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A Classification Model for the Prostate Cancer Based on Deep Learning 10

A Classification Model for the Prostate Cancer Based on Deep...

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10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Liu, Ying An, Xiaomei Univ Shanghai Sci & Technol Sch Med Instrument & Food Engn Shanghai Peoples R China

ISBN: (纸本)9781538619377

Regarded as one of the common cancers, the prostate cancer is a main reason harming the health of senile men, especially in Europe and the United States. In China, with increasing of living standards and aging of populations, the incidence of prostate cancer has an upward and younger tendency. Early detection and early treatment are helpful to patients. In the imaging diagnosis methods of prostate, magnetic resonance imaging (MRI) has been recognized as the most effective way. Whereas MRI image has several specialized configurations with a lot of medical information, and diagnosis results have a strong relationship with doctor's professional skill and experience, which makes the diagnosis for prostate cancer more difficult. Based on deep learning and the Convolutional Neural Networks (CNN), an image classification model which can provide some diagnosis classification reference was proposed in this paper. The data sets used in this paper consisted of 10056 diffusion weighted magnetic resonance imaging (DWI) images. Three quarters of the images were used for training and the rest images for testing. Experiments show that the accuracy rate of training set is 80.1539%, and the accuracy rate of testing set is 78.1538%. The curves of testing accuracy rate and loss show that this model has been trained steadily. The accuracy rate for single images are above 64.91%, and some may reach 99.99%. With certain clinical application value, this deep learning method can be widely applied to the grading and staging of prostate cancer and other cancer tasks.

关键词： deep learning prostate cancer Convolutional Neural Networks image classification

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Sharpness-Preservation Video Upscaling 10

Sharpness-Preservation Video Upscaling

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10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Wang, Zhongyuan Zhou, Liguo Zhu, Rong He, Zheng Chen, Dan Wuhan Univ Sch Comp NERCMS Wuhan 430072 Hubei Peoples R China Wuhan Univ Shenzhen Res Inst Shenzhen 518057 Peoples R China

ISBN: (纸本)9781538619377

Video upscaling is a common task performing the adaption of the actual video resolution to the screen display area. The extensively used interpolation methods are easily implemented but usually result in some artifacts, such as blurring and aliasing effects. This paper proposes to combine sharpening with interpolation to reduce the blurring effects of enlarged videos, in which videos are firstly preprocessed with sharpening to enhance profiles so as to compensate the loss of high-frequency components during interpolation. To tune sharpness degree in sharpening procedure, the blurriness metric considering image gradients as well as pixel intensities is particularly suggested and the generation rule for sharpening strength is accordingly regulated. In practice, three representative interpolation methods, namely, Bilinear, Bicubic and Lanczos, are experimentally incorporated into our framework to carry out validation. Results with respect to both original and decoded videos show the significant objective and subjective improvements against the respective regular interpolation.

关键词： video upscaling image interpolation image sharpening blurriness metric

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A Modified MRF Segmentation of Brain MR images 10

A Modified MRF Segmentation of Brain MR Images

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10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Zhang, Yuhao Lu, Pei Liu, Xiaoyun Zhou, Shoujun Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538619377

image noise seriously affects the accuracy of brain magnetic resonance (MR) image classification. The traditional Gaussian mixture model (GMM) is widely applied in image segmentation, which uses only image intensity information thus is susceptible to image noise. Markov random field (MRF) model overcome the problem by using a priori probability for local smoothing and denoising, which depends on the pixel class labelling with the Gibbs distribution. However, the MRF model still lacks accurate classification of brain tissue. To improve the segmentation of brain MR image, the paper proposes a joint segmentation scheme based on combination of GMM and MRF model to classify the brain tissue into three parts: white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF). During the implementation, both image intensity and local neighbouring voxels are considered. The main steps are as follows: 1) we use K-means estimation to acquire the initial distribution parameters of the three brain tissues;then use Expectation maximization (EM) estimation to obtain GMM parameters. 2) For the spatial voxels, the energy function of joint probability is change in terms of their corresponding GMM class information;then the intensity-related energy function's component is controlled. Therefore, the parameter of the joint model is estimated adaptively according to the local intensity and spatial information. The proposed method performs well in the experiments, for example, it is not sensitive to image noise, and has good robustness and segmentation accuracy as well as high computational efficiency.

