Medical and Natural Image Segmentation Algorithm using M-F based Optimization Model and Modified Fuzzy Clustering : A Novel Approach

Bingquan Huo,Guoxin Li,Fengling Yin 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.7

In this paper, we propose and present a novel algorithm for medical image segmentation (MIS). By analyzing the current state-of-the-art related algorithms, we introduce the multi-band active contour model based limit function to make the multilayer segmentation available. With the development of image segmentation technology, the development of medical image segmentation technology also got very big, because there is no find common, accepted effect ideal is suitable for medical image segmentation method, almost existing each kind of segmentation method has application in the field of medical image segmentation. Furtherly, with the optimized aims of being robust to the noise and avoiding the bad effluence on the result, we adopt the kernel method and new initialization curve. This model suffers from low noise robustness, and model algorithm is difficult to achieve. Integrated segmentation technology refers to two or more technology is used, combined with their own advantages, so they can on the accuracy or efficiency to achieve better performance than when using a single. A new penalty term is introduced to improve numerical stability and the step length is increased to improve efficiency. As far as the robustness and effectiveness are concerned, our method is better than the existing medical image segmentation algorithms. Experimental analysis verifies the success of our method.

An Exploratory Study on Tourist Choice Attributes and Characteristics of Medical Tourism in the Philippines

Gwijeong Park(박귀정),Kihan Chung(정기한),Wonjong Kim(김원종),Jaesin Oh(오재신) 한국인터넷전자상거래학회 2017 인터넷전자상거래연구 Vol.17 No.6

The purpose of this study is to present a tourism strategy suitable to market characteristics. Therefore, this study is to identify medical tourism situation in the Philippines, which is a potential competitor of the Korean medical tourism market, and to derive the characteristics of medical tourism selection that can reveal the specificity of the medical tourism market. In order to achieve the purpose of research, we conducted a survey on the actual condition of medical tourism for tourists who had experienced tourism in the Philippines. As a result of this study, we could divide eight dimensions of medical tourism selection attributes in the Philippines, including ‘Political and social image’, ‘Medical service quality’, ‘National image on local people’, ‘facilities and convenience’, ‘social environment’, ‘Medical service reputation’, ‘economic image,’ and ‘tourism attractions’. In addition, cluster analysis was conducted on the basis of the revealed eight dimensions, and it was classified into four subdivided markets: ‘tourist destination priority group’, ‘medical service oriented group’, ‘long term stay oriented group’, and ‘medical tourism group’. Also, as a result of the Chi-square test, it was confirmed that income, occupation, and purpose of visit are significant variables that reveal characteristics of subdivision market. These findings can provide useful implications for future medical tourism strategies.

EISCR : Efficient Image Segmentation using Cluster Representatives for Carcinoma Images

Mustafa Sabah 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.3

Medical care requires intensively Image segmentation which a significant tool used to extract origin of interest from the background. Different segmentation techniques are deployed in medical images leading to an essential developments in both diagnosis and detection process. Such development has managed to assist specialists and doctors specified in the medical care system to diagnose the patients accurately. This study will propose a more developed methodology via incorporating cluster representatives and second derivative filter technique on Carcinoma image. The conventional segmentation algorithm is widely deployed in medical care image, in spite of its advantages; being able to produce a complete division of the image, but it contains major drawbacks represented by the over-segmentations and sensitivity to false edges. This study will expose these major problems of the conventional algorithm and fixing these problems via deploying Cluster Representatives; that uses a set of pixels as representatives that represent the clusters and producing advanced results. The other process deployed is a second derivative filter applied to the segmented image resulting of the demolition of unwanted region from the image. Nevertheless, results of this study has proved that the number of partitions in medical care images are much fewer when comparing partitions produced by deploying the traditional algorithm deployed in medical care images. Such result will assist experts to easily diagnose health problems in a more accurate measure.

