scholarly journals Multi-Trend Structure Descriptor at Micro-Level for Histological Image Retrieval

2019 ◽  
Vol 8 (3) ◽  
pp. 7539-7543 ◽  
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Image Representation ◽  
Image Data ◽  
Similarity Measurement ◽  
Challenging Problem ◽  
Histological Image ◽  
Illumination Changes ◽  
Micro Level ◽  
Structure Descriptor

Since hospitals are generating and using image data extensively, medical image databases and its size are rising rapidly. This led to difficulties in browsing and managing the huge databases. Therefore, the necessity for the development of efficient content-based medical image retrieval (CBMIR) system arises and is more challenging problem for researchers. In this paper, to alleviate the unbalanced distribution of image representation using multi-trend structure descriptor (MTSD), MTSD is computed at micro level i.e., image is divided into number of sub-images and for each sub-image MTSD is exploited. In similarity measurement, we compared the MTSDs of corresponding sub-images in query and target images than the liner ordered collection of smallest similarity values between the sub-images are considered for retrieval. Experiments revels that computation of proposed feature at micro level retains the localized representation and considering the liner ordered collection of smallest similarity values between the sub-images provides consistency under illumination changes and noise and thus proposed CBMIR achieves better results.

Similarity Searching of Medical Image Data in Distributed Systems

2012 ◽  
pp. 58-77
Author(s):  
Amalia Charisi ◽  
Panagiotis Korvesis ◽  
Vasileios Megalooikonomou
Keyword(s):  
Distributed Systems ◽  
Image Retrieval ◽  
Medical Image ◽  
Image Data ◽  
Medical Data ◽  
Similarity Searching ◽  
Network Bandwidth ◽  
Local Site ◽  
Distributed Index ◽  
Medical Image Data

In this paper, the authors propose a method for medical image retrieval in distributed systems to facilitate telemedicine. The proposed framework can be used by a network of healthcare centers, where some can be remotely located, assisting in diagnosis without the necessary transfer of patients. Security and confidentiality issues of medical data are expected, which are handled at the local site following the procedures and protocols of each institution. To make the search more effective, the authors introduce a distributed index based on features that are extracted from each image. Considering network bandwidth limitations and other restrictions that are associated with handling medical data, the images are processed locally and a pointer is distributed in the network. For the distribution of this pointer, the authors propose a function that maps the pointer of each image to a node with similar contents.

A Perceptual Approach for Image Representation and Retrieval

2011 ◽  
pp. 121-141
Author(s):  
Noureddine Abbadeni
Keyword(s):  
Image Retrieval ◽  
Image Representation ◽  
Rank Correlation ◽  
Human Perception ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Data Set ◽  
Psychometric Method ◽  
Results Merging ◽  
Spearman Rank Correlation Coefficient

This chapter describes an approach based on human perception to content-based image representation and retrieval. We consider textured images and propose to model the textural content of images by a set of features having a perceptual meaning and their application to content-based image retrieval. We present a new method to estimate a set of perceptual textural features, namely coarseness, directionality, contrast and busyness. The proposed computational measures are based on two representations: the original images representation and the autocovariance function (associated with images) representation. The correspondence of the proposed computational measures to human judgments is shown using a psychometric method based on the Spearman rank-correlation coefficient. The set of computational measures is applied to content-based image retrieval on a large image data set, the well-known Brodatz database. Experimental results show a strong correlation between the proposed computational textural measures and human perceptual judgments. The benchmarking of retrieval performance, done using the recall measure, shows interesting results. Furthermore, results merging/fusion returned by each of the two representations is shown to allow significant improvement in retrieval effectiveness.

Study on the Data Mining of Image Storage on the Hadoop Cloud Platform

2014 ◽  
Vol 543-547 ◽  
pp. 3667-3670 ◽  
Author(s):  
Jian Xin Zhu
Keyword(s):  
Data Mining ◽  
Satellite Remote Sensing ◽  
Medical Image ◽  
Rapid Development ◽  
Image Data ◽  
Challenging Problem ◽  
Cloud Platform ◽  
Huge Number ◽  
Image Storage ◽  
Usable Information

Along with the rapid development of image acquisition and image storage, a huge number of usable image data are obtained by people, such as satellite remote sensing image data, medical image data, etc. Data mining of images is to analyze these useful images and extract the usable information from them. How to effectively store rapidly make data mining for the increasing images has become the most challenging problem faced by people. This paper focuses on data mining of the massive images with the help of the Hadoop cloud platform.

scholarly journals Heterogeneous Medical Image Retrieval using Multi-Trend Structure Descriptor and Fuzzy SVM Classifier

2019 ◽  
Vol 8 (3) ◽  
pp. 3958-3963 ◽  
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Local Level ◽  
Level Structure ◽  
Research Work ◽  
Support Vector ◽  
Svm Classifier ◽  
Fuzzy Support Vector Machine ◽  
Medical Image Retrieval ◽  
Structure Descriptor

This research work contributes a system for heterogeneeous medical image retrieval usiing Multi-trend structure descriptor (MTSD) and fuzzy support vector machine (FSVM) classifier. The MTSD encodes the local level structure in the form of trends for color, shape and texture information of medical images. Experimental results demonstrate thatt the fusion of MTSD and FSVM significantly increases the retrieval precision for heterogeneeous medical image dataset. The simplest Manhattan diistance is incorporated for measuring the similarity. The feasibility of thee proposed system is extensively experimented on benchmark daataset and the experimental study clearly demonstrated that proposed fusion of MTSD with Fuzzy SVM gives significantly superior average retrieval precision.