关键词： brain magnetic resonance image Gaussian mixture model Markov random field joint energy function

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Segmentation of GBM in MRI images Using an Efficient Speed Function Based on Level Set Method 10

Segmentation of GBM in MRI Images Using an Efficient Speed F...

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10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Mojtabavi, Alireza Farnia, Parastoo Ahmadian, Alireza Alimohamadi, Meysam Pourrashidi, Ahmad Rad, Hamidreza Saligheh Alirezaie, Javad Univ Tehran Med Sci RCBTR Image Guided Surg Grp Tehran Iran Univ Tehran Med Sci Sina Specialized & Subspecialty Hosp Tehran Iran Ryerson Univ Dept Elect Engn Toronto ON Canada

ISBN: (纸本)9781538619377

Accurate segmentation and characterization of abnormalities in brain tumor are challenging task, especially in the case of GBM tumors, where the ambiguities presented in the boundaries of these tumors necessitates using efficient segmentation method. Level set methods have proven to be a flexible and powerful tool for image segmentation because of being shape-driven method with a properly defined speed function to grow or shrink the boundaries to segment complex objects of interest, precisely. In this study a combined level set algorithm consists of both region and boundary terms for GBM segmentation is proposed. The modified speed function incorporates threshold based level set and the Laplacian filter to highlight the fine details for performing an accurate extraction of the tumor region using multiple seed points selected by the user. An evaluation was performed on a dataset containing 6 patients with GBM by using three measures Dice, false positive error (FPE) and false negative error (FNE). Manual segmentation of GBM is considered as gold standard. Compared to traditional method, the mean of FPE and FNE are improved by 53.5% and 53.1%, respectively. The mean of Dice coefficients between our results and gold standard measurement reached to 0.88. As the results proved, the proposed combined method improves the accuracy of GBM segmentation by 16% compared to conventional level set method with threshold based speed function. Our method is also robust to change of parameters.

关键词： semi-automatic segmentation GBM Level set

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Improved Prediction of Short Exons via Multiscale Products 10

Improved Prediction of Short Exons via Multiscale Products

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10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Zhang, Guishan Zhang, Xiaolei Pan, Guocheng Yu, Yangjiang Chen, Yaowen Shantou Univ Key Lab Digital Signal & Image Proc Guangdong Shantou Guangdong Peoples R China Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou Guangdong Peoples R China Civil Aviat Flight Univ China Coll Air Traff Management Guanghan Sichuan Peoples R China Shantou Univ Med Coll Shantou Guangdong Peoples R China

ISBN: (纸本)9781538619377

Exon is an important functional region of eukaryotic DNA sequence. Prediction of exons can help to understand the structure and function of protein. However, the issue of finding an efficient technique to detect the numbers and locations of short coding sequences automatically is an unsolved problem. In this work, a short exon prediction method based on multiscale products in B-spline wavelet domain is proposed. The proposed wavelet denoising and multiscale products-based technique (WDMP) for short exons prediction have the following three features. (1) A wavelet package denoising method is applied to smooth the DNA numerical sequences. (2) A new B-spline wavelet function is designed to extract the exon features in multiscale domain, so the effect of window length is avoided. In addition, this wavelet has a higher degree of freedom for curve design. (3) We multiply the adjacent coefficients to exploit the high inter-scale correlation of the exon data, while these correlation features are used to separate the exon signals from background noise. Compared with four well-known model-independent methods, case studies demonstrate that the proposed WDMP method helps to improve the prediction accuracy of short exons significantly.

关键词： short exon detection wavelet package denoising B-spline wavelet transform multiscale products

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Analysis of Magnetoencephalography Based on Symbolic Transfer Entropy 10

Analysis of Magnetoencephalography Based on Symbolic Transfe...