An Exploratory Study on Tourist Choice Attributes and Characteristics of Medical Tourism in the Philippines

박귀정,정기한,김원종,오재신 한국인터넷전자상거래학회 2017 인터넷전자상거래연구 Vol.17 No.6

The purpose of this study is to present a tourism strategy suitable to market characteristics. Therefore, this study is to identify medical tourism situation in the Philippines, which is a potential competitor of the Korean medical tourism market, and to derive the characteristics of medical tourism selection that can reveal the specificity of the medical tourism market. In order to achieve the purpose of research, we conducted a survey on the actual condition of medical tourism for tourists who had experienced tourism in the Philippines. As a result of this study, we could divide eight dimensions of medical tourism selection attributes in the Philippines, including ‘Political and social image’, ‘Medical service quality’, ‘National image on local people’, ‘facilities and convenience’, ‘social environment’, ‘Medical service reputation’, ‘economic image,’ and ‘tourism attractions’. In addition, cluster analysis was conducted on the basis of the revealed eight dimensions, and it was classified into four subdivided markets: ‘tourist destination priority group’, ‘medical service oriented group’, ‘long term stay oriented group’, and ‘medical tourism group’. Also, as a result of the Chi-square test, it was confirmed that income, occupation, and purpose of visit are significant variables that reveal characteristics of subdivision market. These findings can provide useful implications for future medical tourism strategies.

Medical Image Segmentation Based on Morphology Algorithm and FCM Algorithm

Shigang Wang,Zhinan Rong,Xueshan Gao 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.10

Fuzzy c-means algorithm is an unsupervised clustering algorithm, its clustering process can reduce the human intervention, and it is suitable for processing medical images of uncertainty and ambiguity. When simply using FCM algorithm in brain image segmentation will leads to the condition of low accuracy. On the basis of FCM algorithm, this paper proposes a new method which combines FCM algorithm and morphology algorithm. The result of simulation shows that this method can accurately and efficiently segment the brain image. The new algorithm is an effective method for image segmentation.

딥러닝 성능 향상을 위한 3D 의료 영상 증강법

박상근(Sangkun Park) (사)한국CDE학회 2021 한국CDE학회 논문집 Vol.26 No.2

This paper proposes a 3D image augmentation method for improving the generalization performance of deep neural networks. It allows us to enrich the diversity of training data samples that is essential in medical image segmentation tasks, thus reducing the data overfitting problem caused by the fact the scale of medical image dataset is typically smaller. It also enables us to predict medical segmentation surfaces in Euclidean space without additional labeled datasets. This method includes image transformation functions, which are comprised of a spatial deformation and image intensity change, enabling the synthesis of complex effects such as variations in anatomy and image acquisition procedures. Our numerical experiments demonstrate that the proposed approach provides significant improvements over state-of-the-art methods for 3D medical image segmentation.

Simulation of Filter Application for Segmentation of Medical Images

Young-Bok Cho,Min-Kang Kim,Sung-Hee Woo 한국정보통신학회 2018 2016 INTERNATIONAL CONFERENCE Vol.10 No.1

Image Segmentation segments an image into different homogenous regions. An efficient semantic based image retrieval system divides the image into different regions separated by color or texture sometimes even both. This paper two techniques of image segmentation to facilitate image edge detection, that can be used further by image analysis based on the extracted features of image edges, Canny edge detection and Otsu threshold are examples of the proposed techniques, the paper evaluates the effectiveness of the two methods with a variety of images, testing their suitability to natural as well as medical images.

3차원 의료 영상의 영역 분할을 위한 효율적인 데이터 보강 방법

박상근,Park, Sangkun 한국교통대학교 융복합기술연구소 2021 융ㆍ복합기술연구소 논문집 Vol.11 No.1

Deep learning based methods achieve state-of-the-art accuracy, however, they typically rely on supervised training with large labeled datasets. It is known in many medical applications that labeling medical images requires significant expertise and much time, and typical hand-tuned approaches for data augmentation fail to capture the complex variations in such images. This paper proposes a 3D image augmentation method to overcome these difficulties. It allows us to enrich diversity of training data samples that is essential in medical image segmentation tasks, thus reducing the data overfitting problem caused by the fact the scale of medical image dataset is typically smaller. Our numerical experiments demonstrate that the proposed approach provides significant improvements over state-of-the-art methods for 3D medical image segmentation.