Application of Content-Based Image Retrieval in Medical Image Acquisition

2020 ◽  
pp. 220-240
Author(s):  
Vinayak Majhi ◽  
Sudip Paul
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Medical Diagnosis ◽  
Medical Image ◽  
Image Acquisition ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Extraction Algorithm ◽  
Day By Day ◽  
Acquisition Technique

Content-based image retrieval is a promising technique to access visual data. With the huge development of computer storage, networking, and the transmission technology now it becomes possible to retrieve the image data beside the text. In the traditional way, we find the content of image by the tagged image with some indexed text. With the development of machine learning technique in the domain of artificial intelligence, the feature extraction techniques become easier for CBIR. The medical images are continuously increasing day by day where each image holds some specific and unique information about some specific disease. The objectives of using CBIR in medical diagnosis are to provide correct and effective information to the specialist for the quality and efficient diagnosis of the disease. Medical image content requires different types of CBIR technique for different medical image acquisition techniques such as MRI, CT, PET Scan, USG, MRS, etc. So, in this concern, each CBIR technique has its unique feature extraction algorithm for each acquisition technique.

scholarly journals Medical Image Retrieval: A Multimodal Approach

2014 ◽  
Vol 13s3 ◽  
pp. CIN.S14053 ◽  
Author(s):  
Yu Cao ◽  
Shawn Steffey ◽  
Jianbiao He ◽  
Degui Xiao ◽  
Cui Tao ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Image Retrieval ◽  
Medical Image ◽  
Semantic Analysis ◽  
Medical Images ◽  
Image Data ◽  
Probabilistic Latent Semantic Analysis ◽  
Medical Image Retrieval ◽  
Digital Format ◽  
Deep Boltzmann Machine

Medical imaging is becoming a vital component of war on cancer. Tremendous amounts of medical image data are captured and recorded in a digital format during cancer care and cancer research. Facing such an unprecedented volume of image data with heterogeneous image modalities, it is necessary to develop effective and efficient content-based medical image retrieval systems for cancer clinical practice and research. While substantial progress has been made in different areas of content-based image retrieval (CBIR) research, direct applications of existing CBIR techniques to the medical images produced unsatisfactory results, because of the unique characteristics of medical images. In this paper, we develop a new multimodal medical image retrieval approach based on the recent advances in the statistical graphic model and deep learning. Specifically, we first investigate a new extended probabilistic Latent Semantic Analysis model to integrate the visual and textual information from medical images to bridge the semantic gap. We then develop a new deep Boltzmann machine-based multimodal learning model to learn the joint density model from multimodal information in order to derive the missing modality. Experimental results with large volume of real-world medical images have shown that our new approach is a promising solution for the next-generation medical imaging indexing and retrieval system.

scholarly journals Optimal Features Selection for X-Ray Image Retrieval with Regular and Irregular Zoning Methods

2018 ◽  
Vol 7 (4.5) ◽  
pp. 87
Author(s):  
P. Nalini ◽  
Dr B. L. Malleswari
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Image Annotation ◽  
Image Data ◽  
Image Features ◽  
Data Sets ◽  
Features Selection ◽  
X Ray ◽  
Eigen Values ◽  
Textural Attributes

Medical Image Retrieval is mainly meant for enhancing the healthcare system by coordinating physicians and interact with computing machines. This helps the doctors and radiologists in understanding the case and leads to automatic medical image annotation process. The choice of image attributes have crucial role in retrieving similar looking images of various anatomic regions.  In this paper we presented an empirical analysis of an X-Ray image retrieval system with intensity, statistical features, DFT and DWT transformed coefficients and Eigen values using Singular Valued Decomposition techniques as parameters. We computed these features by dividing the images in five different regular and irregular zones. In our previous work we proved that analyzing the image with local attributes result in better retrieval efficiency and hence in this paper we computed the attributes by dividing the image into 64 regular and irregular zones. This experimentation carried out on IRMA 2008 and IRMA 2009 X-Ray image data sets. In this work we come up with some conclusions like wavelet based textural attributes, intensity features and Eigen values extracted from different regular zones worked well in retrieving the images over the features computed over irregular zones. We also determined like the set of image features in which form of zoning for different anatomical regions  result in excellent retrieval of  similar looking X-Ray images.

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