다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

CA : A Cancer Journal for Clinicians

We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. The comparison is based on the combined performance measures. We identify the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1631316)

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Survey over image thresholding techniques and quantitative performance evaluation

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Fuzzy subfiber and its application to seismic lithology classification

Breast ultrasound (BUS) image segmentation is challenging and critical for BUS Computer-Aided Diagnosis (CAD) systems. Many BUS segmentation approaches have been proposed in the last two decades, but the performances of most approaches have been assessed using relatively small private datasets with different quantitative metrics, which result in discrepancy in performance comparison.

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A Benchmark for Breast Ultrasound Image Segmentation (BUSIS)

A novel approach of applying the theory of fuzzy sets and moment invariants to analyze pavement images in proposed in this paper. By applying the theory of fuzzy sets and calculating moment invariants from different types of distress, features are obtained. Then, a back-propagation neural network is used to classify these features. The crack density is used to obtain extent information. This approach is illustrated using randomly selected samples from NCHRP Project 1-27 video images of real cracks. Based on these samples, the feasibility of using the theory of fuzzy sets and moment invariants to classify different types of crack is proven. High accuracy of classification is also obtained.

Pavement distress evaluation using fuzzy logic and moment invariants

In this paper, a novel lesion segmentation within breast ultrasound (BUS) image based on the cellular automata principle is proposed. Its energy transition function is formulated based on global image information difference and local image information difference using different energy transfer strategies. First, an energy decrease strategy is used for modeling the spatial relation information of pixels. For modeling global image information difference, a seed information comparison function is developed using an energy preserve strategy. Then, a texture information comparison function is proposed for considering local image difference in different regions, which is helpful for handling blurry boundaries. Moreover, two neighborhood systems (von Neumann and Moore neighborhood systems) are integrated as the evolution environment, and a similarity-based criterion is used for suppressing noise and reducing computation complexity. The proposed method was applied to 205 clinical BUS images for studying its characteristic and functionality, and several overlapping area error metrics and statistical evaluation methods are utilized for evaluating its performance. The experimental results demonstrate that the proposed method can handle BUS images with blurry boundaries and low contrast well and can segment breast lesions accurately and effectively.

An effective approach of lesion segmentation within the breast ultrasound image based on the cellular automata principle.

Breast cancer is still a serious disease in the world Early detection is very essential for breast cancer prevention and diagnosis Breast ultrasound (US) imaging has been proven to be a valuable adjunct to mammography in the detection and classification of breast lesions Because of the fuzzy and noisy nature of the US images and the low contrast between the breast cancer and tissue, it is difficult to provide an accurate and effective diagnosis This paper presents a novel algorithm based on fuzzy logic that uses both the global and local information and has the ability to enhance the fine details of the US images while avoiding noise amplification and overenhancement We normalize the images and then fuzzify the normalized images based on the maximum entropy principle Edge and textural information are extracted to describe the lesion features and the scattering phenomenon of US images and the contrast ratio measuring the degree of enhancement is computed and modified The defuzzification process is used to obtain the enhanced US images To demonstrate the performance of the proposed approach, the algorithm was tested on 86 breast US images Experimental results confirm that the proposed method can effectively enhance the details of the breast lesions without overenhancement or underenhancement

Heng-Da Cheng

Papers

Fuzzy subfiber and its application to seismic lithology classification

A Benchmark for Breast Ultrasound Image Segmentation (BUSIS)

Pavement distress evaluation using fuzzy logic and moment invariants

An effective approach of lesion segmentation within the breast ultrasound image based on the cellular automata principle.

Breast ultrasound image enhancement using fuzzy logic