비모수적 베이지안 추론방법에 기반을 둔 2차원 무한 은닉 마코프 메쉬 모형을 이용한 영상분할

김선월(Sun-Worl Kim),조완현(Wan-Hyun Cho) 한국정보과학회 2012 정보과학회논문지 : 소프트웨어 및 응용 Vol.39 No.11

본 논문에서는 영상분할을 실시할 때 사전의 정보 없이 상태의 수를 자동으로 결정하는 새로운 2차원 무한 은닉 마코프 메쉬모형을 제안한다. 영상을 확률모형으로 표현할 때 사용되는 2차원 은닉 마코프 메쉬모형은 이웃시스템을 이용하여 이전의 시점을 정의하고 인과관계를 통하여 전이확률을 계산한다. 그리고 영상의 최적분할을 위한 각 화소의 상태행렬을 비모수적 베이지안 추론방법으로 추정한다. 이때 유동적인 무한상태의 수를 갖는 상태행렬에 대한 사전분포는 계층적 디리쉴레 확률과정을 가정하고, 관측 값에 대한 확률분포는 유한혼합분포를 가정하여 블록화 깁스샘플링 방법을 통하여 최적의 상태 수와 가정된 모형의 모수를 자율적으로 결정한다. 최종적으로 각 화소의 상태행렬에 대한 사후확률을 계산하고 이중 최댓값을 갖는 상태로 해당 화소를 할당하여 영상분할을 수행한다. 그리고 다양한 의료영상에 대하여 제안된 방법과 기존방법의 비교실험을 통하여 제안된 방법의 우수성을 입증할 수 있는 실험한 결과를 제시하였다. In this study, we propose the new method to automatically select the number of states without a prior information for image segmentation. The Markov mesh model used to express images as probabilistic model in 2D image defines the time before using a neighbor system, and can calculate the transition probability by the causality. Then the state matrix of 2-dimensional infinite hidden Markov mesh model for the optimal segmentation is estimated by the nonparametric Bayesian Inference. The number of states gets a infinite number instead of finite number by applying a hierarchical Dirichlet process for prior distribution of state matrix, and the optimal number is automatically selected by the blocked Gibbs sampling method. Finally, the image segmentation is performed by assignment the pixels to state having the maximum a posterior probability, and we show that our method get better results in comparison experiments with existing method.

Segmentation of Mammography Breast Images using Automatic Segmen Adversarial Network with Unet Neural Networks

Suriya Priyadharsini.M,J.G.R Sathiaseelan International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.12

Breast cancer is the most dangerous and deadly form of cancer. Initial detection of breast cancer can significantly improve treatment effectiveness. The second most common cancer among Indian women in rural areas. Early detection of symptoms and signs is the most important technique to effectively treat breast cancer, as it enhances the odds of receiving an earlier, more specialist care. As a result, it has the possible to significantly improve survival odds by delaying or entirely eliminating cancer. Mammography is a high-resolution radiography technique that is an important factor in avoiding and diagnosing cancer at an early stage. Automatic segmentation of the breast part using Mammography pictures can help reduce the area available for cancer search while also saving time and effort compared to manual segmentation. Autoencoder-like convolutional and deconvolutional neural networks (CN-DCNN) were utilised in previous studies to automatically segment the breast area in Mammography pictures. We present Automatic SegmenAN, a unique end-to-end adversarial neural network for the job of medical image segmentation, in this paper. Because image segmentation necessitates extensive, pixel-level labelling, a standard GAN's discriminator's single scalar real/fake output may be inefficient in providing steady and appropriate gradient feedback to the networks. Instead of utilising a fully convolutional neural network as the segmentor, we suggested a new adversarial critic network with a multi-scale L1 loss function to force the critic and segmentor to learn both global and local attributes that collect long- and short-range spatial relations among pixels. We demonstrate that an Automatic SegmenAN perspective is more up to date and reliable for segmentation tasks than the state-of-the-art U-net segmentation technique.