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Comparison of Active Contour and Active Shape Approaches for Corpus Callosum Segmentation
Adiya, Enkhbolor,Izmantoko, Yonny S.,Choi, Heung-Kook Korea Multimedia Society 2013 멀티미디어학회논문지 Vol.16 No.9
The corpus callosum is the largest connective structure in the brain, and its shape and size are correlated to sex, age, brain growth and degeneration, handedness, musical ability, and neurological diseases. Manually segmenting the corpus callosum from brain magnetic resonance (MR) image is time consuming, error prone, and operator dependent. In this paper, two semi-automatic segmentation methods are present: the active contour model-based approach and the active shape model-based approach. We tested these methods on an MR image of the human brain and found that the active contour approach had better segmentation accuracy but was slower than the active shape approach.
Cell Segmentation in Phase Contrast Microscopy by Constrained Optimization
Adiya, Enkhbolor,Vongphachah, Bouasone,Al-Shidaifat, Alaaddin,Rentsen, Enkhbat,Choi, Heung-Kook IGI Global 2015 International journal of e-health and medical comm Vol.6 No.1
<P>Utilization of automatic cell segmentation process is difficult to identify in a cell due to halo and shade-off distortions when observing the phase contrast microscopy images. Therefore, it is an important step to restore artifact-free images made ready for segmentation process. The main focus of this paper is to define a gradient projection algorithm to restore images based on the minimization problem of quadratic objective function with non-negative constraints. The proposed algorithm converges to a global minimum solution independent on initialization. The experimental result shows that the proposed algorithm can restore artifact-free images, which could produce high quality segmentation results using a simple thresholding method.</P>
Adiya, Enkhbolor,Yadam, Bazarsad,Choi, Heung-Kook Korea Multimedia Society 2014 The journal of multimedia information system Vol.1 No.1
The aim of microscopic image restoration is to recover the image by applying the inverse process of degradation, and the results facilitate automated and improved analysis of the image. In this work, we consider the problem of image restoration as a minimization problem of convex cost function, which consists of a least-squares fitting term and regularization terms with non-negative constraints. The finite step method is proposed to solve this constrained convex optimization problem. We demonstrate the convergence of this method. Efficiency and restoration capability of the proposed method were tested and illustrated through numerical experiments.
A MULTIPHASE LEVEL SET FRAMEWORK FOR IMAGE SEGMENTATION USING GLOBAL AND LOCAL IMAGE FITTING ENERGY
DULTUYA TERBISH,ENKHBOLOR ADIYA,MYUNGJOO KANG 한국산업응용수학회 2017 Journal of the Korean Society for Industrial and A Vol.21 No.2
Segmenting the image into multiple regions is at the core of image processing. Many segmentation formulations of an images with multiple regions have been suggested over the years. We consider segmentation algorithm based on the multi-phase level set method in this work. Proposed method gives the best result upon other methods found in the references. Moreover it can segment images with intensity inhomogeneity and have multiple junction. We extend our method (GLIF) in [T. Dultuya, and M. Kang, Segmentation with shape prior using global and local image fitting energy, J.KSIAM Vol.18, No.3, 225–244, 2014.] using a multiphase level set formulation to segment images with multiple regions and junction. We test our method on different images and compare the method to other existing methods.
Contrast Enhancement for Segmentation of Hippocampus on Brain MR Images
Sengee, Nyamlkhagva,Sengee, Altansukh,Adiya, Enkhbolor,Choi, Heung-Kook Korea Multimedia Society 2012 멀티미디어학회논문지 Vol.15 No.12
An image segmentation result depends on pre-processing steps such as contrast enhancement, edge detection, and smooth filtering etc. Especially medical images are low contrast and contain some noises. Therefore, the contrast enhancement and noise removal techniques are required in the pre-processing. In this study, we present an extension by a novel histogram equalization in which both local and global contrast is enhanced using neighborhood metrics. When checking neighborhood information, filters can simultaneously improve image quality. Most important is that original image information can be used for both global brightness preserving and local contrast enhancement, and image quality improvement filtering. Our experiments confirmed that the proposed method is more effective than other similar techniques reported previously.
Implementation of 2D Active Shape Model-based Segmentation on Hippocampus
Izmantoko, Yonny S.,Yoon, Ho-Sung,Adiya, Enkhbolor,Mun, Chi-Woong,Huh, Young,Choi, Heung-Kook Korea Multimedia Society 2014 멀티미디어학회논문지 Vol.17 No.1
Hippocampus is an important part of brain which is related with early memory storage and spatial navigation. By observing the anatomy of hippocampus, some brain diseases effecting human memory (e.g. Alzheimer, schizophrenia, etc.) can be diagnosed and predicted earlier. The diagnosis process is highly related with hippocampus segmentation. In this paper, hippocampus segmentation using Active Shape Model, which not only works based on image intensity, but also by using prior knowledge of hippocampus shape and intensity from the training images, is proposed. The results show that ASM is applicable in segmenting hippocampus from whole brain MR image. It also shows that adding more images in the training set results in better accuracy of hippocampus segmentation.