Using Global Shape Descriptors for Content Medical-Based Image Retrieval

2013 ◽  
pp. 492-502
Author(s):  
Saïd Mahmoudi ◽  
Mohammed Benjelloun
Keyword(s):  
Fourier Transform ◽  
Image Retrieval ◽  
Real Time ◽  
Search Engine ◽  
Shape Descriptors ◽  
Query Image ◽  
Global Shape ◽  
Aided Diagnosis ◽  
Preliminary Classification

In this chapter, the authors propose a new method belonging to content medical-based image retrieval approaches and that uses a set of region-based shape descriptors. The search engine discussed in this work allows the classification of newly acquired medical images into some well known categories and also to get the images that are more similar to a query image. The final goal is to help the medical staff to annotate these images. To achieve this task, the authors propose a set of three descriptors that are based on: (1) Hu, (2) Zernike moments, and (3) Fourier transform-based signature, which are considered as region descriptors. The advantage of using this kind of global descriptor is that they are very fast, real time, and they do not need any segmentation step. The authors propose also a comparative study between these three approaches. The search engines are tested by using a database composed of 75 images that have different sizes, and that are classified into five classes. The results provided by the proposed retrieval approaches are given with high precision. The comparison between the three approaches is achieved using classification matrices and the recall/precision curves. The three proposed retrieval approaches produce accurate results in real time. This proves the advantage of using global shape features as a preliminary classification step in an automated aided diagnosis system.

Intelligent Content-Based Dermoscopic Image Retrieval with Relevance Feedback for Computer-Aided Melanoma Diagnosis

2017 ◽  
Vol 10 (1) ◽  
pp. 85-108 ◽  
Author(s):  
Khadidja Belattar ◽  
Sihem Mostefai ◽  
Amer Draa
Keyword(s):  
Skin Lesion ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Query Image ◽  
Melanoma Diagnosis ◽  
Computer Aided ◽  
Histogram Intersection ◽  
Dermoscopic Image ◽  
Image Query ◽  
Aided Diagnosis

The use of Computer-Aided Diagnosis in dermatology raises the necessity of integrating Content-Based Image Retrieval (CBIR) technologies. The latter could be helpful to untrained users as a decision support system for skin lesion diagnosis. However, classical CBIR systems perform poorly due to semantic gap. To alleviate this problem, we propose in this paper an intelligent Content-Based Dermoscopic Image Retrieval (CBDIR) system with Relevance Feedback (RF) for melanoma diagnosis that exhibits: efficient and accurate image retrieval as well as visual features extraction that is independent of any specific diagnostic method. After submitting a query image, the proposed system uses linear kernel-based active SVM, combined with histogram intersection-based similarity measure to retrieve the K most similar skin lesion images. The dominant (melanoma, benign) class in this set will be identified as the image query diagnosis. Extensive experiments conducted on our system using a 1097 image database show that the proposed scheme is more effective than CBDIR without the assistance of RF.

scholarly journals A New Content Based Search Mechanism for Image Retrieval Search Engine

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Image Retrieval ◽  
Search Engine ◽  
Data Storage ◽  
Search Engines ◽  
Input Image ◽  
Medical Field ◽  
Query Image ◽  
Search Mechanism ◽  
User Expectations ◽  
Diagnostics System

In the growing world of technology, where everything is available in just one click, the user expectations has increased with time. In the era of Search Engines, where Google, Yahoo are providing the facility to search through text and voice and image , it has become a complex work to handle all the operations and lot more of data storage is needed. It is also a time consuming process. In the proposed Image retrieval Search Engine, the user enters the queried image and that image is being matched with the template images . The proposed approach takes the input image with 15% accuracy to 100% accuracy to retrieve the intended image by the user. But it is found that due to the efficiency of the applied algorithm, in all cases, the retrieved images are with the same accuracy irrespective of the input query image accuracy. This implementation is very much useful in the fields of forensic, defense and diagnostics system in medical field etc. .

