventriculography showed the both catheters arriving in the same ventricle i.e. anterior chamber, morphological left ventricle was in right and anterior position, simultaneous visualization of aorta and pulmonary artery and aorta locating anterior and right side of pulmonary artery. Echo cardiogram surely disclosed interventricular septum. Conclusively it was clarified that the patient has Double-outlet of left ventricle and corrected transposition of great arteries [S.L.D.]. Operation was performed to correct the anomalies under extracorporeal circulation with intermittent moderate hypothermia. Right-sided ventriculotomy disclosed the following findings. 1. Right-sided ventricle was morphological left ventricle. 2. Left-sided ventricle was morphological right ventricle. 3. Right side atrioventricular valve was bicuspid. 4. Left side atrioventricular valve was tricuspid. 5. Aortic valve was superior, anterior and right side of pulmonary valve. 6. Subpulmonary membranous stenosis. 7. Non-committed ventricular septal defect. We made a tunnel between VSD and aorta with Teflon patch so that arterial blood comes through VSD and the tunnel into aorta. After correction the patient needed assisted circulation for 135 min. to have adequate blood pressure. Postoperatively by any means, adequate blood pressure could not be maintained and expired in the evening of operation day.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.52-58
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• 1995

A shared-weight neural network that performed classification based on the features extracted with the fuzzy morphological operation is introduced. Learning rules for the structuring elements, degree of membership, and weighting factors are also precisely described. In application to handwritten digit recognition problem, the fuzzy morphological shared-weight neural network produced the results which are comparable to the state-of-art for this problem.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.1
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• pp.92-98
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• 2019

In this paper, we propose a deep learning system based on morphological neural network(MNN). The deep learning layers are morphological operation layer, pooling layer, ReLU layer, and the fully connected layer. The operations used in morphological layer are erosion, dilation, and edge detection, etc. Unlike CNN, the number of hidden layers and kernels applied to each layer is limited in MNN. Because of the reduction of processing time and utility of VLSI chip design, it is possible to apply MNN to various mobile embedded systems. MNN performs the edge and shape detection operations with a limited number of kernels. Through experiments using database images, it is confirmed that MNN can be used as a deep learning system and its performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5112-5129
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• 2019

Image enhancement is a challenging problem in the field of image processing, especially low-light color images enhancement. This paper proposed a robust and comprehensive enhancement method based several points. First, the idea of bright channel is introduced to estimate the illumination map which is used to attain the enhancing result with Retinex model, and the color constancy is keep as well. Second, in order eliminate the illumination offsets wrongly estimated, morphological closing operation is used to modify the initial estimating illumination. Furthermore, in order to avoid fabricating edges, enlarged noises and over-smoothed visual features appearing in enhancing result, a multi-scale closing operation is used. At last, in order to avoiding the haloes and artifacts presented in enhancing result caused by gradient information lost in previous step, guided filtering is introduced to deal with previous result with guided image is initial bright channel. The proposed method can get good illumination map, and attain very effective enhancing results, including dark area is enhanced with more visual features, color natural and constancy, avoiding artifacts and over-enhanced, and eliminating Incorrect light offsets.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.900-902
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• 2015

In the modern society, according to the advancement in digital image processing technology, edge detection is being utilized in various application sectors such as smart device and medical, etc. In existing edge detection methods, there are Sobel, Prewitt, Roberts and Laplacian, etc, which uses the mask. These previous methods are easy to implement but shows somewhat insufficient results. Therefore, in order to compensate the problems of existing methods, in this paper, an algorithm that detects the edge using the local mask and morphological operation was proposed and the detection performance was compared against the previous methods.

• The Transactions of the Korea Information Processing Society
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• v.3 no.3
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• pp.449-459
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• 1996

The main goal of this paper is to introduce a novel definition for fuzzy mathematical morphology and a neural network implementation. The generalized- mean operator plays the key role for the definition. Such definition is well suited for neural network implementation. The first stage of the shared-weight neural network has adequate architecture to perform morphological operation. The shared- weight network performs classification based on the features extracted with the fuzzy morphological operation defined in this paper. Therefore, the parameters for the fuzzy definition can be optimized using neural network learning paradigm. Learning rules for the structuring elements, degree of membership, and weighting factors are precisely described. In application to handwritten digit recognition problem, the fuzzy morphological shared-weight neural network produced the results which are comparable to the state-of art for this problem.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.4
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• pp.191-194
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• 2014

This paper proposes a fast and efficient method to extract the skin and bone automatically in CT images. First, the images were smoothed by applying an anisotropic diffusion filter to remove noise. The whole body was then detected by thresholding, which was set automatically. In addition, the contour of the skin was segmented using morphological operators and connected component labeling (CCL). Finally, the bone was extracted by iterative thresholding.

• The Journal of the Acoustical Society of Korea
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• v.18 no.3E
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• pp.37-43
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• 1999

In this paper, we present a new technique for the optimal local decomposition of convex structuring elements on a hexagonal grid, which are used as templates for morphological image processing. Each basis structuring element in a local decomposition is a local convex structuring element, which can be contained in hexagonal window centered at the origin. Generally, local decomposition of a structuring element results in great savings in the processing time for computing morphological operations. First, we define a convex structuring element on a hexagonal grid and formulate the necessary and sufficient conditions to decompose a convex structuring element into the set of basis convex structuring elements. Further, a cost function was defined to represent the amount of computation or execution time required for performing dilations on different computing environments and by different implementation methods. Then the decomposition condition and the cost function are applied to find the optimal local decomposition of convex structuring elements, which guarantees the minimal amount of computation for morphological operation. Simulation shows that optimal local decomposition results in great reduction in the amount of computation for morphological operations. Our technique is general and flexible since different cost functions could be used to achieve optimal local decomposition for different computing environments and implementation methods.