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10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Zhang, Bihan Ding, Chuchu Yang, Wei Guo, Li Wang, Jun Hou, Fengzhen Nanjing Univ Posts & Telecommun Image Proc & Image Commun Key Lab Nanjing Jiangsu Peoples R China Nanjing Med Univ Affiliated Nanjing Brain Hosp Dept Psychiat Nanjing Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Smart Hlth Big Data Anal & Locat Serv Engn Lab Ji Nanjing Jiangsu Peoples R China China Pharmaceut Univ Sch Sci Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538619377

In this paper, we symbolize two kinds of different channels of Magnetoencephalography(MEG) and analyze their coupling relationship using symbolic transfer entropy algorithm. We record MEG signals from six depressive disorders and nine healthy subjects stimulated by positive, neutral, and negative emotional pictures and explore coupling relationship of different MEG channels. The results show that there are obvious differences on correlations between two channels of MLP32 and MRP32 with positive emotional stimulus, MLP31 and MRP31 with neutral emotional stimulus, MLP53 and MRP53 with negative emotional stimulus. In general, these channels have more correlation in patients with major depression, and can be able to distinguish depression patient from crowd. It also shows that the research of symbolic transfer entropy in MEG channel can distinguish the difference between normal and case samples, which of significance for clinical pathological estimation and diagnosis.

关键词： MEG depression symbolic transfer entropy

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Multiscale Mutual Mode Entropy In Theta Brain Wave Analysis For Epilepsy 10

Multiscale Mutual Mode Entropy In Theta Brain Wave Analysis ...

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10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Ji, Ning Wang, Jun Dai, Jiafei Li, Jin Hou, Fengzhen Nanjing Univ Posts & Telecommun Image Proc & Image Commun Key Lab Nanjing Jiangsu Peoples R China Nanjing Gen Hosp Nanjing Mil Command Nanjing Jiangsu Peoples R China Shaanxi Normal Univ Coll Phys & Informat Technol Xian Shaanxi Peoples R China China Pharmaceut Univ Sch Sci Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538619377

This article analyzes theta rhythms of EEG signals from the healthy and epileptics with the algorithm called Multiscale Mutual Mode Entropy. By calculation of Multiscale Mutual Mode Entropy of EEG signals from two channels, we can quantify the coupling degree between two sequences and obtain the coupling information of electroencephalogram. The original EEG signals are filtered and scaling processed. The calculation results show that entropy value of healthy people is higher than that of people with epilepsy on high scale, and the entropy value of original data is higher than that of their surrogate data. We also find evidence of the superiority of Multiscale Mutual Mode Entropy algorithm in terms of noise resistance. Therefore, we can easily find out the coupling information between the sequences of specific rhythm EEG by means of Multiscale Mutual Mode Entropy, which facilitates assessment of brain function and pathological detection.

关键词： multiscale epileptic coupling mutual mode entropy

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Space-Invariant Signature Algorithm processing of Ultrasound images for the Detection and Localization of Early Abnormalities in Animal Tissues 10

Space-Invariant Signature Algorithm Processing of Ultrasound...

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10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Parolia, Kushagra Gupta, Madan M. Babyn, Paul Zhang, Wenjun Bitner, Doug Univ Saskatchewan Coll Engn Div Biomed Engn Saskatoon SK S7N 5A9 Canada Univ Saskatchewan Coll Engn Dept Mech Engn Saskatoon SK S7N 5A9 Canada Saskatoon Hlth Reg Dept Med Imaging Saskatoon SK S7N 5A9 Canada Univ Saskatchewan Saskatoon SK S7N 5A9 Canada

ISBN: (纸本)9781538619377

In this paper we present an innovative space-variance approach named as "Space-Invariant Signature Algorithm (SISA)" for processing images from active systems, such as cancer cells, tumor growth, and dead cells, for the detection and localization of abnormalities at an early stage. In this paper, a SISA processing algorithm is developed, and this algorithm is tested on animal tissues such as pigs and chicken tissues. The abnormality in an active system can be defined as the obstacle or a failure which impedes the activities in tissues such as smooth flow of blood or electrical signals etc. Due to this impeding nature of the abnormality, some parameter perturbations are induced. In this paper using the SISA approach, these perturbations were detected in a preliminary experiment on animal tissues. The degree and position of the space-variance helps us in the detection and localization of abnormality even at an early (incipient) stage. The space-variance signature pattern is named as a SISA signature pattern'. In the absence of any abnormality, the signature pattern is space invariant, whereas, in the presences of any abnormality, the SISA signature pattern varies in the space (space-variant). The basic experimental studies on animal tissues using ultra sound imaging strongly suggest a possible use of the SISA approach as a non-invasive method for the detection and localization of abnormalities in biological tissues such as cancer cells non invasively.