Improving Image Retrieval by Clustering

2011 ◽  
pp. 20-43 ◽  
Author(s):  
Dany Gebara ◽  
Reda Alhajj
Keyword(s):  
Image Retrieval ◽  
Feature Vector ◽  
Search Space ◽  
Query Image ◽  
Novel Approach ◽  
Density Based Clustering ◽  
Clustering Approach ◽  
Classification Of Images ◽  
Single Cluster

This chapter presents a novel approach for content-fbased image retrieval and demonstrates its applicability on non-texture images. The process starts by extracting a feature vector for each image; wavelets are employed in the process. Then the images (each represented by its feature vector) are classified into groups by employing a density-based clustering approach, namely OPTICS. This highly improves the querying facility by limiting the search space to a single cluster instead of the whole database. The cluster to be searched is determined by applying on the query image the same clustering process OPTICS. This leads to the closest cluster to the query image, and hence, limits the search to the latter cluster without adding the query image to the cluster, except if such request is explicitly specified. The power of this system is demonstrated on non-texture images from the Corel dataset. The achieved results demonstrate that the classification of images is extremely fast and accurate.

An Efficient Image Retrieval Based on Fusion of Fast Features and Query Image Classification

2017 ◽  
Vol 4 (1) ◽  
pp. 19-37 ◽  
Author(s):  
Vibhav Prakash Singh ◽  
Subodh Srivastava ◽  
Rajeev Srivastava
Keyword(s):  
Image Retrieval ◽  
Image Classification ◽  
Texture Features ◽  
Research Area ◽  
Vital Role ◽  
Query Image ◽  
Similar Images ◽  
Multiclass Classifier

Content Based Image Retrieval (CBIR) is an emerging research area in computer vision, in which, we yield similar images as per the query content. For the implementation of CBIR system, feature extraction plays a vital role, where colour feature is quite remarkable. But, due to unevenly colored or achromatic surfaces, the role of texture is also important. In this paper, an efficient and fast CBIR system is proposed, which is based on a fusion of computationally light weighted colour and texture features; chromaticity moment, colour percentile, and local binary pattern (LBP). Using these features with multiclass classifier, the authors propose a supervised query image classification and retrieval model, which filters all irrelevant class images. Basically, this model categorizes and recovers the class of a query image based on its visual content, and this successful classification of image significantly enhances the performance and searching time of retrieval system. Descriptive experimental analysis on benchmark databases confirms the effectiveness of proposed retrieval framework.

Classification of Liver Disease from CT Images Using a Support Vector Machine

2007 ◽  
Vol 11 (4) ◽  
pp. 396-402 ◽  
Author(s):  
Chien-Cheng Lee ◽  
◽  
Sz-Han Chen ◽  
Yu-Chun Chiang ◽  
Keyword(s):  
Support Vector Machine ◽  
Liver Disease ◽  
Operating Characteristic ◽  
State Of The Art ◽  
Support Vector ◽  
Shape Descriptors ◽  
Learning Problem ◽  
Occurrence Matrix ◽  
Aided Diagnosis

We propose a classifier based on the support vector machine (SVM) for automatic classification in liver disease. The SVM, stemming from statistical learning theory, involves state-of-the-art machine learning. The classifier is a part of computer-aided diagnosis (CADx), which assists radiologists in accurately diagnosing liver disease. We formulate discriminating between cysts, hepatoma, cavernous hemangioma, and normal tissue as a supervised learning problem, and apply SVM to classifying the diseases using gray level and co-occurrence matrix features and region-based shape descriptors, calculated from regions of interest (ROIs), as input. Significant features of ROI enable us to simplify SVM input space and to feed the SVM representative information. By simplifying and clarifying the diagnosis process, we separate the classification of liver disease into hierarchical multiclass classification. We use the receiver operating characteristic (ROC) curve to evaluate diagnosis performance, demonstrating the classifier’s good performance.

Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Saliha Zahoor ◽  
Ikram Ullah Lali ◽  
Muhammad Attique Khan ◽  
Kashif Javed ◽  
Waqar Mehmood
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Diagnose Breast Cancer ◽  
Initial Stage ◽  
Computer Aided ◽  
Diagnosis And Classification ◽  
And Performance ◽  
Tools And Techniques ◽  
Aided Diagnosis

: Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

scholarly journals A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 234 ◽  
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Normal Control ◽  
Input Data ◽  
Deep Neural Network ◽  
Pattern Mining ◽  
F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

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