Du-Yih Tsai - Academia.edu (original) (raw)

Papers by Du-Yih Tsai

WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000

Abstract The aim of this paper is to compare the performance of four different methods, ie, neura... more Abstract The aim of this paper is to compare the performance of four different methods, ie, neural network (NN) with backpropagation learning, NN with genetic-algorithm-based (GA-based) learning, fuzzy reasoning, and the GA-based fuzzy logic approach, for automated ...

Japanese Journal of Medical Electronics and Biological Engineering, 2002

Medical Imaging and Information Sciences, 1984

Medical Imaging and Information Sciences, 1997

Medical Imaging and Information Sciences, 1994

Medical Imaging and Information Sciences, 2006

Medical Imaging Technology, 2012

Copyright © 2013 Du-Yih Tsai et al. This is an open access article distributed under the Creative... more Copyright © 2013 Du-Yih Tsai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We propose a method for improving image quality in medical images by using a wavelet-based approach. The proposed method integrates two components: image denoising and image enhancement. In the first component, a modified undecimated discrete wavelet transform is used to eliminate the noise. In the second component, a wavelet coefficient mapping function is applied to enhance the contrast of denoised images obtained from the first component.This methodology can be used not only as a means for improving visual quality of medical images but also as a preprocessing module for computer-aided detection/diagnosis systems to improve the performance of screening and detecting regions of interest in images. To confirm its superiority over existi...

Journal of Biomedical Science and Engineering, 2016

Computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by cli... more Computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases. In general, a CAD system employs a classifier to detect or distinguish between abnormal and normal tissues on images. In the phase of classification, a set of image features and/or texture features extracted from the images are commonly used. In this article, we investigated the characteristic of the output entropy of an image and demonstrated the usefulness of the output entropy acting as a texture feature in CAD systems. In order to validate the effectiveness and superiority of the output-entropy-based texture feature, two well-known texture features, i.e., mean and standard deviation were used for comparison. The database used in this study comprised 50 CT images obtained from 10 patients with pulmonary nodules, and 50 CT images obtained from 5 normal subjects. We used a support vector machine for classification. A leave-one-out method was employed for training and classification. Three combinations of texture features, i.e., mean and entropy, standard deviation and entropy, and standard deviation and mean were used as the inputs to the classifier. Three different regions of interest (ROI) sizes, i.e., 11 × 11, 9 × 9 and 7 × 7 pixels from the database were selected for computation of the feature values. Our experimental results show that the combination of entropy and standard deviation is significantly better than both the combination of mean and entropy and that of standard deviation and mean in the case of the ROI size of 11 × 11 pixels (p < 0.05). These results suggest that information entropy of an image can be used as an effective feature for CAD applications. E. Matsuyama et al.

Abstract An artificial neural network approach to classification of possible tumors into benign a... more Abstract An artificial neural network approach to classification of possible tumors into benign and malignant ones in mammograms was developed earlier by the authors (1993), and an average of 75% recognition rate was shown. In this paper the authors propose a different ...

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)

This paper describes a segmentation method of liver structure from abdominal CT images using an a... more This paper describes a segmentation method of liver structure from abdominal CT images using an artificial neural network (NN), together with a priori information about liver location and area in the abdomen cross section and digital imaging processing techniques. This approach based on the NN is to classify each pixel on an image into one of three categories: boundary, liver, and nonliver. Supervised training technique is used. The training data set is obtained from one of the given set of images by creating gray level histograms for the three categories. The histograms are considered as the respective feature values. Prior to NN classification, preprocessing is employed to locally enhance the contrast of the region of interest. Postprocessing are also applied after the NN classification to smooth the detected boundary. Our preliminary results show that the proposed method has potential utility in automatic segmentation of liver structure and other organs in the human body.<<ETX>>

Japanese Journal of Applied Physics, 1978

Abstract A method for the quality evaluation of radiographic images in terms of entropy is presen... more Abstract A method for the quality evaluation of radiographic images in terms of entropy is presented. By this method, the image quality can be synthetically evaluated by a single number. The method presented is used to calculate the amount of information contributed ...

Open Journal of Medical Imaging, 2020

Classification of breast density is significantly important during the process of breast diagnosi... more Classification of breast density is significantly important during the process of breast diagnosis. The purpose of this study was to develop a useful computerized tool to help radiologists determine the patient's breast density category on the mammogram. In this article, we presented a model for automatically classifying breast densities by employing a wavelet transform-based and fine-tuned convolutional neural network (CNN). We modified a pre-trained AlexNet model by removing the last two fully connected (FC) layers and appending two newly created layers to the remaining structure. Unlike the common CNN-based methods that use original or pre-processed images as inputs, we adopted the use of redundant wavelet coefficients at level 1 as inputs to the CNN model. Our study mainly focused on discriminating between scattered density and heterogeneously dense which are the two most difficult density categories to differentiate for radiologists. The proposed system achieved 88.3% overall accuracy. In order to demonstrate the effectiveness and usefulness of the proposed method, the results obtained from a conventional fine-tuning CNN model was compared with that from the proposed method. The results demonstrate that the proposed technique is very promising to help radiologists and serve as a second eye for them to classify breast density categories in breast cancer screening.