关键词： Abnormality Ultrasound Animal tissue Signature pattern Space-Variance Space-Invariant SISA image processing

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CNNs Based Multi-Modality Classification for AD Diagnosis 10

CNNs Based Multi-Modality Classification for AD Diagnosis

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10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Cheng, Danni Liu, Manhua Shanghai Jiao Tong Univ Sch EIEE Dept Instrument Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781538619377

Accurate and early diagnosis of Alzheimer's disease (AD) plays a significant part for the patient care and development of future treatment. Magnetic Resonance image (MRI) and Positron Emission Tomography (PET) neuroimages are effective modalities that can help physicians to diagnose AD. In past few years, machine-learning algorithm have been widely studied on the analyses for multi-modality neuroimages in quantitation evaluation and computer-aided-diagnosis (CAD) of AD. Most existing methods extract the hand-craft features after image preprocessing such as registration, segmentation and feature extraction, and then train a classifier to distinguish AD from other groups. This paper proposes to construct multi-level convolutional neural networks (CNNs) to gradually learn and combine the multi-modality features for AD classification using MRI and PET images. First, the deep 3D-CNNs are constructed to transform the whole brain information into compact high-level features for each modality. Then, a 2D CNNs is cascaded to ensemble the high-level features for image classification. The proposed method can automatically learn the generic features from MRI and PET imaging data for AD classification. No rigid image registration and segmentation are performed on the brain images. Our proposed method is evaluated on the baseline MRI and PET images from Alzheimer's Disease Neuroimaging Initiative (ADNI) database on 193 subjects including 93 Alzheimer's disease (AD) subjects and 100 normal controls (NC) subjects. Experimental results and comparison show that the proposed method achieves an accuracy of 89.64% for classification of AD vs. NC, demonstrating the promising classification performance.

关键词： Multi-modality Classification Convolutional neural networks (CNN) Cascaded CNNs Alzheimer's disease diagnosis image classification

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Development of a Mobile Phone Based Tongue image Acquisition System 10

Development of a Mobile Phone Based Tongue Image Acquisition...

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10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Parcus, Robert Chow, Kin Long Zhu, Hai Long Yu, Qin Zhang, Shi Ping Hong Kong Baptist Univ Sch Chinese Med Hong Kong Peoples R China

ISBN: (纸本)9781538619377

Background. Tongue diagnosis is one of the main diagnostic methods in Chinese Medicine (CM). In addition to being used with other techniques to generate a CM diagnosis, methods derived from CM tongue diagnosis may have new applications, especially in early detection of certain diseases. Therefore, we believe that automating the collection and analysis of tongue images is of great benefit. While several methods for capturing images, extracting features and analyzing the data have been proposed over the years, most have limitations in reliability, accuracy, cost and usability. Nonetheless, recent advances in the field of image processing and machine learning (ML), together with improved hardware capabilities are rapidly enabling novel solutions to the problem. This paper describes a new mobile phone-based image acquisition method and reports our assessment of image quality from a clinical perspective. Methodology. A mobile phone-based system that uses a phone's flash and back camera to collect images with automatic white balance was developed. The usability and image quality were tested on forty (40) volunteers. Information pertaining to CM diagnosis of the tongue was collected through direct visual inspection, and by inspecting images produced by a commercially available tongue image diagnostic instrument, and by our newly developed system. The data collected amongst the three methods were compared using Cohen's kappa coefficient to evaluate the practical value of the images collected by the new system. Result. Our system is able to collect good quality tongue images and in general provides information comparable to those obtained through direct visual inspection or by the commercial instrument. Compared with the commercial instrument, our system has better inter-rater agreement with direct visual inspection in detecting the presence of tooth marks (kappa value: 0.633 vs 0.259), but inferior in detecting tongue coating greasiness (kappa value: 0.217 vs 0.739). Main c

关键词： Tongue image color analysis Feature extraction Coatings Cameras image segmentation Diseases

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